CN111523609A - Vehicle data processing method and device, computer equipment and storage medium - Google Patents
Vehicle data processing method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a vehicle data processing method, a vehicle data processing device, a computer device and a storage medium. The method comprises the following steps: receiving an event to be analyzed sent by a vehicle terminal, wherein the event to be analyzed is obtained after the vehicle terminal sorts the acquired vehicle original data; performing big data analysis on the event to be analyzed so as to classify the event to be analyzed according to standard event purposes; carrying out optimization processing on the classified events to be analyzed to obtain valuable events; acquiring data requirements corresponding to each service, and extracting the application of a requirement event contained in the data requirements; extracting demand events corresponding to the data demands from the valuable events according to the demand event usages and the standard event usages; and sending the demand event to a server corresponding to the service. By adopting the method, the resource occupation of the server can be reduced, and the data processing efficiency is improved.
Description
Technical Field
The present application relates to the field of intelligent vehicle technologies, and in particular, to a vehicle data processing method and apparatus, a computer device, and a storage medium.
Background
Modern automobile industry rapid development, automatic driving, the car networking, various novel functions such as long-range and user experience are introduced into the car, the promotion of the complexity geometric grade of car, communication load in the car is from original 30-40% to present 70-90%, the one time more, thereby operation monitoring and the capture of unusual action to the vehicle have brought very big puzzlement, since having had the car networking after, data can upload to the high in the clouds in real time, a large amount of data are gathered after uploading, utilize data statistics and model algorithm to carry out various big data analysis.
However, at present, all the collected raw data are uploaded to the cloud end by the vehicle, and when the cloud end processes the raw data, large data analysis can be performed only after judgment of a large amount of redundant information is performed, so that the cloud end is excessively consumed.
Disclosure of Invention
In view of the above, it is necessary to provide a vehicle data processing method, apparatus, computer device, and storage medium capable of reducing server resource consumption in view of the above technical problems.
A vehicle data processing method, the method comprising:
receiving an event to be analyzed sent by a vehicle terminal, wherein the event to be analyzed is obtained after the vehicle terminal sorts the acquired vehicle original data;
performing big data analysis on the event to be analyzed so as to classify the event to be analyzed according to standard event purposes;
carrying out optimization processing on the classified events to be analyzed to obtain valuable events;
acquiring data requirements corresponding to each service, and extracting the application of a requirement event contained in the data requirements;
extracting demand events corresponding to the data demands from the valuable events according to the demand event usages and the standard event usages;
and sending the demand event to a server corresponding to the service.
In the embodiment, the received data is obtained by the vehicle terminal after event sorting of the collected vehicle original data, so that redundant data processing of the server can be reduced, the server classifies the data during processing, and then performs optimization processing, the redundant data is reduced, so that valuable events can be distributed according to the requirements of each service conveniently, the resource occupation of the server is reduced, and the data processing efficiency is improved.
In one embodiment, before the big data analysis of the event to be analyzed to classify the event to be analyzed according to a standard event usage, the method further includes:
extracting abnormal events from the events to be analyzed, and judging whether the abnormal events are useful events or not;
when the abnormal event is useless, deleting the abnormal event from the event to be analyzed;
the big data analysis of the event to be analyzed is performed to classify the event to be analyzed according to standard event usage, and the big data analysis comprises the following steps:
and carrying out big data analysis on the events to be analyzed, in which the useless events are deleted, so as to classify the events to be analyzed according to the standard event usage.
In the embodiment, the server preferentially extracts the abnormal events during processing, so that the value of normal data is far less than that of abnormal data during the driving process of the vehicle, and redundant data is reduced through further processing.
In one embodiment, the extracting abnormal events from the events to be analyzed includes:
extracting historical vehicle data corresponding to the event to be analyzed, and extracting a historical event corresponding to the event to be analyzed from the historical vehicle data;
judging whether the event to be analyzed changes or not according to the historical event;
and when the event to be analyzed changes, extracting the changed event to be analyzed as an abnormal event.
In the embodiment, when judging whether the event to be analyzed is an abnormal event, longitudinal comparison can be performed, that is, data of changes of the vehicle in the driving process is determined through a longitudinal global view angle, and the data is used as abnormal data to perform subsequent analysis and processing, so that redundant data is reduced, resource occupation of a server is reduced, and data processing efficiency is improved.
In one embodiment, the extracting abnormal events from the events to be analyzed includes:
extracting the vehicle type of the vehicle corresponding to the event to be analyzed;
acquiring a first vehicle event which is uploaded by a vehicle of the same type as the vehicle model and is of the same type as the event to be analyzed;
judging whether a first difference value between the event to be analyzed and the first vehicle event is smaller than a first preset value or not;
and when the first difference value is not smaller than a first preset value, extracting the event to be analyzed as an abnormal event.
In the above embodiment, when it is determined whether an event to be analyzed is an abnormal event, a transverse comparison may be performed, that is, whether corresponding data of the same vehicle type are consistent is determined through a transverse global view, and the inconsistent data is used as abnormal data to perform subsequent analysis processing, so that redundant data is reduced, resource occupation of a server is reduced, and data processing efficiency is improved.
In one embodiment, the extracting an abnormal event from the event to be analyzed and determining whether the abnormal event is a useful event includes:
extracting abnormal events from the events to be analyzed, and caching the abnormal events;
extracting the cached abnormal events and judging whether the abnormal events are useful events or not;
the big data analysis of the event to be analyzed is performed to classify the event to be analyzed according to standard event usage, and the big data analysis comprises the following steps:
when the abnormal event is useful, generating a correction event according to a plurality of useful events;
performing big data analysis on the correction event to classify the event to be analyzed according to standard event usage.
In the above embodiment, because the physical meanings of some abnormal events are the same, only one of the abnormal events may be needed, and the server deletes other abnormal events, so that the subsequent processing data is reduced, the resource occupation of the server is reduced, and the data processing efficiency is improved.
In one embodiment, the optimizing the classified events to be analyzed to obtain the valuable events includes:
longitudinally calculating a second difference value of the historical events corresponding to the vehicle terminal of the events to be analyzed in each classification;
and acquiring the event to be analyzed corresponding to the maximum second difference value as a valuable event.
In the embodiment, the redundant events are deleted through longitudinal judgment, so that the resource occupation of the server is reduced, and the data processing efficiency is improved.
In the embodiment, the redundant events are deleted through transverse judgment, so that the resource occupation of the server is reduced, and the data processing efficiency is improved.
Before the event to be analyzed after the classification is optimized to obtain the valuable event, the method further comprises the following steps:
transversely acquiring a second vehicle event which is the same as the vehicle type of the vehicle terminal and corresponds to the event to be analyzed;
and when a third difference value between the event to be analyzed and the second vehicle event is smaller than a third preset value, deleting the event to be analyzed.
In the embodiment, the redundant events are deleted through transverse judgment, so that the resource occupation of the server is reduced, and the data processing efficiency is improved.
A vehicle data processing apparatus, the apparatus comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving an event to be analyzed sent by a vehicle terminal, and the event to be analyzed is obtained after the vehicle terminal sorts the acquired vehicle original data;
the classification module is used for carrying out big data analysis on the event to be analyzed so as to classify the event to be analyzed according to the purpose of a standard event;
the optimization module is used for optimizing the classified events to be analyzed to obtain valuable events;
the demand acquisition module is used for acquiring data demands corresponding to each service and extracting demand event purposes contained in the data demands;
the event extraction module is used for extracting a demand event corresponding to the data demand from the valuable events according to the demand event usage and the standard event usage;
and the sending module is used for sending the demand event to a server corresponding to the service.
In the embodiment, the received data is obtained by the vehicle terminal after event sorting of the collected vehicle original data, so that redundant data processing of the server can be reduced, the server classifies the data during processing, and then performs optimization processing, the redundant data is reduced, so that valuable events can be distributed according to the requirements of each service conveniently, the resource occupation of the server is reduced, and the data processing efficiency is improved.
A computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
In the embodiment, the received data is obtained by the vehicle terminal after event sorting of the collected vehicle original data, so that redundant data processing of the server can be reduced, the server classifies the data during processing, and then performs optimization processing, the redundant data is reduced, so that valuable events can be distributed according to the requirements of each service conveniently, the resource occupation of the server is reduced, and the data processing efficiency is improved.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
In the embodiment, the received data is obtained by the vehicle terminal after event sorting of the collected vehicle original data, so that redundant data processing of the server can be reduced, the server classifies the data during processing, and then performs optimization processing, the redundant data is reduced, so that valuable events can be distributed according to the requirements of each service conveniently, the resource occupation of the server is reduced, and the data processing efficiency is improved.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of a vehicle data processing method;
FIG. 2 is a schematic flow chart diagram of a vehicle data processing method in one embodiment;
FIG. 3 is a schematic flow chart diagram of a vehicle data processing method in another embodiment;
FIG. 4 is a block diagram showing the construction of a vehicle data processing apparatus according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The vehicle data processing method provided by the application can be applied to the application environment shown in fig. 1. The vehicle terminal 102 communicates with the cloud 104 through a network. The vehicle terminal 102 collects original data of a vehicle, sorts the vehicle original data star events and sends the sorted vehicle original data star events to the cloud 104, and the cloud 104 performs big data analysis on events to be analyzed so as to classify the events to be analyzed according to standard event purposes; carrying out optimization processing on the classified events to be analyzed to obtain valuable events; in this way, the cloud 104 can acquire the data requirements corresponding to each service, and extract the requirement events corresponding to the data requirements from the valuable events; and sending the demand event to a server corresponding to the service. The vehicle terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and the like installed in the vehicle, and a dedicated terminal is configured in each vehicle or a substitute controller meeting the acquisition requirement is used as a carrier, and may be in a hardware form or may be embedded in an existing terminal by using software as a carrier, as long as network data in the vehicle can be obtained and connected to the cloud, and a supporting data recording carrier is provided. Cloud 104 may be implemented as a stand-alone cloud or as a cloud cluster consisting of multiple clouds.
In one embodiment, as shown in fig. 2, a vehicle data processing method is provided, which is exemplified by the method applied to the cloud end in fig. 1, and includes the following steps:
s202: and receiving an event to be analyzed sent by the vehicle terminal, wherein the event to be analyzed is obtained after the vehicle terminal sorts the acquired vehicle original data.
Specifically, the event to be analyzed is obtained by the vehicle terminal sorting vehicle original data acquired by the vehicle terminal through the event, wherein the event sorting comprises the steps that the vehicle terminal generates a basic event according to the vehicle original data, then the basic event is subjected to abnormity judgment to obtain an abnormal event and a normal event, and the normal event is subjected to secondary sorting, wherein the step of directly extracting the normal event or generating an event to be analyzed according to a plurality of normal events, and both the abnormal event and the event after secondary sorting can be used as the event to be analyzed. Therefore, firstly, the vehicle raw data is generated into basic events, namely, the events replace data collection, the transmission amount of the data can be reduced, and secondly, the basic events are subjected to secondary sorting to reduce the transmission of useless events, so that the transmission of the data is further reduced. The vehicle raw data for generating the basic events includes, but is not limited to, event-type data, numerical-type data, and fault data, so that the generated basic events include, but are not limited to, event-type events (e.g., the quantities of signals for braking, opening and closing doors and windows are counted according to times), numerical-type events (e.g., the quantity of fuel, the engine speed, the vehicle speed and the like are recorded according to numerical changes), and fault events (e.g., fault events are recorded after fault signals are detected, and fault code records are obtained).
S204: and carrying out big data analysis on the event to be analyzed so as to classify the event to be analyzed according to the standard event purpose.
S206: and carrying out optimization processing on the classified events to be analyzed to obtain valuable events.
Specifically, the big data analysis means that an event to be analyzed is obtained, then the event to be analyzed is classified according to the purpose of a standard event, for example, the vehicle speed and the engine speed can both represent the current running time length of the vehicle and the state of the vehicle, so the vehicle speed and the engine speed can be classified into one category, after the classification is completed, the cloud end finally performs the single-vehicle longitudinal data analysis (historical data and current data comparison) and the transverse data analysis (other vehicles of the same vehicle type) to obtain a valuable event, for example, the vehicle speed and the engine speed can both represent the current running time length of the vehicle and the state of the vehicle, and then one of the events can be selected. The selecting mode can comprise the steps of obtaining the number of groups corresponding to the vehicle speed and the rotating speed of the transmitter, and selecting one more group number as a final valuable event.
The standard event usage described above may be empirically derived events that may be indicative of various states of the vehicle.
S208: and acquiring data requirements corresponding to each service, and extracting the application of the requirement events contained in the data requirements.
S210: demand events corresponding to the data demand are extracted from the valuable events according to the demand event usage and the standard event usage.
Specifically, the data requirement refers to the type of data required by the service, and the like, and the service may include, but is not limited to, vehicle running state, owner life circle, analysis of UBI insurance model through driving behavior, prediction of vehicle failure, development of maintenance advice, analysis of functional use of the vehicle, provision of data support for vehicle type upgrade, evaluation of value of a second-hand vehicle according to use of the vehicle, comparison with similar vehicles, and various big data applications. The demand event application refers to the application to which an event required by a certain demand belongs; for example, the vehicle condition running state may need data representing the current running time of the vehicle, the state of the vehicle and the like, so that the required event usage required by the vehicle condition running state can be determined according to the data, the required event usage can be compared with the standard event usage to determine the events required by the service, and the cloud end can extract the required events from the valuable events.
S212: and sending the demand event to a server corresponding to the service.
Specifically, after the cloud calculates the demand events, the corresponding demand events are shared to the server corresponding to the service, so that the server can obtain valuable data by analyzing the demand events, and the service quality and the like are improved.
In the above embodiment, the received event is obtained after the vehicle terminal sorts the acquired vehicle original data, so that redundant data processing of the cloud can be reduced, the cloud classifies the data during processing, and then performs optimization processing, so that redundant data is reduced, the valuable events can be subsequently distributed according to the requirements of each service, the resource occupation of the cloud is reduced, and the data processing efficiency is improved.
Specifically, the cloud may perform data processing including several steps of detecting an abnormal event, analyzing big data, sharing information according to requirements, and the like, and sequentially analyze events to be analyzed through the several steps to filter out useless events and redundant events, so that the events finally provided to the servers corresponding to the services are the most valuable events.
The abnormal event is mainly detected to filter out the normal event, because the normal event is generally the same, the value of the abnormal event is far lower than that of the abnormal event, for example, most of data on a vehicle terminal is sent according to a period, the vehicle is normally opened for 1 hour, a vehicle speed signal has thousands of messages (10ms period), so that the data collected by the same event in most of events are the same, but if the abnormal event exists, for example, the vehicle speed is suddenly increased from 0 to 100 or suddenly decreased from 80 to 0, the abnormal event can analyze the vehicle condition and the accident, and the driving behavior. The normal data of the whole vehicle are transmitted all at all times, and if the data volume is analyzed to be too large, the normal data of the whole vehicle are transmitted all at all times. Therefore, only data with problems in analysis are valuable, normal driving data are not valuable at ordinary times, and a large amount of cloud resources are occupied.
The big data analysis is to analyze a large amount of data uploaded by a vehicle terminal longitudinally and/or transversely, classify and gather to obtain valuable data, for example, the event to be analyzed is classified according to the purpose of a standard event, so that the data in the same class is subjected to single-vehicle longitudinal data analysis and transverse data analysis of other vehicles of the same vehicle type, the single-vehicle longitudinal data analysis is to extract data with larger transformation in the longitudinal use time of the vehicle, and the analysis of other vehicles of the same vehicle is to extract whether the other vehicles have the same abnormal data change, if so, the prediction of the same type of vehicle can be performed, for example, analysis events are provided for follow-up maintenance suggestions and life circle services, and the like.
The information sharing is to extract from the valuable events according to the data requirements corresponding to each service, and the information sharing conforms to the requirement events of each service, so that the server corresponding to each service only needs to analyze the requirement events, and does not need to process a large number of events which have no value to the server. Therefore, the event amount is further reduced, the processing of the cloud is improved, multi-thread parallel sending can be performed during information sharing, for example, sending interfaces are respectively allocated to all services, so that when corresponding demand events need to be sent, the sending interfaces corresponding to the services are called to send, data sent by all the services are complementarily interfered and decoupled, and therefore one sending interface is in a problem and normal operation of other services cannot be influenced.
The following will describe in detail the steps of detecting the above abnormal event, analyzing big data, and sharing information according to the requirement:
in one embodiment, before performing big data analysis on the event to be analyzed to classify the event to be analyzed according to the standard event usage, the method further includes: extracting abnormal events from the events to be analyzed, and judging whether the abnormal events are useful events or not; when the abnormal event is useless, deleting the abnormal event from the event to be analyzed; performing big data analysis on an event to be analyzed to classify the event to be analyzed according to standard event purposes, wherein the big data analysis comprises the following steps: and carrying out big data analysis on the events to be analyzed, in which the useless events are deleted, so as to classify the events to be analyzed according to the standard event purpose.
In the embodiment, the server preferentially extracts the abnormal events during processing, so that the value of normal data is far less than that of abnormal data during the driving process of the vehicle, and redundant data is reduced through further processing.
In one embodiment, extracting an abnormal event from an event to be analyzed, and determining whether the abnormal event is a useful event includes: extracting abnormal events from the events to be analyzed, and caching the abnormal events; extracting the cached abnormal events and judging whether the abnormal events are useful events or not; performing big data analysis on an event to be analyzed to classify the event to be analyzed according to standard event purposes, wherein the big data analysis comprises the following steps: when the abnormal event is useful, generating a correction event according to a plurality of useful events; the correction events are subjected to big data analysis to classify the events to be analyzed according to standard event usage.
Specifically, the abnormal event refers to a basic event which is uploaded by the vehicle terminal and is identified with an abnormal identifier, and the abnormal event is obtained by judging after the vehicle terminal generates the basic event. In addition, the abnormal events also comprise events with larger difference obtained by comparing the events to be analyzed with the previous events of the same vehicle through the cloud end and events with larger difference with the same data of the same type of vehicles.
After the cloud end judges the part of abnormal events, whether detailed log files are needed or not, the judgment can be carried out according to whether the abnormal events occur for the first time or whether the frequency of the abnormal events is smaller than a preset value or not, for example, if the abnormal events occur for the first time or the frequency of the abnormal events far meets the analysis requirement, the cloud end needs to send log request instructions to the vehicle terminal corresponding to the abnormal events, and therefore the vehicle terminal can send the abnormal logs corresponding to the log request instructions to the cloud end, and the cloud end can carry out subsequent processing conveniently.
Optionally, when the cloud determines that the event to be analyzed is an abnormal event, the cloud may mark the part of the event to be analyzed first, then start another thread or process to obtain the corresponding log from the corresponding vehicle terminal, and after the log is successfully obtained, cache the corresponding marked abnormal event and the obtained log in association through another thread, for example, may cache the event in a pre-configured initial event memory.
The method comprises the steps that after the cloud acquires abnormal events, the abnormal events are screened and analyzed, wherein the screening and analysis can be conducted through computer autonomous analysis or manual analysis of the cloud, the analysis is mainly conducted to judge whether the abnormal events are useful events, for example, if the abnormal events are useless events, the abnormal events can be directly deleted, so that the data quantity of follow-up analysis can be reduced, if the abnormal events are useful events, whether the useful events need to be subjected to secondary processing can be judged, for example, a plurality of useful events generate correction events, and if the abnormal events are useful events, the cloud acquires corresponding useful events and then generates corresponding correction events.
The cloud end judges whether the abnormal event is a useful event or not, and can judge whether other equivalent data are replaced with the abnormal event or not, for example, the vehicle speed and the engine speed can both represent the current running time of the vehicle and the state of the vehicle, so only one of the abnormal event needs to be reserved, or for example, the current position information of the vehicle, because the vehicle speed is known, the abnormal event can be acquired only once in 1 minute, but the acquisition time is 1 uploading in 1 second, at this time, the data acquired in 1 minute every interval is a useless event, the data in 1 minute interval is a useful event, and in order to acquire the track of the vehicle, the track of the vehicle can be generated according to the current position information of the vehicle acquired in 1 minute, namely the correction event.
The cloud end can judge whether the abnormal event is a useful event or not, and also can judge the occurrence frequency and the occurrence range of the abnormal event, for example, whether the abnormal event occurs randomly or mostly in vehicles of the same type, for example, if the abnormal event occurs mostly in vehicles of the same type, that is, the occurrence range is larger than a preset range, the abnormal event can be judged to be valuable.
In other embodiments, the cloud determines whether the abnormal event is a useful event, and the abnormal event may be processed according to a configured rule of a user or machine learning, which is not limited herein.
In the embodiment, the server preferentially extracts the abnormal events during processing, so that the value of normal data is far less than that of abnormal data during the driving process of the vehicle, and redundant data is reduced through further processing. And because the physical meanings represented by some abnormal events are the same, the abnormal events may only need one, and the server deletes other abnormal events, so that the subsequent processing data is reduced, the resource occupation of the server is reduced, and the data processing efficiency is improved.
In one embodiment, extracting abnormal events from the events to be analyzed includes: extracting historical vehicle data corresponding to the event to be analyzed, and extracting a historical event corresponding to the event to be analyzed from the historical vehicle data; judging whether the event to be analyzed changes according to the historical event; and when the event to be analyzed changes, extracting the changed event to be analyzed as an abnormal event.
In the embodiment, when judging whether the event to be analyzed is an abnormal event, longitudinal comparison can be performed, that is, data of changes of the vehicle in the driving process is determined through a longitudinal global view angle, and the data is used as abnormal data to perform subsequent analysis and processing, so that redundant data is reduced, resource occupation of a server is reduced, and data processing efficiency is improved.
In one embodiment, extracting abnormal events from the events to be analyzed includes: extracting the vehicle type of the vehicle corresponding to the event to be analyzed; acquiring a first vehicle event which is uploaded by a vehicle with the same type as the vehicle type and has the same type as the event to be analyzed; judging whether a first difference value between the event to be analyzed and the first vehicle event is smaller than a first preset value or not; and when the first difference value is not smaller than the first preset value, extracting the event to be analyzed as an abnormal event.
In the above embodiment, when it is determined whether an event to be analyzed is an abnormal event, a transverse comparison may be performed, that is, whether corresponding data of the same vehicle type are consistent is determined through a transverse global view, and the inconsistent data is used as abnormal data to perform subsequent analysis processing, so that redundant data is reduced, resource occupation of a server is reduced, and data processing efficiency is improved.
Specifically, when judging whether the event to be analyzed is an abnormal event, the cloud end may determine, through a longitudinal global view, data of changes of the vehicle in the driving process and determine, through a transverse global view, whether corresponding data of the same vehicle type are consistent, so that the abnormal event may be accurately extracted.
The cloud end can acquire an event to be analyzed, then acquire an identifier of a vehicle corresponding to the event to be analyzed (which can uniquely determine a certain vehicle), acquire corresponding historical vehicle data according to the identifier of the vehicle, and extract the historical event corresponding to the event to be analyzed from the historical vehicle data, so that the cloud end judges whether the event to be analyzed changes according to the historical event, for example, if the original vehicle speed is normal and the vehicle speed suddenly changes, the event to be analyzed with the changed vehicle speed is an abnormal event.
If the fact that the events to be analyzed are consistent in the driving process of the vehicle is determined through the overall perspective, the cloud end can continuously determine whether the corresponding data of the same vehicle type are consistent through the overall perspective, for example, a first vehicle event which is uploaded by the same vehicle and has the same type as the events to be analyzed can be obtained, and whether a first difference value between the events to be analyzed and the first vehicle event is smaller than a first preset value or not is judged; and when the first difference value is not smaller than the first preset value, extracting the event to be analyzed as an abnormal event. Further, the horizontal and vertical determinations may be performed in serial processing or in parallel processing, and the efficiency of processing may be improved by the parallel processing.
In one embodiment, the optimizing the classified events to be analyzed to obtain the valuable events includes: longitudinally calculating a second difference value of the historical events corresponding to the vehicle terminal of the events to be analyzed in each classification; and acquiring the event to be analyzed corresponding to the maximum second difference value as a valuable event.
In the embodiment, the redundant events are deleted through longitudinal judgment, so that the resource occupation of the server is reduced, and the data processing efficiency is improved.
In one embodiment, before optimizing the classified events to be analyzed to obtain the valuable events, the method further includes: transversely acquiring a second vehicle event which is the same as the vehicle type of the vehicle terminal and corresponds to the event to be analyzed; and when the third difference value between the event to be analyzed and the second vehicle event is smaller than the third preset value, deleting the event to be analyzed.
In the embodiment, the redundant events are deleted through transverse judgment, so that the resource occupation of the server is reduced, and the data processing efficiency is improved.
In the above embodiment, the valuable data is obtained through big data analysis, specifically, the method includes vertical comparison to remove redundant events, and horizontal comparison to remove redundant events. The longitudinal comparison and redundant event removal mainly comprises the step of longitudinally calculating a second difference value of the historical events corresponding to the vehicle terminal of the events to be analyzed in each category, and since one category represents one running state of the vehicle, in order to obviously identify the running state, the cloud end can obtain the event to be analyzed corresponding to the largest second difference value as a valuable event, so that other events to be analyzed with fewer second difference values can be deleted, and only one event is reserved, so that the subsequent analysis is facilitated.
The transverse analysis mainly comprises the steps that the cloud transversely acquires a second vehicle event which is the same as the vehicle type of the vehicle terminal and corresponds to the event to be analyzed; when the third difference value between the event to be analyzed and the second vehicle event is smaller than the third preset value, the event to be analyzed is deleted, that is, when the events to be analyzed of all vehicles of the same type are basically consistent, the events to be analyzed are useless, because the events to be analyzed are the same, the events to be analyzed are deleted by the cloud, and only the events with the third difference value larger than or equal to the third preset value are reserved, so that the difference of all vehicles of the same vehicle type can be obtained. Optionally, if the abnormal events of the vehicles in the same vehicle type are basically consistent, the abnormal events are not valuable, and only one abnormal event needs to be analyzed, so that the subsequent maintenance is facilitated.
In the embodiment, the redundant events are deleted through transverse judgment and longitudinal judgment, so that the resource occupation of the server is reduced, and the data processing efficiency is improved.
Specifically, referring to fig. 3, fig. 3 is a flowchart of a vehicle data processing method according to another embodiment.
The vehicle data processing method provided by the embodiment is similar to a water purifier device and is divided into four layers: event collection, abnormal event analysis, big data analysis and information analysis. The method comprises the steps that firstly, an event acquisition layer is responsible for collecting data uploaded by all vehicles, recording the data into a database, simultaneously conducting pre-inspection when the data are stored, conducting data inspection according to transverse and longitudinal strategies, judging that the event data are abnormal events if the event data are changed from the previous event data or have great difference from the same data of the same type of vehicles, marking the abnormal events, sending a log uploading request to a vehicle terminal, storing and marking the uploaded logs, and storing and marking the uploaded logs into an initial event storage.
And secondly, the abnormal event analysis layer screens and analyzes the abnormal events, can be automatically analyzed by a cloud computer or manually analyzed, judges whether the abnormal events are valuable events or not, and whether an event acquisition item of the terminal is necessary to be added or neglected, designs a strategy for capturing the events by the terminal, and whether logs are required to be uploaded or not for the same subsequent events (the frequency of log uploading is reduced), so that a new event item is formed and is classified into a corrected event memory.
Thirdly, the big data analysis layer takes the event from the 'correction event memory', performs 'single-vehicle longitudinal data analysis' (comparing historical data with current data), performs transverse data analysis (other vehicles of the same vehicle type), performs 'classified summarization' of the event, and saves the event to a 'valuable database'.
Finally, the valuable data essence which is extracted layer by layer is analyzed by the information analysis layer, the data value can be found according to various uses of the information, such as the vehicle running state and the life circle of a vehicle owner, the UBI insurance model is analyzed through the driving behavior, the maintenance suggestion is proposed for predicting the vehicle fault, the functional use condition of the vehicle is analyzed, the data support of vehicle type upgrading is provided, and the value of a second-hand vehicle is evaluated according to the use condition of the vehicle and the comparison of the same vehicle, and the like.
In the above embodiment, the received event is obtained after the vehicle terminal sorts the acquired vehicle original data, so that redundant data processing of the cloud can be reduced, the cloud classifies the data during processing, and then performs optimization processing, so that redundant data is reduced, the valuable events can be subsequently distributed according to the requirements of each service, the resource occupation of the cloud is reduced, and the data processing efficiency is improved.
It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 4, there is provided a vehicle data processing apparatus including: the system comprises a receiving module 100, a classifying module 200, an optimizing module 300, a requirement obtaining module 400, an event extracting module 500 and a sending module 600, wherein:
the system comprises a receiving module 100, a processing module and a processing module, wherein the receiving module 100 is used for receiving an event to be analyzed sent by a vehicle terminal, and the event to be analyzed is obtained after the vehicle terminal sorts acquired vehicle original data;
the classification module 200 is used for performing big data analysis on the event to be analyzed so as to classify the event to be analyzed according to the standard event purpose;
the optimizing module 300 is configured to perform optimization processing on the classified events to be analyzed to obtain valuable events;
a requirement obtaining module 400, configured to obtain a data requirement corresponding to each service, and extract a requirement event purpose included in the data requirement;
an event extraction module 500, configured to extract a demand event corresponding to a data demand from the valuable events according to the demand event usage and the standard event usage;
a sending module 600, configured to send the demand event to a server corresponding to the service.
In one embodiment, the vehicle data processing apparatus may further include:
an abnormal event extraction module 500, configured to extract an abnormal event from an event to be analyzed, and determine whether the abnormal event is a useful event;
the deleting module is used for deleting the abnormal event from the event to be analyzed when the abnormal event is useless;
the classification module 200 is further configured to perform big data analysis on the event to be analyzed from which the useless event is deleted, so as to classify the event to be analyzed according to the standard event usage.
In one embodiment, the above abnormal event extracting module 500 includes:
the device comprises a first extraction unit, a second extraction unit and a third extraction unit, wherein the first extraction unit is used for extracting historical vehicle data corresponding to an event to be analyzed and extracting a historical event corresponding to the event to be analyzed from the historical vehicle data;
the first judgment unit is used for judging whether the event to be analyzed changes or not according to the historical event;
and the second extraction unit is used for extracting the changed event to be analyzed as an abnormal event when the event to be analyzed changes.
In one embodiment, the above abnormal event extracting module 500 may further include:
the third extraction unit is used for extracting the vehicle type of the vehicle corresponding to the event to be analyzed;
the data acquisition unit is used for acquiring a first vehicle event which is uploaded by a vehicle of the same type as the vehicle type and is of the same type as the event to be analyzed;
the second judging unit is used for judging whether a first difference value between the event to be analyzed and the first vehicle event is smaller than a first preset value or not;
and the fourth extraction unit is used for extracting the event to be analyzed as an abnormal event when the first difference value is not less than the first preset value.
In one embodiment, the above abnormal event extracting module 500 may include:
the cache unit is used for extracting the abnormal events from the events to be analyzed and caching the abnormal events;
the third judging unit is used for extracting the cached abnormal events and judging whether the abnormal events are useful events or not;
the classification module 200 described above may include:
the event generating unit is used for generating a correction event according to a plurality of useful events when the abnormal event is useful;
and the classification unit is used for performing big data analysis on the correction event so as to classify the event to be analyzed according to the standard event purpose.
In one embodiment, the optimization module 300400 includes:
a longitudinal calculation unit for longitudinally calculating a second difference value of the historical events corresponding to the vehicle terminal for the correction event in each category;
and the fifth extraction unit is used for acquiring the correction event corresponding to the largest second difference value as the valuable event.
In one embodiment, the vehicle data processing apparatus further includes:
the transverse processing module is used for transversely acquiring a second vehicle event which is the same as the vehicle type of the vehicle terminal and corresponds to the correction event;
and the deleting module is used for deleting the correction event when a third difference value between the correction event and the second vehicle event is smaller than a third preset value.
For specific limitations of the vehicle data processing device, reference may be made to the above limitations of the vehicle data processing method, which are not described herein again. The respective modules in the above-described vehicle data processing apparatus may be entirely or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, and the computer device may be a cloud, and an internal structure diagram of the computer device may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as events to be analyzed. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a vehicle data processing method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: receiving an event to be analyzed sent by a vehicle terminal, wherein the event to be analyzed is obtained by the vehicle terminal after event sorting of the acquired vehicle original data; performing big data analysis on the event to be analyzed so as to classify the event to be analyzed according to the standard event purpose; carrying out optimization processing on the classified events to be analyzed to obtain valuable events; acquiring data requirements corresponding to each service, and extracting the application of a requirement event contained in the data requirements; extracting demand events corresponding to the data demands from the valuable events according to the demand event usage and the standard event usage; and sending the demand event to a server corresponding to the service.
In one embodiment, before the big data analysis of the event to be analyzed implemented when the processor executes the computer program to classify the event to be analyzed according to the standard event usage, the method further comprises: extracting abnormal events from the events to be analyzed, and judging whether the abnormal events are useful events or not; when the abnormal event is useless, deleting the abnormal event from the event to be analyzed; performing big data analysis on an event to be analyzed to classify the event to be analyzed according to standard event purposes, wherein the big data analysis comprises the following steps: and carrying out big data analysis on the events to be analyzed, in which the useless events are deleted, so as to classify the events to be analyzed according to the standard event purpose.
In one embodiment, the extraction of abnormal events from events to be analyzed, as implemented by a processor executing a computer program, comprises: extracting historical vehicle data corresponding to the event to be analyzed, and extracting a historical event corresponding to the event to be analyzed from the historical vehicle data; judging whether the event to be analyzed changes according to the historical event; and when the event to be analyzed changes, extracting the changed event to be analyzed as an abnormal event.
In one embodiment, the extraction of abnormal events from events to be analyzed, as implemented by a processor executing a computer program, comprises: extracting the vehicle type of the vehicle corresponding to the event to be analyzed; acquiring a first vehicle event which is uploaded by a vehicle with the same type as the vehicle type and has the same type as the event to be analyzed; judging whether a first difference value between the event to be analyzed and the first vehicle event is smaller than a first preset value or not; and when the first difference value is not smaller than the first preset value, extracting the event to be analyzed as an abnormal event.
In one embodiment, the extracting abnormal events from the events to be analyzed and determining whether the abnormal events are useful events, which is realized when the processor executes the computer program, comprises: extracting abnormal events from the events to be analyzed, and caching the abnormal events; extracting the cached abnormal events and judging whether the abnormal events are useful events or not; the big data analysis of the event to be analyzed realized when the processor executes the computer program so as to classify the event to be analyzed according to the standard event purpose, comprising the following steps: when the abnormal event is useful, generating a correction event according to a plurality of useful events; the correction events are subjected to big data analysis to classify the events to be analyzed according to standard event usage.
In one embodiment, the optimizing the classified events to be analyzed, which is performed when the processor executes the computer program, obtains the valuable events, and includes: longitudinally calculating a second difference value of the historical events corresponding to the vehicle terminal of the events to be analyzed in each classification; and acquiring the event to be analyzed corresponding to the maximum second difference value as a valuable event.
In one embodiment, before the processor performs optimization processing on the classified events to be analyzed when executing the computer program to obtain the valuable events, the method further includes: transversely acquiring a second vehicle event which is the same as the vehicle type of the vehicle terminal and corresponds to the event to be analyzed; and when the third difference value between the event to be analyzed and the second vehicle event is smaller than the third preset value, deleting the event to be analyzed.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: receiving an event to be analyzed sent by a vehicle terminal, wherein the event to be analyzed is obtained by the vehicle terminal after event sorting of the acquired vehicle original data; performing big data analysis on the event to be analyzed so as to classify the event to be analyzed according to the standard event purpose; carrying out optimization processing on the classified events to be analyzed to obtain valuable events; acquiring data requirements corresponding to each service, and extracting the application of a requirement event contained in the data requirements; extracting demand events corresponding to the data demands from the valuable events according to the demand event usage and the standard event usage; and sending the demand event to a server corresponding to the service.
In one embodiment, before the big data analysis of the event to be analyzed for classifying the event to be analyzed according to the standard event usage, the computer program when executed by the processor further comprises: extracting abnormal events from the events to be analyzed, and judging whether the abnormal events are useful events or not; when the abnormal event is useless, deleting the abnormal event from the event to be analyzed; performing big data analysis on an event to be analyzed to classify the event to be analyzed according to standard event purposes, wherein the big data analysis comprises the following steps: and carrying out big data analysis on the events to be analyzed, in which the useless events are deleted, so as to classify the events to be analyzed according to the standard event purpose.
In one embodiment, the extraction of abnormal events from events to be analyzed, as implemented by a computer program when executed by a processor, comprises: extracting historical vehicle data corresponding to the event to be analyzed, and extracting a historical event corresponding to the event to be analyzed from the historical vehicle data; judging whether the event to be analyzed changes according to the historical event; and when the event to be analyzed changes, extracting the changed event to be analyzed as an abnormal event.
In one embodiment, the extraction of abnormal events from events to be analyzed, as implemented by a computer program when executed by a processor, comprises: extracting the vehicle type of the vehicle corresponding to the event to be analyzed; acquiring a first vehicle event which is uploaded by a vehicle with the same type as the vehicle type and has the same type as the event to be analyzed; judging whether a first difference value between the event to be analyzed and the first vehicle event is smaller than a first preset value or not; and when the first difference value is not smaller than the first preset value, extracting the event to be analyzed as an abnormal event.
In one embodiment, the extracting of abnormal events from events to be analyzed and determining whether the abnormal events are useful events, implemented when the computer program is executed by the processor, includes: extracting abnormal events from the events to be analyzed, and caching the abnormal events; extracting the cached abnormal events and judging whether the abnormal events are useful events or not; the big data analysis of the event to be analyzed realized when the processor executes the computer program so as to classify the event to be analyzed according to the standard event purpose, comprising the following steps: when the abnormal event is useful, generating a correction event according to a plurality of useful events; the correction events are subjected to big data analysis to classify the events to be analyzed according to standard event usage.
In one embodiment, the optimizing the classified events to be analyzed, which is performed by the computer program when the computer program is executed by the processor, obtains the valuable events, and includes: longitudinally calculating a second difference value of the historical events corresponding to the vehicle terminal of the events to be analyzed in each classification; and acquiring the event to be analyzed corresponding to the maximum second difference value as a valuable event.
In one embodiment, before the categorized event to be analyzed is optimized to obtain the valuable event when the computer program is executed by the processor, the method further includes: transversely acquiring a second vehicle event which is the same as the vehicle type of the vehicle terminal and corresponds to the event to be analyzed; and when the third difference value between the event to be analyzed and the second vehicle event is smaller than the third preset value, deleting the event to be analyzed.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A vehicle data processing method, the method comprising:
receiving an event to be analyzed sent by a vehicle terminal, wherein the event to be analyzed is obtained after the vehicle terminal sorts the acquired vehicle original data;
performing big data analysis on the event to be analyzed so as to classify the event to be analyzed according to standard event purposes;
carrying out optimization processing on the classified events to be analyzed to obtain valuable events;
acquiring data requirements corresponding to each service, and extracting the application of a requirement event contained in the data requirements;
extracting demand events corresponding to the data demands from the valuable events according to the demand event usages and the standard event usages;
and sending the demand event to a server corresponding to the service.
2. The method of claim 1, wherein prior to the big data analysis of the event to be analyzed to classify the event to be analyzed by standard event usage, further comprising:
extracting abnormal events from the events to be analyzed, and judging whether the abnormal events are useful events or not;
when the abnormal event is useless, deleting the abnormal event from the event to be analyzed;
the big data analysis of the event to be analyzed is performed to classify the event to be analyzed according to standard event usage, and the big data analysis comprises the following steps:
and carrying out big data analysis on the events to be analyzed, in which the useless events are deleted, so as to classify the events to be analyzed according to the standard event usage.
3. The method according to claim 2, wherein the extracting abnormal events from the events to be analyzed comprises:
extracting historical vehicle data corresponding to the event to be analyzed, and extracting a historical event corresponding to the event to be analyzed from the historical vehicle data;
judging whether the event to be analyzed changes or not according to the historical event;
and when the event to be analyzed changes, extracting the changed event to be analyzed as an abnormal event.
4. The method according to claim 2, wherein the extracting abnormal events from the events to be analyzed comprises:
extracting the vehicle type of the vehicle corresponding to the event to be analyzed;
acquiring a first vehicle event which is uploaded by a vehicle of the same type as the vehicle model and is of the same type as the event to be analyzed;
judging whether a first difference value between the event to be analyzed and the first vehicle event is smaller than a first preset value or not;
and when the first difference value is not smaller than a first preset value, extracting the event to be analyzed as an abnormal event.
5. The method according to any one of claims 2 to 4, wherein the extracting abnormal events from the events to be analyzed and determining whether the abnormal events are useful events comprises:
extracting abnormal events from the events to be analyzed, and caching the abnormal events;
extracting the cached abnormal events and judging whether the abnormal events are useful events or not;
the big data analysis of the event to be analyzed is performed to classify the event to be analyzed according to standard event usage, and the big data analysis comprises the following steps:
when the abnormal event is useful, generating a correction event according to a plurality of useful events;
performing big data analysis on the correction event to classify the event to be analyzed according to standard event usage.
6. The method according to claim 1, wherein the optimizing the classified events to be analyzed to obtain the valuable events comprises:
longitudinally calculating a second difference value of the historical events corresponding to the vehicle terminal of the events to be analyzed in each classification;
and acquiring the event to be analyzed corresponding to the maximum second difference value as a valuable event.
7. The method according to claim 1, wherein before optimizing the classified events to be analyzed to obtain the valuable events, the method further comprises:
transversely acquiring a second vehicle event which is the same as the vehicle type of the vehicle terminal and corresponds to the event to be analyzed;
and when a third difference value between the event to be analyzed and the second vehicle event is smaller than a third preset value, deleting the event to be analyzed.
8. A vehicular data processing apparatus characterized by comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving an event to be analyzed sent by a vehicle terminal, and the event to be analyzed is obtained after the vehicle terminal sorts the acquired vehicle original data;
the classification module is used for carrying out big data analysis on the event to be analyzed so as to classify the event to be analyzed according to the purpose of a standard event;
the optimization module is used for optimizing the classified events to be analyzed to obtain valuable events;
the demand acquisition module is used for acquiring data demands corresponding to each service and extracting demand event purposes contained in the data demands;
the event extraction module is used for extracting a demand event corresponding to the data demand from the valuable events according to the demand event usage and the standard event usage;
and the sending module is used for sending the demand event to a server corresponding to the service.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN114689159A (en) * | 2022-03-18 | 2022-07-01 | 西安拓米网络科技有限公司 | System, method, electronic device, and medium for metering device data management |
| WO2022199581A1 (en) * | 2021-03-26 | 2022-09-29 | 华为技术有限公司 | Storage method and device |
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| WO2022199581A1 (en) * | 2021-03-26 | 2022-09-29 | 华为技术有限公司 | Storage method and device |
| CN114689159A (en) * | 2022-03-18 | 2022-07-01 | 西安拓米网络科技有限公司 | System, method, electronic device, and medium for metering device data management |
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