CN118568645A

CN118568645A - Charging seat abnormality warning method, device, equipment and medium

Info

Publication number: CN118568645A
Application number: CN202411035981.3A
Authority: CN
Inventors: 蔡伟
Original assignee: Xiaomi Automobile Technology Co Ltd
Current assignee: Xiaomi Automobile Technology Co Ltd
Priority date: 2024-07-31
Filing date: 2024-07-31
Publication date: 2024-08-30

Abstract

The disclosure provides a charging seat abnormality warning method, a charging seat abnormality warning device, charging seat abnormality warning equipment and a charging seat abnormality warning medium, the method comprises the following steps: acquiring a first candidate abnormal vehicle set with abnormal charging in an initial vehicle set; filtering the abnormal charging piles from the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set; obtaining fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; and in response to identifying the target charging seat abnormal vehicle with the charging seat abnormality in the second candidate abnormal vehicle set according to the fitting parameters, carrying out abnormality warning on the target charging seat abnormal vehicle. The method reduces the manual dependency degree, the labor cost and the operation cost of abnormal vehicle alarming, determines the abnormal vehicle of the target charging seat through fitting parameters, improves the timeliness and the accuracy of vehicle charging abnormal alarming, further improves the safety of vehicle driving, and optimizes the driving experience of users.

Description

Charging seat abnormality warning method, device, equipment and medium

Technical Field

The disclosure relates to the field of data processing, and in particular relates to a charging seat abnormality warning method, device, equipment and medium.

Background

With the development of technology, more and more people select electric vehicles as a walking tool, and charging abnormality possibly occurs in the process of charging the electric vehicles, so that potential safety hazards of the electric vehicles are caused.

In the related art, the temperature of the charging equipment in the electric vehicle can be monitored, when the temperature of the charging equipment is monitored to be abnormal, the abnormal charging alarm of the electric vehicle is carried out, the timeliness of abnormal charging of the vehicle is poor, and the user experience is influenced to a certain extent.

Disclosure of Invention

The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.

For this reason, the first aspect of the present disclosure proposes a charging stand abnormality warning method.

The second aspect of the present disclosure provides a charging stand abnormality warning device.

A third aspect of the present disclosure proposes a vehicle.

A fourth aspect of the present disclosure proposes an electronic device.

A fifth aspect of the present disclosure proposes a computer-readable storage medium.

The first aspect of the present disclosure provides a charging stand abnormality alarm method, including: acquiring a first candidate abnormal vehicle set with abnormal charging in an initial vehicle set; performing abnormal charging pile filtering on the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set; obtaining fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; and in response to identifying a target charging seat abnormal vehicle with charging seat abnormality in the second candidate abnormal vehicle set according to the fitting parameters, carrying out abnormality warning on the target charging seat abnormal vehicle.

In addition, the charging seat abnormality warning method provided in the first aspect of the present disclosure may further have the following additional technical features:

According to one embodiment of the present disclosure, the acquiring a first set of abnormal vehicle candidates with abnormal charging in an initial set of vehicles includes: acquiring a first target analysis data set of the initial vehicle set, and acquiring a fragment temperature rise rate set in each first target analysis data set; and acquiring the first candidate abnormal vehicle set with abnormal charging from the initial vehicle set according to the segment temperature rise rate set of each first target analysis data set.

According to one embodiment of the present disclosure, the acquiring the first set of target analysis data sets of the initial set of vehicles includes: acquiring first charging data sets of all initial vehicles, and integrating all the first charging data sets to obtain a total second charging data set; acquiring a first grouping item set of the two charging data sets, and grouping items of the second charging data set according to the first grouping item set to obtain a third charging data set combination under each first grouping item; combining according to the third charging data sets under each first grouping item to obtain a first candidate analysis data set of the initial vehicle set; a first number of segments for each first candidate analysis dataset is obtained, and the first set of target analysis datasets is obtained from the first set of candidate analysis datasets according to the first number of segments.

According to one embodiment of the disclosure, the combining the first candidate analysis data set according to the third charging data set under each first grouping item to obtain the first candidate analysis data set of the initial vehicle set includes: acquiring a fourth charging data set under each first grouping item from each third charging data set combination, wherein the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item for any first grouping item; and obtaining a first candidate analysis data set obtained by combining the fourth charging data sets under each first grouping item, so as to obtain the first candidate analysis data set.

According to one embodiment of the disclosure, the obtaining the first number of segments of each first candidate analysis data set, and obtaining the first target analysis data set from the first candidate analysis data set according to the first number of segments, includes: for any first candidate analysis data set, acquiring a first data fragment set in the first candidate analysis data set, and obtaining the first fragment number according to the first data fragment set, wherein for any first data fragment, each charging data in the first data fragment is generated based on the same charging behavior; determining the first candidate analysis data set as a first target analysis data set in response to the first number of segments being greater than or equal to a preset first number of segments threshold; and obtaining the first target analysis data set of the initial vehicle set according to each first target analysis data set.

According to one embodiment of the disclosure, the acquiring the set of fragment temperature rise rates in each first target analysis data set includes: acquiring a second data fragment set in each first target analysis data set; and aiming at any first target analysis data set, acquiring the fragment temperature rise rate of each second data fragment in the second data fragment set of the first target analysis data set so as to acquire the fragment temperature rise rate set of the first target analysis data set.

According to one embodiment of the disclosure, the acquiring the first candidate abnormal vehicle set with abnormal charging from the initial vehicle set according to the segment temperature rise rate set of each first target analysis data set includes: for any first target analysis data set, acquiring a first quarter bit value and a second quarter bit value of the first target analysis data set to obtain a quarter bit distance parameter of the first target analysis data set; acquiring an outlier temperature rise rate reference value according to the second quarter bit value and the quarter bit distance parameter; acquiring an outlier segment temperature rise rate set in each segment temperature rise rate set according to the outlier temperature rise rate reference value, wherein any outlier segment temperature rise rate in the outlier segment temperature rise rate set is smaller than or equal to the outlier temperature rise rate reference value; and acquiring respective vehicle identifications of the outlier segment temperature rise rate sets, and acquiring first candidate abnormal vehicles of each vehicle identification in the initial vehicle set to obtain the first candidate abnormal vehicle set.

According to an embodiment of the present disclosure, the filtering the abnormal charging pile for the first abnormal candidate vehicle set to obtain a filtered second abnormal candidate vehicle set includes: obtaining charging pile identifiers of all first candidate abnormal vehicles in the first candidate abnormal vehicle set so as to obtain the number of the identifiers of all the charging pile identifiers; acquiring a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set according to the identification number of each charging pile identification; and carrying out abnormal charging pile filtering on the first candidate abnormal vehicle set according to the third candidate abnormal vehicle set to obtain the rest abnormal vehicle sets except the third candidate abnormal vehicle set in the first candidate abnormal vehicle set as the second candidate abnormal vehicle set.

According to an embodiment of the present disclosure, the obtaining, according to the number of identifiers of each charging pile identifier, a third abnormal vehicle candidate set with abnormal charging piles in the first abnormal vehicle candidate set includes: and for any charging pile identifier, determining the corresponding vehicle of the charging pile identifier in the first candidate abnormal vehicle set as a third candidate abnormal vehicle with the charging pile abnormality in response to the number of identifiers of the charging pile identifiers being greater than or equal to a preset identifier number threshold.

According to one embodiment of the disclosure, the obtaining the fitting parameters of each second abnormal vehicle candidate in the second abnormal vehicle candidate set includes: acquiring a second target analysis data set of the second candidate abnormal vehicle set; and obtaining the fitting slope and the fitting degree of each second target analysis data set to obtain the fitting parameters of each second candidate abnormal vehicle.

According to one embodiment of the disclosure, the acquiring the second set of target analysis data sets of the second set of candidate abnormal vehicles includes: acquiring a fifth charging data set of each second candidate abnormal vehicle; acquiring a second grouping item set of the fifth charging data set, and respectively grouping items of each fifth charging data set according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item; obtaining a second candidate analysis data set of each second candidate abnormal vehicle according to the sixth charging data set combination under each second grouping item; and acquiring a third data fragment set of each second candidate analysis data set, and acquiring the second target analysis data set from the second candidate analysis data set according to the third data fragment set.

According to one embodiment of the disclosure, the obtaining a third data segment set of each second candidate analysis data set, and obtaining the second target analysis data set from the second candidate analysis data set according to the third data segment set includes: for any second candidate analysis data set, acquiring a fourth data fragment set which does not belong to a preset abnormal charging pile list from a third data fragment set of the second candidate analysis data set; obtaining a third candidate analysis data set according to the fourth data fragment set in each second candidate analysis data set; and for any third candidate analysis data set, determining the third candidate analysis data set as a second target analysis data set in response to the number of second fragments in a fourth data fragment set in the third candidate analysis data set being greater than or equal to a preset second fragment number threshold, so as to obtain the second target analysis data set.

According to one embodiment of the disclosure, the responding to the identification of the target abnormal charging seat vehicle with abnormal charging seat in the second candidate abnormal vehicle set according to the fitting parameters, carries out abnormal warning on the target abnormal charging seat vehicle, and comprises the following steps: obtaining a fitting slope and a fitting degree in fitting parameters of each second target analysis data set; for any second target analysis data set, determining that the second target analysis data set is a target charging seat abnormality data set with abnormal charging seat in response to the fitting slope of the second target analysis data set being greater than or equal to a preset fitting slope threshold and the fitting degree of the second target analysis data set being greater than or equal to a preset fitting degree threshold; and acquiring a corresponding vehicle of the target charging seat abnormal data set in the second candidate abnormal vehicle set as the target charging seat abnormal vehicle with abnormal charging seat, and carrying out abnormal warning on the target charging seat abnormal vehicle.

A second aspect of the present disclosure proposes a charging stand abnormality warning apparatus, the apparatus comprising: the first acquisition module is used for acquiring a first candidate abnormal vehicle set with abnormal charging in the initial vehicle set; the filtering module is used for filtering the abnormal charging piles of the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set; the second acquisition module is used for acquiring fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; and the alarming module is used for alarming the abnormal target charging seat in response to the fact that the abnormal target charging seat in the second candidate abnormal vehicle set is identified according to the fitting parameters.

In addition, the charging seat abnormality warning device provided in the second aspect of the present disclosure may further have the following additional technical features:

According to an embodiment of the present disclosure, the first obtaining module is further configured to: acquiring a first target analysis data set of the initial vehicle set, and acquiring a fragment temperature rise rate set in each first target analysis data set; and acquiring the first candidate abnormal vehicle set with abnormal charging from the initial vehicle set according to the segment temperature rise rate set of each first target analysis data set.

According to an embodiment of the present disclosure, the first obtaining module is further configured to: acquiring first charging data sets of all initial vehicles, and integrating all the first charging data sets to obtain a total second charging data set; acquiring a first grouping item set of the two charging data sets, and grouping items of the second charging data set according to the first grouping item set to obtain a third charging data set combination under each first grouping item; combining according to the third charging data sets under each first grouping item to obtain a first candidate analysis data set of the initial vehicle set; a first number of segments for each first candidate analysis dataset is obtained, and the first set of target analysis datasets is obtained from the first set of candidate analysis datasets according to the first number of segments.

According to an embodiment of the present disclosure, the first obtaining module is further configured to: acquiring a fourth charging data set under each first grouping item from each third charging data set combination, wherein the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item for any first grouping item; and obtaining a first candidate analysis data set obtained by combining the fourth charging data sets under each first grouping item, so as to obtain the first candidate analysis data set.

According to an embodiment of the present disclosure, the first obtaining module is further configured to: for any first candidate analysis data set, acquiring a first data fragment set in the first candidate analysis data set, and obtaining the first fragment number according to the first data fragment set, wherein for any first data fragment, each charging data in the first data fragment is generated based on the same charging behavior; determining the first candidate analysis data set as a first target analysis data set in response to the first number of segments being greater than or equal to a preset first number of segments threshold; and obtaining the first target analysis data set of the initial vehicle set according to each first target analysis data set.

According to an embodiment of the present disclosure, the first obtaining module is further configured to: acquiring a second data fragment set in each first target analysis data set; and aiming at any first target analysis data set, acquiring the fragment temperature rise rate of each second data fragment in the second data fragment set of the first target analysis data set so as to acquire the fragment temperature rise rate set of the first target analysis data set.

According to an embodiment of the present disclosure, the first obtaining module is further configured to: for any first target analysis data set, acquiring a first quarter bit value and a second quarter bit value of the first target analysis data set to obtain a quarter bit distance parameter of the first target analysis data set; acquiring an outlier temperature rise rate reference value according to the second quarter bit value and the quarter bit distance parameter; acquiring an outlier segment temperature rise rate set in each segment temperature rise rate set according to the outlier temperature rise rate reference value, wherein any outlier segment temperature rise rate in the outlier segment temperature rise rate set is smaller than or equal to the outlier temperature rise rate reference value; and acquiring respective vehicle identifications of the outlier segment temperature rise rate sets, and acquiring first candidate abnormal vehicles of each vehicle identification in the initial vehicle set to obtain the first candidate abnormal vehicle set.

According to one embodiment of the disclosure, the filter module is further configured to: obtaining charging pile identifiers of all first candidate abnormal vehicles in the first candidate abnormal vehicle set so as to obtain the number of the identifiers of all the charging pile identifiers; acquiring a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set according to the identification number of each charging pile identification; and carrying out abnormal charging pile filtering on the first candidate abnormal vehicle set according to the third candidate abnormal vehicle set to obtain the rest abnormal vehicle sets except the third candidate abnormal vehicle set in the first candidate abnormal vehicle set as the second candidate abnormal vehicle set.

According to one embodiment of the disclosure, the filter module is further configured to: and for any charging pile identifier, determining the corresponding vehicle of the charging pile identifier in the first candidate abnormal vehicle set as a third candidate abnormal vehicle with the charging pile abnormality in response to the number of identifiers of the charging pile identifiers being greater than or equal to a preset identifier number threshold.

According to an embodiment of the present disclosure, the second obtaining module is further configured to: acquiring a second target analysis data set of the second candidate abnormal vehicle set; and obtaining the fitting slope and the fitting degree of each second target analysis data set to obtain the fitting parameters of each second candidate abnormal vehicle.

According to an embodiment of the present disclosure, the second obtaining module is further configured to: acquiring a fifth charging data set of each second candidate abnormal vehicle; acquiring a second grouping item set of the fifth charging data set, and respectively grouping items of each fifth charging data set according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item; obtaining a second candidate analysis data set of each second candidate abnormal vehicle according to the sixth charging data set combination under each second grouping item; and acquiring a third data fragment set of each second candidate analysis data set, and acquiring the second target analysis data set from the second candidate analysis data set according to the third data fragment set.

According to an embodiment of the present disclosure, the second obtaining module is further configured to: for any second candidate analysis data set, acquiring a fourth data fragment set which does not belong to a preset abnormal charging pile list from a third data fragment set of the second candidate analysis data set; obtaining a third candidate analysis data set according to the fourth data fragment set in each second candidate analysis data set; and for any third candidate analysis data set, determining the third candidate analysis data set as a second target analysis data set in response to the number of second fragments in a fourth data fragment set in the third candidate analysis data set being greater than or equal to a preset second fragment number threshold, so as to obtain the second target analysis data set.

According to one embodiment of the disclosure, the alarm module is further configured to: obtaining a fitting slope and a fitting degree in fitting parameters of each second target analysis data set; for any second target analysis data set, determining that the second target analysis data set is a target charging seat abnormality data set with abnormal charging seat in response to the fitting slope of the second target analysis data set being greater than or equal to a preset fitting slope threshold and the fitting degree of the second target analysis data set being greater than or equal to a preset fitting degree threshold; and acquiring a corresponding vehicle of the target charging seat abnormal data set in the second candidate abnormal vehicle set as the target charging seat abnormal vehicle with abnormal charging seat, and carrying out abnormal warning on the target charging seat abnormal vehicle.

A third aspect of the present disclosure proposes a vehicle for implementing the charging seat abnormality warning method proposed in the first aspect described above.

A fourth aspect of the present disclosure proposes an electronic device comprising: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute instructions to implement the charging stand abnormality warning method as set forth in the first aspect above.

A fifth aspect of the present disclosure proposes a computer-readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the charging stand abnormality warning method as proposed in the first aspect above.

The charging seat abnormality warning method, the charging seat abnormality warning device, the charging seat abnormality warning equipment and the charging seat abnormality warning medium provided by the present disclosure acquire a first candidate abnormal vehicle set of a charging abnormality in an initial vehicle set, perform abnormal charging pile filtering on the first candidate abnormal vehicle set to obtain a second candidate abnormal vehicle set, further acquire fitting parameters of each second candidate abnormal vehicle, identify whether a target charging seat abnormal vehicle exists in the second candidate abnormal vehicle set according to the fitting parameters, and perform abnormality warning on the target charging seat abnormal vehicle when the target charging seat abnormal vehicle exists is identified. In the method, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set filtered by the abnormal charging pile, the operator is not required to be relied on for confirmation, the labor dependence degree, the labor cost and the operation cost of abnormal vehicle alarming are reduced, the target charging seat abnormal vehicle is determined through fitting parameters, compared with the prior art that abnormal alarming is carried out after temperature abnormality occurs in the charging equipment through temperature monitoring, the timeliness and the accuracy of vehicle charging abnormal alarming are improved, the safety of vehicle driving is further improved, and the driving experience of a user is optimized.

It should be understood that the description herein is not intended to identify key or critical features of the embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

Fig. 1 is a flowchart illustrating a charging stand abnormality alarm method according to an embodiment of the disclosure;

FIG. 2 is a flowchart illustrating a charging stand abnormality warning method according to another embodiment of the disclosure;

FIG. 3 is a schematic diagram of charge data acquisition according to an embodiment of the present disclosure;

FIG. 4 is a flowchart illustrating a charging stand abnormality warning method according to another embodiment of the disclosure;

FIG. 5 is a schematic diagram illustrating a structure of a charging stand abnormality warning device according to an embodiment of the disclosure;

fig. 6 is a block diagram of an electronic device of an embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.

The following describes a charging seat abnormality warning method, device, equipment and medium according to an embodiment of the present disclosure with reference to the accompanying drawings.

Fig. 1 is a flow chart of a charging seat abnormality alarm method according to an embodiment of the disclosure, as shown in fig. 1, the method includes:

s101, acquiring a first candidate abnormal vehicle set with abnormal charging in the initial vehicle set.

In the embodiment of the disclosure, when a vehicle is charged, a charging seat on the vehicle is required to be connected with a charging pile, and in the scene, the charging of the vehicle can be abnormal due to the fact that any one of the charging seat and the charging pile is abnormal, so that potential safety hazards can be caused in the charging process of the vehicle.

In this scenario, whether the charging seat configured on the vehicle is abnormal or not may be identified by historical charging data of the vehicle, wherein the vehicle requiring the charging abnormality identification may be marked as an initial vehicle, thereby obtaining an initial vehicle set composed of a plurality of initial vehicles.

Note that, for any initial vehicle set, each initial vehicle in the initial vehicle set may be a vehicle having the same vehicle model, or may be a vehicle having a charging stand of the same model, and the present invention is not limited thereto.

In the embodiment of the disclosure, historical charging data of each initial vehicle in the initial vehicle set can be acquired, abnormal data analysis processing is performed on the acquired historical charging data according to a data analysis method in related technology, and further, partial vehicles with abnormal charging are identified from the initial vehicle set according to analysis results, and marked as first candidate abnormal vehicles, so that a first candidate abnormal vehicle set formed by at least one first candidate abnormal vehicle is obtained.

S102, carrying out abnormal charging pile filtering on the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set.

In the embodiment of the disclosure, the first abnormal vehicle candidate set includes vehicles having charging abnormality in the initial vehicle set, that is, the first abnormal vehicle candidate set may include vehicles having charging abnormality due to the abnormality of the charging pile.

In order to realize the abnormality alarm of the charging seat of the vehicle, the first candidate abnormal vehicle set can be screened to obtain partial vehicles with abnormal charging caused by the abnormal charging pile in the first candidate abnormal vehicle set, wherein the partial vehicles are the abnormal charging pile vehicles in the first candidate abnormal vehicle set.

Optionally, the part of vehicles can be filtered and deleted from the first candidate abnormal vehicle set to realize abnormal charging pile filtering of the first candidate abnormal vehicle set, and the residual vehicles after filtering and deleting are marked as at least one second candidate abnormal vehicle in the first candidate abnormal vehicle set, so that a second candidate abnormal vehicle set composed of the at least one second candidate abnormal vehicle is obtained.

S103, obtaining fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set.

In the embodiment of the disclosure, the historical charging data of each second candidate abnormal vehicle in the second candidate abnormal vehicle set may be obtained, and the data analysis may be performed on the historical charging data of each second candidate abnormal vehicle.

Optionally, for the historical charging data of any second candidate abnormal vehicle, algorithm processing may be performed on the historical charging data according to an algorithm for obtaining the fitting parameters in the related art, and then, according to the result of the algorithm processing, the fitting parameters of the historical charging data are obtained, and the fitting parameters may be marked as the fitting parameters of the second candidate abnormal vehicle.

S104, in response to the fact that the target charging seat abnormal vehicle with the charging seat abnormality in the second candidate abnormal vehicle set is identified according to the fitting parameters, carrying out abnormality warning on the target charging seat abnormal vehicle.

In the embodiment of the disclosure, whether a vehicle with a charging seat abnormality causing charging abnormality exists in the second candidate abnormal vehicle set may be identified according to the fitting parameters, and the part of vehicles may be marked as target charging seat abnormal vehicles in the second candidate abnormal vehicle set.

Optionally, an abnormality determination condition corresponding to the fitting parameter may be obtained, and each fitting parameter may be compared with the abnormality determination condition, where, for any fitting parameter, when the fitting parameter meets the abnormality determination condition, the second candidate abnormal vehicle corresponding to the fitting parameter may be determined as the target abnormal vehicle of the charging stand abnormality.

Further, the abnormal warning of the abnormal vehicle of the target charging seat can be performed based on a preset warning mode, wherein the vehicle information of the abnormal vehicle of the target charging seat can be sent to related staff through the preset communication mode so as to realize the abnormal warning of the abnormal vehicle of the target charging seat, and the abnormal warning of the abnormal vehicle of the charging seat can also be performed to a driver of the vehicle through an audible and visual warning device configured on the vehicle, and the abnormal warning of the charging seat is not particularly limited herein.

According to the charging seat abnormality warning method, a first candidate abnormal vehicle set of charging abnormality in an initial vehicle set is obtained, abnormal charging piles are filtered on the first candidate abnormal vehicle set to obtain a second candidate abnormal vehicle set, further, fitting parameters of second candidate abnormal vehicles are obtained, whether a target charging seat abnormal vehicle exists in the second candidate abnormal vehicle set or not is identified according to the fitting parameters, and abnormality warning of the target charging seat abnormal vehicle is carried out when the target charging seat abnormal vehicle exists is identified. In the method, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set filtered by the abnormal charging pile, the operator is not required to be relied on for confirmation, the labor dependence degree, the labor cost and the operation cost of abnormal vehicle alarming are reduced, the target charging seat abnormal vehicle is determined through fitting parameters, compared with the prior art that abnormal alarming is carried out after temperature abnormality occurs in the charging equipment through temperature monitoring, the timeliness and the accuracy of vehicle charging abnormal alarming are improved, the safety of vehicle driving is further improved, and the driving experience of a user is optimized.

In the foregoing embodiment, regarding the abnormality alert of the target charging stand abnormal vehicle, it may be further understood with reference to fig. 2, and fig. 2 is a flow chart of a charging stand abnormality alert method according to another embodiment of the disclosure, as shown in fig. 2, the method includes:

S201, acquiring a first target analysis data set of an initial vehicle set, and acquiring a fragment temperature rise rate set in each first target analysis data set.

Optionally, a first charging data set of each initial vehicle is obtained, and each first charging data set is integrated to obtain a total second charging data set.

In the embodiment of the disclosure, whether the target charging seat abnormal vehicle with the abnormal charging seat exists in the initial vehicle set can be identified through the historical charging data of the initial vehicle set, wherein the historical charging data can be marked as the first charging data set of the initial vehicle set.

Optionally, the historical charging data may be collected based on the vehicle-end system and uploaded to the cloud end, so as to obtain the first charging data set of each initial vehicle from the cloud end.

As an example, as shown in fig. 3, when the charging system is connected with the charging pile to perform the charging action of the vehicle, the battery management system shown in fig. 3 may collect charging data in the charging system, where the temperature data of the charging seat may also be collected by the temperature collection module shown in fig. 3. Further, the collected charging data are transmitted to the whole vehicle controller shown in fig. 3, and the collected data are uploaded to the cloud end through the whole vehicle controller by the internet of vehicles system shown in fig. 3.

The first charging data sets may be combined and integrated, and the data set obtained after the combination and integration is marked as the second charging data set.

Optionally, a first grouping item set of the second charging data set is obtained, and the second charging data set is subjected to item grouping according to the first grouping item set, so that a third charging data set combination under each first grouping item is obtained.

In this embodiment of the present disclosure, the second charging data set includes a plurality of charging data items, for example, an SOC, an ambient temperature, a starting charging stand temperature, and a charging current, and the plurality of items may be marked as first grouping items of the second charging data set, and the plurality of first grouping items form a set, i.e., a first grouping item set.

In this scenario, the second charging data sets may be grouped according to each first grouping item, and a plurality of charging data sets obtained by grouping under each first grouping item may be obtained from the second charging data sets, and marked as a plurality of third charging data sets under each first grouping item, so as to obtain a third charging data set combination composed of the plurality of third charging data sets under each first grouping item.

Taking the SOC and the ambient temperature included in the first grouping item set as an example, the second charging data set may be grouped into items based on the SOC and the ambient temperature, the second charging data set may be grouped according to a grouping policy corresponding to the SOC item, to obtain a third charging data set under the SOC item,

And grouping the second charging data sets according to grouping strategies corresponding to the ambient temperature items to obtain third charging data set combinations under the ambient temperature items.

Optionally, regarding the acquisition of the third charging dataset combination, it may be understood in connection with the following:

The grouping strategies of the first grouping item sets can be obtained to group the second charging data set combinations, and a third charging data set combination under each first grouping item is obtained.

In the embodiment of the disclosure, the grouping policies of the first grouping items in the first grouping item set are different, and in this scenario, the grouping policies under the first grouping items may be acquired, and the second charging data set combinations may be respectively grouped according to the acquired grouping policies.

As an example, taking the environmental temperature as the first grouping item as an example, the grouping policy corresponding to the setting environmental temperature item is grouped into a low-temperature environmental temperature interval and a normal environmental temperature interval based on a preset environmental temperature threshold value.

In this example, the charging data with the environmental temperature lower than or equal to 0 degree in the second charging data set may be divided into one data set as a third charging data set in the low-temperature environmental temperature interval, and the charging data with the environmental temperature higher than 0 degree in the second charging data set may be divided into one data set as a third charging data set in the normal environmental temperature interval, so as to obtain a third charging data set combination under the first grouping item corresponding to the environmental temperature.

As another example, taking the initial charging seat temperature as an example, the grouping strategy for setting the initial charging seat temperature is to group into a low-temperature initial charging seat temperature interval and a normal initial charging seat temperature interval based on a preset initial charging seat temperature threshold.

In this example, the charging data of the second charging data set with the initial charging seat temperature lower than or equal to 0 degree may be divided into one data set as a third charging data set in the low-temperature initial charging seat temperature interval, and the charging data of the second charging data set with the initial charging seat temperature higher than 0 degree may be divided into one data set as a third charging data set in the normal initial charging seat temperature interval, so as to obtain a third charging data set combination under the first grouping item corresponding to the initial charging seat temperature.

As another example, taking an average charging current as an example, wherein a grouping policy of the average charging current as the first grouping item is set to be grouped into an average charging current low-magnification section, a medium-magnification section, and a high-magnification section based on a preset average charging current threshold value.

Wherein, the average charging current threshold value for grouping between the low-rate interval and the medium-rate interval is 1 times of capacity rate (1C), and the average charging current threshold value for grouping between the medium-rate interval and the high-rate interval is 2 times of capacity rate (2C).

In this example, the charging data of the capacity magnification in which the average charging current in the second charging data set is less than or equal to 1 time may be divided into one data set as the third charging data set in the low magnification section. And dividing the charging data of the second charging data set with the average charging current higher than 1-time capacity multiplying power and lower than 2-time capacity multiplying power into one data set as a third charging data set in the middle-rate section. And dividing the charging data with the capacity multiplying power, the average charging current of which is higher than or equal to 2 times in the second charging data set, into one data set, and using the data set as a third charging data set in a high multiplying power interval, so as to obtain a third charging data set combination under a first grouping item corresponding to the average charging current.

Optionally, a first candidate analysis dataset set of the initial vehicle set is obtained from the third charging dataset combination under each first grouping item.

And acquiring a fourth charging data set under each first grouping item from each third charging data set combination, wherein the fourth charging data set under each first grouping item is any data set in the third charging data set combination under each first grouping item aiming at any first grouping item, and acquiring a first candidate analysis data set obtained by combining the fourth charging data sets under each first grouping item so as to obtain a first candidate analysis data set.

In the embodiment of the disclosure, any third charging data set may be obtained from the third charging data set combinations under each first grouping item, and used as the fourth charging data set under each first grouping item, and the fourth charging data sets under each first grouping item are combined according to the data set combination method in the related art, so as to obtain a combined data set, and the data set is marked as a first candidate analysis data set.

As an example, as is known from the above example, the ambient temperature in the first group item set includes a total of 2 third charge data sets of the third charge data set in the low-temperature ambient temperature section and the third charge data set in the normal ambient temperature section, the starting charging seat temperature includes a total of 2 third charge data sets of the third charge data set in the low-temperature starting charging seat temperature section and the third charge data set in the normal starting charging seat temperature, and the average charging current includes a total of 3 third charge data sets of the third charge data set in the low-magnification section, the third charge data set in the medium-magnification section, and the third charge data set in the high-magnification section.

Then in this example any third charge data set may be obtained from the 2 third charge data sets comprised at ambient temperature as the fourth charge data set at ambient temperature, and any third charge data set from the 2 third charge data sets comprised at the starting cradle temperature as the fourth charge data set at the starting cradle temperature, and any third charge data set from the 3 third charge data sets comprised at average charge current as the fourth charge data set at average charge current, and the 3 fourth charge data sets are combined to obtain a combined data set, which may be marked as one of the first candidate analysis data sets obtained in this example.

It should be noted that, the number of the first candidate analysis data sets in this example may be 2×2×3=12, and in this example, the first candidate analysis data set of the initial vehicle set may be obtained based on the 12 first candidate analysis data sets.

Optionally, a first number of segments of each first candidate analysis dataset is obtained, and a first set of target analysis datasets is obtained from the first set of candidate analysis datasets according to the first number of segments.

The method comprises the steps of acquiring a first data fragment set in a first candidate analysis data set according to any first candidate analysis data set, and obtaining the number of first fragments according to the first data fragment set, wherein each charging data in the first data fragment is generated based on the same charging behavior according to any first data fragment.

In the embodiment of the disclosure, for any first candidate analysis data set, a plurality of charging data segments may be included in the first candidate analysis data set and may be marked as first data segments, where for any first data segment, charging data included in the first data segment is generated based on the same charging behavior, and it may be understood that the partial charging data are all data generated by connecting the same charging stand and the same charging pile in the same time range.

In this scenario, the number of all the first data segments included in the first data segment set may be obtained according to the counting method in the related art, where the number is the number of the first segments of the first data segment set.

Optionally, for any of the first candidate analysis data sets, determining the first candidate analysis data set as the first target analysis data set in response to the number of first fragments in the first set of data fragments in the first candidate analysis data set being greater than or equal to a preset first fragment number threshold.

In the embodiment of the disclosure, when the number of the charging data segments in the first candidate analysis data set is small, there is a situation that may cause inaccurate analysis results, under this scenario, the first segment number in each first data segment set may be obtained respectively, and whether the first candidate analysis data set can meet the requirement of performing the charging stand abnormality analysis is identified through the first segment number.

The first segment number of any first candidate analysis data set may be compared with a preset first segment number threshold, and when the first segment number of the first data segments in the first candidate analysis data set is greater than or equal to the first segment number threshold, the first candidate analysis data set may be determined as the first target analysis data set for any first candidate analysis data set.

Optionally, a first set of target analysis data sets of the initial set of vehicles is obtained from each first set of target analysis data.

The method comprises the steps of carrying out construction processing on each first target analysis data set according to a set construction method in the related technology, and further obtaining a first target analysis data set constructed by each first target analysis data set according to a processing result.

Optionally, acquiring a second data segment set in each first target analysis data set, and for any first target analysis data set, acquiring a segment temperature rise rate of each second data segment in the second data segment set of the first target analysis data set, so as to obtain a segment temperature rise rate set of the first target analysis data set.

In the embodiment of the disclosure, a data fragment set in each first target analysis data set may be acquired and marked as a second data fragment set, and each second data fragment is subjected to algorithm processing according to a temperature rise rate acquisition algorithm in the related art, so that a temperature rise rate of each second data fragment is obtained according to a result of the algorithm processing and marked as a fragment temperature rise rate of each second data fragment.

As an example, the segment temperature rise rate of each second data segment is calculated as follows:

In the above formula, V represents a segment temperature rise rate of the second data segment, dT represents a temperature change value in a T period, and T represents a charging time corresponding to the second data segment.

S202, acquiring a first candidate abnormal vehicle set with abnormal charging from an initial vehicle set according to the segment temperature rise rate set of each first target analysis data set.

Optionally, for any first target analysis data set, a first quarter-bit value and a second quarter-bit value of the first target analysis data set are obtained to obtain a quarter-bit distance parameter of the first target analysis data set.

In the embodiment of the disclosure, for any first target analysis data set, the respective segment temperature rise rate of the second data segment set in the first target analysis data set may be obtained, and the segment temperature rise rate of each second data segment is subjected to algorithm processing according to an acquisition algorithm of the quarter bit value in the related art, and the quarter bit value of the first target analysis data set is obtained through the result of the algorithm processing.

Alternatively, the first target analysis data set may include two quarter-bit values, which may be marked as a first quarter-bit value and a second quarter-bit value, respectively, wherein the first quarter-bit value may be the upper quarter-bit value and the second quarter-bit value may be the lower quarter-bit value.

In this scenario, the computation of the quartile range parameter of the first target analysis dataset may be performed according to a preset algorithm, where the algorithm formula may be as follows:

IQR=Q3-Q1

in the above formula, IQR represents a quarter-bit distance parameter of the first target analysis data set, Q3 represents a first quarter-bit value, and Q1 represents a second quarter-bit value.

Optionally, according to the second quarter bit value and the quarter bit distance parameter, an outlier temperature rise rate reference value is obtained, and according to the outlier temperature rise rate reference value, an outlier segment temperature rise rate set in each segment temperature rise rate set is obtained, wherein any one of the outlier segment temperature rise rate sets is smaller than or equal to the outlier temperature rise rate reference value.

In an embodiment of the disclosure, for any first target analysis data set, there may be an outlier data segment in a second data segment set in the first target analysis data set, where the outlier data segment is a data segment of charging anomaly data in the first target analysis data set.

Optionally, the second quarter bit value and the quarter bit distance parameter may be calculated according to a preset algorithm, and the determination value of the outlier data segment of the first target analysis data set is obtained through the result of the algorithm calculation, and the value is marked as an outlier temperature rise rate reference value.

The algorithm formula can be as follows:

Q4=Q1-1.5×IQR

In the above formula, Q4 represents an outlier temperature rise rate reference value, Q1 represents a second quarter-bit value, and IQR represents a quarter-bit distance parameter of the first target analysis data set.

Optionally, for any first target analysis data set, acquiring a fragment temperature rise rate of each second data fragment in the first target analysis data set, comparing each fragment temperature rise rate with an outlier temperature rise rate reference value corresponding to the first target analysis data set, acquiring at least one fragment temperature rise rate smaller than or equal to the outlier temperature rise rate reference value from the fragment temperature rise rates of each second data fragment, and marking the at least one fragment temperature rise rate as at least one outlier fragment temperature rise rate in the first target analysis data set.

Further, constructing a set of outlier segment temperature rise rates measured in the first target analysis data set from the at least one outlier segment temperature rise rate.

Optionally, acquiring respective first vehicle identifications of the outlier segment temperature rise rate sets, and acquiring first candidate abnormal vehicles of the first vehicle identifications in the initial vehicle set to obtain a first candidate abnormal vehicle set.

In the embodiment of the disclosure, the vehicle identifier corresponding to each outlier temperature rise rate segment in the outlier temperature rise rate segment set may be obtained, and marked as the first vehicle identifier of each outlier temperature rise rate segment, so as to obtain at least one vehicle with the same identifier as each first vehicle identifier in the initial vehicle set, where the at least one vehicle may be determined as at least one first candidate abnormal vehicle in the initial vehicle set, so as to obtain a first candidate abnormal vehicle set composed of the at least one first candidate abnormal vehicle.

S203, carrying out abnormal charging pile filtering on the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set.

Optionally, the charging pile identifiers of the first candidate abnormal vehicles in the first candidate abnormal vehicle set are obtained to obtain the number of identifiers of the charging pile identifiers, and the third candidate abnormal vehicle set with the abnormal charging pile in the first candidate abnormal vehicle set is obtained according to the number of identifiers of the charging pile identifiers.

In the embodiment of the disclosure, each first candidate abnormal vehicle in the first candidate abnormal vehicle set has a charging pile connected thereto, wherein a character number of the charging pile may be marked as a charging pile identifier of each first candidate abnormal vehicle.

In this scenario, according to the counting method in the related art, the number of occurrences of the charging data segments of each charging pile identifier in all the charging data segments in the first candidate abnormal vehicle set may be counted, so as to obtain the number of identifiers of each charging pile identifier.

Optionally, for any charging pile identifier, in response to the number of identifiers of the charging pile identifiers being greater than or equal to a preset number of identifiers threshold, determining a corresponding vehicle of the charging pile identifiers in the first set of candidate abnormal vehicles as a third candidate abnormal vehicle of the charging pile abnormality.

In the embodiment of the disclosure, a preset number of identifiers of the charging pile identifiers is obtained, and at least one charging pile identifier greater than or equal to the number of identifiers of the charging pile identifiers is obtained from the number of identifiers of the charging pile identifiers, wherein the at least one charging pile identifier can be determined as a vehicle with the abnormal charging pile in the corresponding vehicle in the first abnormal vehicle candidate set, and can be marked as at least one third abnormal vehicle candidate with the abnormal charging pile, so that a third abnormal vehicle candidate set formed by the at least one third abnormal vehicle candidate is obtained.

Optionally, the abnormal charging pile filtering is performed on the first abnormal vehicle candidate set according to the third abnormal vehicle candidate set, so that the rest abnormal vehicle sets except the third abnormal vehicle candidate set in the first abnormal vehicle candidate set are obtained and serve as the second abnormal vehicle candidate set.

In the embodiment of the disclosure, the third abnormal vehicle candidate set may be filtered and deleted from the first abnormal vehicle candidate set, where the processing procedure may be understood as filtering the abnormal charging piles of the first abnormal vehicle candidate set, so as to obtain a remaining vehicle set except for the third abnormal vehicle candidate in the first abnormal vehicle candidate set, and the portion of the remaining vehicle set may be determined as the second abnormal vehicle candidate set.

S204, a second target analysis data set of a second candidate abnormal vehicle set is obtained.

Optionally, a fifth charging data set of each second candidate abnormal vehicle is acquired.

In the embodiment of the disclosure, the charging data set of each second abnormal candidate vehicle in the set historical time range may be obtained, or the charging data set of each second abnormal candidate vehicle in the preset charging times may be obtained, and marked as the fifth charging data set of each second abnormal candidate vehicle.

Optionally, a second grouping item set of the fifth charging data set is obtained, and the fifth charging data sets are respectively grouped according to the second grouping item set, so that a sixth charging data set combination of the fifth charging data sets under each second grouping item is obtained.

In the embodiment of the present disclosure, the specific content of the second grouping of the items of the fifth charging data sets according to the second grouping item set may be combined with the specific content of the first grouping of the items of the second charging data set, which is set forth in the above embodiment, and is not described herein.

The second grouping item included in the second grouping item set is the same as the first grouping item included in the first grouping item set.

Optionally, a second candidate analysis data set of each second candidate abnormal vehicle is obtained according to the sixth charging data set combination under each second grouping item.

In the embodiment of the present disclosure, regarding the specific content of the second candidate analysis data set of each second candidate abnormal vehicle obtained according to the combination of the sixth charging data set under each second grouping item, the specific content of the first candidate analysis data set may be obtained by combining the third charging data set combination under each first grouping item set as set forth in the above embodiment, which is not described herein.

Optionally, a third data fragment set of each second candidate analysis data set is obtained, and a second target analysis data set is obtained from the second candidate analysis data set according to the third data fragment set.

In the embodiment of the disclosure, the charging data segments in the second candidate analysis data set may be marked as third data segments, so as to obtain a third data segment set composed of at least one third data segment in the second candidate analysis data set.

Optionally, for any second candidate analysis data set, a fourth data fragment set not belonging to the preset abnormal charging pile list is obtained from the third data fragment set of the second candidate analysis data set.

In the embodiment of the disclosure, charging pile information carried in each third data segment may be obtained, and the obtained charging pile information is compared with abnormal charging pile information included in a preset abnormal charging pile list, where, for any third data segment, when a comparison result indicates that the charging pile information carried in the third data segment does not have matched abnormal charging pile information in the abnormal charging pile list, it may be determined that the third data segment is a data segment not belonging to the abnormal charging pile list, where the data segment may be marked as a fourth data segment in a third data segment set.

Further, a fourth set of data segments in the third set of data segments is obtained from at least one fourth data segment included in the third set of data segments.

It should be noted that, the abnormal charging pile list provided in the embodiment of the present disclosure may be constructed based on the charging pile identifier of each third candidate abnormal vehicle in the third candidate abnormal vehicle set with abnormal charging piles provided in the foregoing embodiment, or may be obtained based on statistics by other methods, which is not limited herein specifically.

Optionally, a third set of candidate analysis data sets is obtained from the fourth set of data segments in each second candidate analysis data set.

In the embodiment of the disclosure, for any second candidate analysis data set, a fourth data segment set in the second candidate analysis data set may be obtained, and data segments in the fourth data segment set are combined to obtain a combined data set, and then the data set is marked as a third candidate analysis data set corresponding to the second candidate analysis data set.

Further, a third candidate analysis data set of each second candidate analysis data set is obtained, and the set of partial third candidate analysis data sets is marked as a third candidate analysis data set.

Optionally, for any third candidate analysis data set, determining the third candidate analysis data set as the second target analysis data set in response to the number of second fragments in the fourth data fragment set in the third candidate analysis data set being greater than or equal to the preset second fragment number threshold, so as to obtain a third target analysis data set.

In the embodiment of the present disclosure, further screening is required for the number of data segments in the fourth data segment set in the third candidate analysis data set, where, for any third candidate analysis data set, the number of segments in the fourth data segment set included in the third candidate analysis data set may be acquired and marked as the second number of segments.

Further, a second segment number threshold corresponding to the second segment number is obtained, and when the second segment number is greater than or equal to the second segment number threshold, the third candidate analysis data set can be determined as a second target analysis data set, so that a second target analysis data set composed of a plurality of second target analysis data sets is obtained.

S205, the fitting slope and the fitting degree of each second target analysis data set are obtained to obtain the fitting parameters of each second candidate abnormal vehicle.

In the embodiment of the disclosure, the second target analysis data sets may be subjected to algorithm processing by a fitting slope algorithm and a fitting degree algorithm in the related art, so as to obtain a fitting slope and a fitting degree of the second target analysis data sets according to a result of the algorithm processing.

As one example, the fit slope acquisition formula may be as follows:

In the above formula, k represents a fitting slope, x represents a number of days vector corresponding to the charging data segment, the value may be [1,2,3, … …, L ], y represents historical charging rate data of the last L times of vehicle charging of the second candidate abnormal vehicle, x ^T represents a transpose of x, and c is an operation parameter, where y=kx+c.

And, the formula for obtaining the fitting degree can be as follows:

In the above formula, R represents a fitting degree, y _fit (i) represents a fitting value y corresponding to the ith x, where y _fit (i) =kx (i) +c, c is an operation parameter, avg (y) represents an average value of the set y, and y (i) represents the ith value in the set y.

Further, for any second target analysis data set, the fitting slope and the fitting degree of the second target analysis data set are determined as the fitting parameters of the second target analysis data set.

S206, in response to the fact that the target charging seat abnormal vehicle with the charging seat abnormality in the second candidate abnormal vehicle set is identified according to the fitting parameters, carrying out abnormality warning on the target charging seat abnormal vehicle.

Optionally, a fitting slope and a fitting degree in the fitting parameters of each second target analysis data set are obtained, and for any second target analysis data set, the second target analysis data set is determined to be a target charging seat abnormality data set with abnormal charging seat in response to the fitting slope of the second target analysis data set being greater than or equal to a preset fitting slope threshold and the fitting degree of the second target analysis data set being greater than or equal to a preset fitting degree threshold.

In the embodiment of the disclosure, for the fitting slope and the fitting degree in the fitting parameters of any second target analysis data set, a preset fitting slope threshold corresponding to the fitting slope and a preset fitting degree threshold corresponding to the fitting degree may be obtained, the fitting slope and the corresponding fitting slope threshold may be compared, and the fitting degree and the corresponding fitting degree threshold may be compared.

And when the fit slope is greater than or equal to the fit slope threshold and the fit degree is greater than or equal to the fit degree threshold, determining that the second target analysis data set is a charging data set with abnormal charging seat, and marking the second target analysis data set as a target charging seat abnormal data set.

Optionally, a corresponding vehicle of the target charging seat abnormality data set in the second candidate abnormal vehicle set is obtained as a target charging seat abnormality vehicle of charging seat abnormality, and abnormality warning is carried out on the target charging seat abnormality vehicle.

In the embodiment of the disclosure, a target vehicle identifier corresponding to charging data in a target charging seat abnormal data set may be acquired, the target vehicle identifier is compared with abnormal vehicle identifiers of second candidate abnormal vehicles in a second candidate abnormal vehicle set, an abnormal vehicle identifier matched with the target vehicle identifier is acquired from the abnormal vehicle identifiers of the second candidate abnormal vehicles, and a second candidate abnormal vehicle corresponding to the abnormal vehicle identifier is determined to be a charging seat abnormal target charging seat abnormal vehicle.

Further, the abnormal vehicle of the target charging seat is subjected to abnormal alarm based on a preset alarm strategy.

According to the charging seat abnormality warning method, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set after abnormal charging pile filtering, no confirmation is needed to be conducted by workers, the labor dependence degree, the labor cost and the operation cost of abnormal vehicle warning are reduced, the target charging seat abnormal vehicle is determined through fitting parameters, compared with the method that abnormal warning is conducted after temperature abnormality occurs in charging equipment through temperature monitoring in the related art, timeliness and accuracy of vehicle charging abnormality warning are improved, further safety of vehicle driving is improved, and driving experience of a user is optimized.

To better understand the above embodiments, fig. 4 may be combined, and fig. 4 is a schematic flow chart of a charging seat abnormality warning method according to another embodiment of the disclosure, as shown in fig. 4, where the method includes:

s401, acquiring first charging data sets of an initial vehicle set to obtain second charging data sets after the first charging data sets are integrated.

In the embodiment of the disclosure, the charging data of the vehicle can be recorded through the battery management system, and the recorded charging data is uploaded to the cloud through the data interaction link between the vehicle end and the cloud.

Under the scene, a first charging data set of the initial vehicle set can be obtained from the historical charging data stored in the cloud, and the first charging data sets of all the initial vehicles are combined to obtain a second charging data set.

S402, carrying out project grouping on the second charging data sets to obtain third charging data set combinations under each first grouping project so as to obtain a first candidate analysis array set.

In the embodiment of the disclosure, the second charging data sets may be grouped according to the first grouping item set to obtain third charging data set combinations under each first grouping item, further, any data set is obtained from the third charging data set combinations under each first grouping item to be used as fourth charging data sets under each first grouping item, and each fourth charging data set is combined to obtain the first candidate analysis data set.

Further, a first set of candidate analytical data sets is obtained from the plurality of first candidate analytical data sets.

S403, identifying whether the first segment number of the first data segment in the first candidate analysis data set is greater than or equal to a first segment number threshold for any first candidate analysis data set.

And determining the first candidate analysis data set as the first target analysis data set when the first fragment number is greater than or equal to the first fragment number threshold.

And when the number of the first segments is smaller than the threshold value of the number of the first segments, acquiring charging pile identification information corresponding to the first data segments, and storing the charging pile identification information in an abnormal charging pile list shown in fig. 4.

S404, when the number of the first fragments is greater than or equal to the threshold value of the number of the first fragments, determining the first candidate analysis data set as the first target analysis data set to obtain a first target analysis data set.

The first segment number threshold may be set to 100, or may be set to another value, which is not specifically limited herein.

S405, acquiring a fragment temperature rise rate set of each first target analysis data set.

The algorithm processing may be performed on each first target analysis data set according to the algorithm of the temperature rise rate set forth in the foregoing embodiment, so as to obtain the segment temperature rise rate of each second data segment in each first target analysis data set according to the result of the algorithm processing, thereby obtaining the segment temperature rise rate set of each first target analysis data set.

S406, acquiring a first candidate abnormal vehicle set with abnormal charging from the initial vehicle set according to the segment temperature rise rate set.

In the embodiment of the disclosure, for any first target analysis data set, an outlier data segment in the first target analysis data set may be obtained through a segment temperature rise rate set of the first target analysis data set, where the outlier data segment is a charging data segment corresponding to a charging abnormality.

Optionally, an outlier temperature rise rate reference value of the first target analysis data set may be obtained according to the set of segment temperature rise rates, and segment temperature rise rates of the second data segments are respectively compared with the outlier temperature rise rate reference value, and for any segment temperature rise rate, when the segment temperature rise rate is less than or equal to the outlier temperature rise rate reference value, it may be determined that the segment temperature rise rate is an outlier segment temperature rise rate, and the data segment corresponding to the outlier segment temperature rise rate is an outlier data segment in the first target analysis data set.

Further, a corresponding vehicle identified in the initial set of vehicles by a first vehicle corresponding to the outlier data segment is determined as a first candidate abnormal vehicle.

S407, carrying out abnormal charging pile filtering on the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set.

In the embodiment of the present disclosure, a third abnormal vehicle candidate set in which the charging pile is abnormal may be obtained from the first abnormal vehicle candidate set, as shown in fig. 4, and abnormal charging pile information of each third abnormal vehicle candidate in the third abnormal vehicle candidate set may be stored in the abnormal charging pile list shown in fig. 4.

S408, a second target analysis data set of each second candidate abnormal vehicle is acquired.

Alternatively, the last M times of historical charging data of each second abnormal candidate vehicle may be obtained, and the second target analysis data set of each second abnormal candidate vehicle may be obtained from the M times of historical charging data, where M may be 50 or another value, and is not limited herein specifically.

The method comprises the steps of obtaining fifth charging data sets of second candidate abnormal vehicles, and grouping the fifth charging data sets according to a second grouping item set to obtain sixth charging data set combinations of the fifth charging data sets under the second grouping items.

Optionally, for any fifth charging data set, according to a sixth charging data set combination of the fifth charging data set under each second grouping item, a second candidate analysis data set screened in the fifth charging data set is obtained.

Further, charging pile information of each third data segment in each second candidate analysis data set is obtained, so that a fourth data segment set which does not belong to an abnormal charging pile list in each second candidate analysis data set is obtained, and each third candidate analysis data set is obtained.

Optionally, at least part of the data sets with the second segment number greater than or equal to the second segment number threshold value are obtained from each third candidate analysis data set, so as to obtain a second target analysis data set of the second candidate abnormal vehicle corresponding to the fifth charging data set.

S409, obtaining the fitting slope and fitting degree of each second target analysis data set.

The algorithm processing may be performed on each second target analysis data set according to the algorithm set forth in the foregoing embodiment, so as to obtain the fitting slope and the fitting degree of each second target analysis data set.

S410, identifying whether the fitting slope of the second target analysis data set is greater than 0 and the fitting degree is greater than 0.9 for any second target analysis data set.

As an example, if the fitting slope threshold corresponding to the fitting slope is set to be 0 and the fitting degree threshold corresponding to the fitting degree is set to be 0.9, the fitting slope and the fitting degree of each second target analysis data set may be respectively compared and identified with the respective thresholds.

S411, when the fitting slope is greater than 0 and the fitting degree is greater than 0.9, determining the abnormal vehicle of the target charging seat according to the second target analysis data set.

It can be understood that when the fitting slope of any one of the second target analysis data sets is greater than 0 and the fitting degree is greater than 0.9, the fitting temperature rise rate result of the second target analysis data set shows an ascending trend, so that it can be determined that the charging seat of the vehicle corresponding to the second target analysis data set may be abnormal.

In this scenario, it may be determined that the vehicle is a target charging dock anomaly vehicle.

S412, carrying out abnormality warning on the target charging seat abnormal vehicle.

In the embodiment of the disclosure, the abnormal warning of the abnormal target charging seat vehicle can be performed according to a preset warning policy, wherein the information warning can be performed on the user and the rear-end staff of the abnormal target charging seat vehicle in a reserved communication mode, and the user can be warned in a warning mode configured at the vehicle end, which is not particularly limited herein.

The foregoing embodiments of the present disclosure provide a charging stand abnormality warning device, and since the charging stand abnormality warning device provided by the embodiment of the present disclosure corresponds to the charging stand abnormality warning method provided by the foregoing embodiments, the implementation of the foregoing charging stand abnormality warning method is also applicable to the charging stand abnormality warning device provided by the embodiment of the present disclosure, which is not described in detail in the following embodiments.

Fig. 5 is a schematic structural diagram of a charging stand abnormality warning device according to an embodiment of the disclosure, as shown in fig. 5, the charging stand abnormality warning device 500 includes a first obtaining module 51, a filtering module 52, a second obtaining module 53, and a warning module 54, where:

A first obtaining module 51, configured to obtain a first abnormal vehicle candidate set with abnormal charging in the initial vehicle set;

The filtering module 52 is configured to perform abnormal charging pile filtering on the first abnormal candidate vehicle set to obtain a filtered second abnormal candidate vehicle set;

a second obtaining module 53, configured to obtain fitting parameters of each second abnormal candidate vehicle in the second abnormal candidate vehicle set;

and an alarm module 54, configured to, in response to identifying, according to the fitting parameters, a target abnormal charging seat vehicle in which the charging seat abnormality exists in the second set of candidate abnormal vehicle, perform an abnormality alarm on the target abnormal charging seat vehicle.

In the embodiment of the present disclosure, the first obtaining module 51 is further configured to: acquiring a first target analysis data set of an initial vehicle set, and acquiring a fragment temperature rise rate set in each first target analysis data set; and acquiring a first candidate abnormal vehicle set with abnormal charging from the initial vehicle set according to the segment temperature rise rate set of each first target analysis data set.

In the embodiment of the present disclosure, the first obtaining module 51 is further configured to: acquiring first charging data sets of all initial vehicles, and integrating all the first charging data sets to obtain a total second charging data set; acquiring a first grouping project set of the two charging data sets, and grouping the second charging data sets according to the first grouping project set to obtain a third charging data set combination under each first grouping project; combining according to the third charging data sets under each first grouping item to obtain a first candidate analysis data set of the initial vehicle set; a first number of segments for each first candidate analysis dataset is obtained, and a first set of target analysis datasets is obtained from the first set of candidate analysis datasets according to the first number of segments.

In the embodiment of the present disclosure, the first obtaining module 51 is further configured to: acquiring a fourth charging data set under each first grouping item from each third charging data set combination, wherein the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item aiming at any first grouping item; and obtaining a first candidate analysis data set obtained by combining the fourth charging data sets under each first grouping item so as to obtain a first candidate analysis data set.

In the embodiment of the present disclosure, the first obtaining module 51 is further configured to: acquiring a first data fragment set in a first candidate analysis data set aiming at any first candidate analysis data set, and obtaining the first fragment number according to the first data fragment set, wherein each charging data in the first data fragment is generated based on the same charging behavior aiming at any first data fragment; determining the first candidate analysis data set as a first target analysis data set in response to the first number of segments being greater than or equal to a preset first number of segments threshold; and obtaining a first target analysis data set of the initial vehicle set according to each first target analysis data set.

In the embodiment of the present disclosure, the first obtaining module 51 is further configured to: acquiring a second data fragment set in each first target analysis data set; and aiming at any first target analysis data set, acquiring the fragment temperature rise rate of each second data fragment in the second data fragment set of the first target analysis data set so as to obtain the fragment temperature rise rate set of the first target analysis data set.

In the embodiment of the present disclosure, the first obtaining module 51 is further configured to: for any first target analysis data set, acquiring a first quarter bit value and a second quarter bit value of the first target analysis data set to obtain a quarter bit distance parameter of the first target analysis data set; acquiring an outlier temperature rise rate reference value according to the second quarter bit value and the quarter bit distance parameter; acquiring an outlier segment temperature rise rate set in each segment temperature rise rate set according to an outlier temperature rise rate reference value, wherein any outlier segment temperature rise rate in the outlier segment temperature rise rate set is smaller than or equal to the outlier temperature rise rate reference value; and acquiring the vehicle identifications of the outlier segment temperature rise rate sets, and acquiring first candidate abnormal vehicles of the vehicle identifications in the initial vehicle set to obtain a first candidate abnormal vehicle set.

In the disclosed embodiment, the filtering module 52 is further configured to: acquiring charging pile identifiers of all first candidate abnormal vehicles in a first candidate abnormal vehicle set to obtain the number of the identifiers of all the charging pile identifiers; acquiring a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set according to the identification number of each charging pile identification; and carrying out abnormal charging pile filtering on the first candidate abnormal vehicle set according to the third candidate abnormal vehicle set to obtain the rest abnormal vehicle sets except the third candidate abnormal vehicle set in the first candidate abnormal vehicle set as a second candidate abnormal vehicle set.

In the disclosed embodiment, the filtering module 52 is further configured to: and for any charging pile identifier, determining the corresponding vehicle of the charging pile identifier in the first candidate abnormal vehicle set as a third candidate abnormal vehicle of the charging pile abnormality in response to the number of identifiers of the charging pile identifiers being greater than or equal to a preset identifier number threshold.

In the embodiment of the present disclosure, the second obtaining module 53 is further configured to: acquiring a second target analysis data set of a second candidate abnormal vehicle set; and obtaining the fitting slope and the fitting degree of each second target analysis data set to obtain the fitting parameters of each second candidate abnormal vehicle.

In the embodiment of the present disclosure, the second obtaining module 53 is further configured to: acquiring a fifth charging data set of each second candidate abnormal vehicle; acquiring a second grouping item set of the fifth charging data set, and respectively grouping the items of each fifth charging data set according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item; obtaining a second candidate analysis data set of each second candidate abnormal vehicle according to the sixth charging data set combination under each second grouping item; and acquiring a third data fragment set of each second candidate analysis data set, and acquiring the second target analysis data set from the second candidate analysis data set according to the third data fragment set.

In the embodiment of the present disclosure, the second obtaining module 53 is further configured to: acquiring a fourth data fragment set which does not belong to a preset abnormal charging pile list from a third data fragment set of the second candidate analysis data set; obtaining a third candidate analysis data set according to the fourth data fragment set in each second candidate analysis data set; and for any third candidate analysis data set, determining the third candidate analysis data set as a second target analysis data set in response to the number of second fragments in the fourth data fragment set in the third candidate analysis data set being greater than or equal to a preset second fragment number threshold, so as to obtain a second target analysis data set.

In the embodiment of the present disclosure, the alarm module 54 is further configured to: obtaining a fitting slope and a fitting degree in fitting parameters of each second target analysis data set; for any second target analysis data set, determining that the second target analysis data set is a target charging seat abnormality data set with abnormal charging seat in response to the fitting slope of the second target analysis data set being greater than or equal to a preset fitting slope threshold and the fitting degree of the second target analysis data set being greater than or equal to a preset fitting degree threshold; and acquiring a corresponding vehicle of the target charging seat abnormal data set in the second candidate abnormal vehicle set as a target charging seat abnormal vehicle with abnormal charging seat, and carrying out abnormal warning on the target charging seat abnormal vehicle.

The charging seat abnormality warning device provided by the disclosure obtains a first candidate abnormal vehicle set with abnormal charging in an initial vehicle set, carries out abnormal charging pile filtering on the first candidate abnormal vehicle set to obtain a second candidate abnormal vehicle set, further obtains fitting parameters of each second candidate abnormal vehicle, identifies whether a target charging seat abnormal vehicle exists in the second candidate abnormal vehicle set according to the fitting parameters, and carries out abnormality warning on the target charging seat abnormal vehicle when the target charging seat abnormal vehicle exists in the second candidate abnormal vehicle set. In the method, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set filtered by the abnormal charging pile, the operator is not required to be relied on for confirmation, the labor dependence degree, the labor cost and the operation cost of abnormal vehicle alarming are reduced, the target charging seat abnormal vehicle is determined through fitting parameters, compared with the prior art that abnormal alarming is carried out after temperature abnormality occurs in the charging equipment through temperature monitoring, the timeliness and the accuracy of vehicle charging abnormal alarming are improved, the safety of vehicle driving is further improved, and the driving experience of a user is optimized.

In order to achieve the above embodiments, the present disclosure further provides a vehicle, where the vehicle is configured to implement the method for alarming abnormality of a charging stand set provided in the above embodiments.

To achieve the above embodiments, the present disclosure also provides an electronic device, a computer-readable storage medium, and a computer program product.

Fig. 6 is a block diagram of an electronic device 600 according to an embodiment of the disclosure, as shown in fig. 6, the electronic device 600 includes a memory 601, a processor 602, and a computer program stored on the memory 601 and capable of running on the processor 602, where the processor 602 executes program instructions to implement the method for alarming of abnormal charging seat provided in the above embodiment.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out the methods themselves may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a grid browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication grid). Examples of communication grids include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain grids.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communications grid. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual PRIVATE SERVER" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates blockchains.

In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

Those of ordinary skill in the art will appreciate that all or part of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, and the program may be stored in a computer readable storage medium, where the program when executed includes one or a combination of the steps of the method embodiments.

Furthermore, each functional unit in the embodiments of the present disclosure may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented as software functional modules and sold or used as a stand-alone product.

The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A charging stand abnormality warning method, characterized by comprising:

acquiring a first candidate abnormal vehicle set with abnormal charging in an initial vehicle set;

performing abnormal charging pile filtering on the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set;

Obtaining fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set;

And in response to identifying a target charging seat abnormal vehicle with charging seat abnormality in the second candidate abnormal vehicle set according to the fitting parameters, carrying out abnormality warning on the target charging seat abnormal vehicle.

2. The method of claim 1, wherein the obtaining a first set of candidate abnormal vehicles for the charge anomaly in the initial set of vehicles comprises:

Acquiring a first target analysis data set of the initial vehicle set, and acquiring a fragment temperature rise rate set in each first target analysis data set;

and acquiring the first candidate abnormal vehicle set with abnormal charging from the initial vehicle set according to the segment temperature rise rate set of each first target analysis data set.

3. The method of claim 2, wherein the obtaining the first set of target analysis data sets of the initial set of vehicles comprises:

Acquiring first charging data sets of all initial vehicles, and integrating all the first charging data sets to obtain a total second charging data set;

Acquiring a first grouping item set of the two charging data sets, and grouping items of the second charging data set according to the first grouping item set to obtain a third charging data set combination under each first grouping item;

combining according to the third charging data sets under each first grouping item to obtain a first candidate analysis data set of the initial vehicle set;

A first number of segments for each first candidate analysis dataset is obtained, and the first set of target analysis datasets is obtained from the first set of candidate analysis datasets according to the first number of segments.

4. A method according to claim 3, wherein said combining said first candidate analysis dataset of said initial vehicle set from said third charging dataset under each first grouping comprises:

Acquiring a fourth charging data set under each first grouping item from each third charging data set combination, wherein the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item for any first grouping item;

and obtaining a first candidate analysis data set obtained by combining the fourth charging data sets under each first grouping item, so as to obtain the first candidate analysis data set.

5. The method of claim 3, wherein the obtaining a first number of segments for each first candidate analysis dataset and obtaining the first set of target analysis datasets from the first set of candidate analysis datasets based on the first number of segments comprises:

for any first candidate analysis data set, acquiring a first data fragment set in the first candidate analysis data set, and obtaining the first fragment number according to the first data fragment set, wherein for any first data fragment, each charging data in the first data fragment is generated based on the same charging behavior;

Determining the first candidate analysis data set as a first target analysis data set in response to the first number of segments being greater than or equal to a preset first number of segments threshold;

and obtaining the first target analysis data set of the initial vehicle set according to each first target analysis data set.

6. The method of claim 2, wherein the obtaining a set of fragment rate of rise in each first target analysis data set comprises:

Acquiring a second data fragment set in each first target analysis data set;

and aiming at any first target analysis data set, acquiring the fragment temperature rise rate of each second data fragment in the second data fragment set of the first target analysis data set so as to acquire the fragment temperature rise rate set of the first target analysis data set.

7. The method of claim 2, wherein the obtaining the first set of candidate abnormal vehicles for charge anomalies from the initial set of vehicles based on the set of segment temperature rise rates for each first target analysis data set comprises:

For any first target analysis data set, acquiring a first quarter bit value and a second quarter bit value of the first target analysis data set to obtain a quarter bit distance parameter of the first target analysis data set;

Acquiring an outlier temperature rise rate reference value according to the second quarter bit value and the quarter bit distance parameter;

Acquiring an outlier segment temperature rise rate set in each segment temperature rise rate set according to the outlier temperature rise rate reference value, wherein any outlier segment temperature rise rate in the outlier segment temperature rise rate set is smaller than or equal to the outlier temperature rise rate reference value;

And acquiring respective vehicle identifications of the outlier segment temperature rise rate sets, and acquiring first candidate abnormal vehicles of each vehicle identification in the initial vehicle set to obtain the first candidate abnormal vehicle set.

8. The method of claim 1, wherein the performing the filtering of the abnormal charging pile on the first set of abnormal vehicle candidates to obtain a filtered second set of abnormal vehicle candidates comprises:

obtaining charging pile identifiers of all first candidate abnormal vehicles in the first candidate abnormal vehicle set so as to obtain the number of the identifiers of all the charging pile identifiers;

acquiring a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set according to the identification number of each charging pile identification;

And carrying out abnormal charging pile filtering on the first candidate abnormal vehicle set according to the third candidate abnormal vehicle set to obtain the rest abnormal vehicle sets except the third candidate abnormal vehicle set in the first candidate abnormal vehicle set as the second candidate abnormal vehicle set.

9. The method of claim 8, wherein the obtaining a third set of abnormal vehicle candidates with abnormal charging piles from the first set of abnormal vehicle candidates according to the number of identifiers of each charging pile identifier includes:

And for any charging pile identifier, determining the corresponding vehicle of the charging pile identifier in the first candidate abnormal vehicle set as a third candidate abnormal vehicle with the charging pile abnormality in response to the number of identifiers of the charging pile identifiers being greater than or equal to a preset identifier number threshold.

10. The method of claim 1, wherein the obtaining fitting parameters for each second candidate abnormal vehicle in the second set of candidate abnormal vehicles comprises:

acquiring a second target analysis data set of the second candidate abnormal vehicle set;

and obtaining the fitting slope and the fitting degree of each second target analysis data set to obtain the fitting parameters of each second candidate abnormal vehicle.

11. The method of claim 10, wherein the obtaining a second set of target analysis data sets for the second set of candidate abnormal vehicles comprises:

acquiring a fifth charging data set of each second candidate abnormal vehicle;

Acquiring a second grouping item set of the fifth charging data set, and respectively grouping items of each fifth charging data set according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item;

Obtaining a second candidate analysis data set of each second candidate abnormal vehicle according to the sixth charging data set combination under each second grouping item;

and acquiring a third data fragment set of each second candidate analysis data set, and acquiring the second target analysis data set from the second candidate analysis data set according to the third data fragment set.

12. The method of claim 11, wherein the obtaining a third set of data segments for each second candidate analysis data set and obtaining the second set of target analysis data sets from the second set of candidate analysis data sets based on the third set of data segments comprises:

For any second candidate analysis data set, acquiring a fourth data fragment set which does not belong to a preset abnormal charging pile list from a third data fragment set of the second candidate analysis data set;

obtaining a third candidate analysis data set according to the fourth data fragment set in each second candidate analysis data set;

and for any third candidate analysis data set, determining the third candidate analysis data set as a second target analysis data set in response to the number of second fragments in a fourth data fragment set in the third candidate analysis data set being greater than or equal to a preset second fragment number threshold, so as to obtain the second target analysis data set.

13. The method of claim 1, wherein the alerting the target cradle anomaly vehicle of the presence of a cradle anomaly in the second set of candidate anomaly vehicles in response to identifying the target cradle anomaly vehicle as having a cradle anomaly based on the fit parameters comprises:

obtaining a fitting slope and a fitting degree in fitting parameters of each second target analysis data set;

for any second target analysis data set, determining that the second target analysis data set is a target charging seat abnormality data set with abnormal charging seat in response to the fitting slope of the second target analysis data set being greater than or equal to a preset fitting slope threshold and the fitting degree of the second target analysis data set being greater than or equal to a preset fitting degree threshold;

And acquiring a corresponding vehicle of the target charging seat abnormal data set in the second candidate abnormal vehicle set as the target charging seat abnormal vehicle with abnormal charging seat, and carrying out abnormal warning on the target charging seat abnormal vehicle.

14. A charging stand abnormality warning device, characterized by comprising:

The first acquisition module is used for acquiring a first candidate abnormal vehicle set with abnormal charging in the initial vehicle set;

the filtering module is used for filtering the abnormal charging piles of the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set;

the second acquisition module is used for acquiring fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set;

And the alarming module is used for alarming the abnormal target charging seat in response to the fact that the abnormal target charging seat in the second candidate abnormal vehicle set is identified according to the fitting parameters.

15. A vehicle, characterized in that it is adapted to implement the method according to any of the preceding claims 1-13.

16. An electronic device, comprising:

A processor;

A memory for storing executable instructions of the processor;

Wherein the processor is configured to execute instructions to implement the method of any of claims 1-13.

17. A computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the method of any of claims 1-13.