CN114547140A - Behavior sequence generation method and device, storage medium and electronic device - Google Patents
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Abstract
The invention provides a method and a device for generating a behavior sequence, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring data records of a target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events; determining the action execution sequence of the plurality of operation events according to the sequence of the action execution times of the plurality of operation events; and determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events which are continuously executed by the target object within a preset time period. The method solves the problems that in the related technology, any length of behavior sequence mining cannot be carried out in original data to determine the operation behaviors of a target object on different devices and the like.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method and an apparatus for generating a behavior sequence, a storage medium, and an electronic apparatus.
Background
With the application of the intelligent home system, the recommendation system based on the intelligent home is applied more and more. In order to apply various artificial intelligence recommendation algorithms and data mining requirements, an algorithm is required to process original single scattered user behavior data so as to meet the requirements of more complex algorithms and data mining.
The intelligent home user behavior data has the characteristics of zero dispersion and dispersibility. For the intelligent home user, after the user gives an instruction to one network device, the user gives an instruction to another network device. The user needs to operate a plurality of network devices each time to achieve an objective, and the current technology records the instruction of each network device independently, but in fact, the user is used to operate a plurality of network devices each time, and in the existing data mining, some ordered and meaningful sequences are often selected, and the user behavior sequence mining with any length in the original data of the smart home cannot be realized.
Aiming at the problems that any length of behavior sequence mining cannot be carried out in original data to determine the operation behaviors of a target object on different devices and the like in the related technology, an effective technical scheme is not provided.
Disclosure of Invention
The embodiment of the invention provides a method and a device for generating a behavior sequence, a storage medium and an electronic device, which are used for at least solving the problems that the behavior sequence mining with any length can not be carried out in original data in the related technology so as to determine the operation behaviors of a target object on different devices and the like.
According to an embodiment of the present invention, there is provided a method for generating a behavior sequence, including: acquiring data records of a target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events; determining the action execution sequence of the plurality of operation events according to the sequence of the action execution times of the plurality of operation events; and determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period.
In one exemplary embodiment, determining a sequence of behaviors of a target object according to a time interval includes: acquiring the time interval of every two adjacent operation events to obtain a plurality of time intervals; for each time interval in the multiple time intervals, determining the size relationship between each time interval and a preset interval threshold, and determining a first tag value of a first characteristic tag and a second tag value of a second characteristic tag of an operation event according to the size relationship; and determining the behavior sequence of the target object according to the first label value and the second label value.
In one exemplary embodiment, determining a first tag value that is a feature tag of an operational event according to a magnitude relationship includes: under the condition that each time interval is larger than a preset interval threshold value, determining that a first label value of a feature label of an operation event with a later action execution time in every two adjacent operation events is an invalid value; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the first label value of the feature label of the operation event with the action execution time after in each two adjacent operation events is a valid value.
In one exemplary embodiment, determining a second tag value that is a second feature tag of the operational event according to the magnitude relationship includes: under the condition that each time interval is larger than a preset interval threshold, determining that a second label value of a feature label of an operation event with a previous action execution time in each two adjacent operation events is an invalid value; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the second label value of the characteristic label of the operation event with the action execution time before in each two adjacent operation events is a valid value.
In one exemplary embodiment, determining the behavior sequence of the target object according to the first tag value and the second tag value includes: combining the first tag value and the second tag value corresponding to each operation event to obtain a data item of the operation event; determining the execution sequence of each operation event in the behavior sequence according to the arrangement sequence of the first tag value and the second tag value in the data item, and identifying each operation event; and determining the behavior sequence of the target object according to the identification result.
In an exemplary embodiment, determining an execution order of each operation event in the action sequence according to an arrangement order of the first tag value and the second tag value in the data item, and identifying each operation event includes: under the condition that the data items are arranged by invalid values and valid values, marking a starting identifier on the current operation event, and determining that the current operation event is a starting action behavior; under the condition that the data items are the effective values and the effective value arrangement, marking a current operation event with an identification, and determining the current operation event as an action-in-progress behavior; and under the condition that the data items are arranged by the effective values and the invalid values, marking an ending mark on the current operation event, and determining that the current operation event is an action ending behavior.
In one exemplary embodiment, determining the behavior sequence of the target object according to the identified result includes: recording the identification result of each operation event by using a target field; matching the identification result with the data records of different equipment obtained by the target object; and extracting the identification result and the action execution time corresponding to the operation event by using a preset data structure to obtain a behavior sequence corresponding to the operation event sequence for representing that the target object executes different devices.
According to another embodiment of the present invention, there is provided a behavior sequence generation apparatus including: the acquisition module is used for acquiring data records of a target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events; the sequence module is used for determining the action execution sequence of the operation events according to the sequence of the action execution times of the operation events; and the determining module is used for determining the time interval of every two adjacent operation events according to the action execution sequence and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period.
In an exemplary embodiment, the determining module is further configured to obtain a time interval between every two adjacent operation events, so as to obtain a plurality of time intervals; for each time interval in the multiple time intervals, determining the size relationship between each time interval and a preset interval threshold, and determining a first tag value of a first characteristic tag and a second tag value of a second characteristic tag of an operation event according to the size relationship; and determining the behavior sequence of the target object according to the first label value and the second label value.
In an exemplary embodiment, the determining module further includes: the first determining unit is used for determining that the first label value of the feature label of the operation event with the action execution time after the action execution time in every two adjacent operation events is an invalid value under the condition that each time interval is larger than a preset interval threshold value; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the first label value of the feature label of the operation event with the action execution time after in each two adjacent operation events is a valid value.
In an exemplary embodiment, the determining module further includes: the second determining unit is used for determining that a second label value of a feature label of an operation event with a previous action execution time in every two adjacent operation events is an invalid value under the condition that each time interval is larger than a preset interval threshold; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the second label value of the characteristic label of the operation event with the action execution time before in each two adjacent operation events is a valid value.
In an exemplary embodiment, the determining module further includes: the identification unit is used for combining the first label value and the second label value corresponding to each operation event to obtain a data item of the operation event; determining the execution sequence of each operation event in the behavior sequence according to the arrangement sequence of the first tag value and the second tag value in the data item, and identifying each operation event; and determining the behavior sequence of the target object according to the identification result.
In an exemplary embodiment, the identification unit is further configured to, in a case that the data item is an arrangement of an invalid value and a valid value, mark a start identifier on the current operation event, and determine that the current operation event is a start action behavior; under the condition that the data items are the effective values and the effective value arrangement, marking a current operation event with an identification, and determining the current operation event as an action-in-progress behavior; and under the condition that the data items are arranged by the effective values and the invalid values, marking an ending mark on the current operation event, and determining that the current operation event is an action ending behavior.
In an exemplary embodiment, the identification unit is further configured to record an identification result of each operation event by using the target field; matching the identification result with the data records of different equipment obtained by the target object; and extracting the identification result and the action execution time corresponding to the operation event by using a preset data structure to obtain a behavior sequence corresponding to the operation event sequence for representing that the target object executes different devices.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
By the invention, data records of the target object to different devices are obtained, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events; determining the action execution sequence of the plurality of operation events according to the sequence of the action execution times of the plurality of operation events; determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period, that is, by extracting the behavior sequence corresponding to the data record of the target object to different devices, and further realizing the processing and mining of the original data under the condition of no special data requirement, therefore, the problems that the behavior sequence mining with any length cannot be carried out in the original data in the prior art so as to determine the operation behavior of the target object to different devices and the like can be solved, and further the behavior sequence belonging to the target object can be generated according to the data record of the target object to different devices, and the behavior habit of the target object can be better predicted by using a knowledge graph algorithm based on the behavior sequence, therefore, the operation experience of the target object on the intelligent equipment can be improved, and the first step from the original data to various machine learning algorithms and artificial intelligence algorithms becomes more convenient, efficient and accurate.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware structure of a cloud of a behavior sequence generation method according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a method of generating a sequence of behaviors in accordance with an embodiment of the present invention;
FIG. 3 is a timing diagram of a sequence of data processing determination actions by a data structure in accordance with an alternative embodiment of the present invention;
FIG. 4 is a schematic diagram of a user time sequence of actions in accordance with an alternative embodiment of the present invention;
FIG. 5 is a schematic illustration of calculating a time interval between any two consecutive actions of a user according to an alternative embodiment of the present invention;
FIG. 6 is a block diagram showing values of feature values after a calculation time interval in accordance with an alternative embodiment of the present invention;
FIG. 7 is a diagram of a result of a calculation corresponding to a sequence of user time actions for which a value of a feature value is given after a time interval has been calculated, in accordance with an alternative embodiment of the present invention;
FIG. 8 is a graphical illustration of the results of a calculation corresponding to a sequence of user time actions for which values of feature values are given after a time interval has been calculated, in accordance with an alternative embodiment of the present invention;
FIG. 9 is a diagram illustrating a state of a time sequence of actions of a user according to an alternative embodiment of the present invention;
FIG. 10 is a flowchart of a data processing determining behavior sequence according to a data structure in accordance with an alternative embodiment of the present invention;
fig. 11 is a block diagram of a configuration of a behavior sequence generation apparatus according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method provided by the embodiment of the application can be executed in a computer terminal or a cloud-end similar computing device. Taking operation on a cloud end as an example, fig. 1 is a block diagram of a hardware structure of a cloud end of a behavior sequence generation method according to an embodiment of the present invention. As shown in fig. 1, the cloud may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, and in an exemplary embodiment, the cloud may further include a transmission device 106 for communication function and an input/output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is merely an illustration, and is not intended to limit the structure of the cloud end. For example, the cloud may also include more or fewer components than shown in fig. 1, or have a different configuration with equivalent functionality to that shown in fig. 1 or more functionality than that shown in fig. 1.
The memory 104 may be used to store a computer program, for example, a software program of an application software and a module, such as a computer program corresponding to the behavior sequence generation method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the cloud over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider in the cloud. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, a method for generating a behavior sequence is provided, and fig. 2 is a flowchart of the method for generating a behavior sequence according to the embodiment of the present invention, where the flowchart includes the following steps:
step S202, acquiring data records of a target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events;
for example, different target objects have different behavior characteristics, and before processing the data records, the data records of different devices corresponding to different target objects can be distinguished through object identifiers existing in the original data.
Step S204, determining the action execution sequence of the operation events according to the sequence of the action execution times of the operation events;
that is to say, in order to facilitate the processing of the subsequent behavior sequence, after the data records of any one target object for different devices are obtained, the plurality of operation events are time-sequenced according to the action execution time corresponding to the operation event carried in the data records, and the action execution sequence executed by the target object for each operation event is determined.
Step S206, determining a time interval between every two adjacent operation events according to the action execution sequence, and determining a behavior sequence of the target object according to the time interval, where the behavior sequence is used to indicate a plurality of operation events that are continuously executed by the target object within a preset time period.
Through the steps, data records of the target object to different devices are obtained, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events; determining the action execution sequence of the plurality of operation events according to the sequence of the action execution times of the plurality of operation events; determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period, that is, by extracting the behavior sequence corresponding to the data record of the target object to different devices, and further realizing the processing and mining of the original data under the condition of no special data requirement, therefore, the problems that the behavior sequence mining with any length cannot be carried out in the original data in the prior art so as to determine the operation behavior of the target object to different devices and the like can be solved, and further the behavior sequence belonging to the target object can be generated according to the data record of the target object to different devices, and the behavior habit of the target object can be better predicted by using a knowledge graph algorithm based on the behavior sequence, therefore, the operation experience of the target object on the intelligent equipment can be improved, and the first step from the original data to various machine learning algorithms and artificial intelligence algorithms becomes more convenient, efficient and accurate.
For example, the above processing procedure for data recording includes the following steps: acquiring original data records of different devices from an intelligent home system; analyzing the original data record, and extracting the actual action (namely, the operation event) of different objects using different equipment and the action occurrence time of the operation instruction corresponding to the operation event sent by different objects executed by different equipment, wherein the actual action is existed in the original data record; and determining action time sequences among different actions according to the actual action and the action occurrence time, and further determining time intervals among adjacent actions so as to calculate behavior sequence data corresponding to different target objects in the intelligent home system.
In one exemplary embodiment, determining a sequence of behaviors of a target object according to a time interval includes: acquiring the time interval of every two adjacent operation events to obtain a plurality of time intervals; for each time interval in the multiple time intervals, determining the size relationship between each time interval and a preset interval threshold, and determining a first tag value of a first characteristic tag and a second tag value of a second characteristic tag of an operation event according to the size relationship; and determining the behavior sequence of the target object according to the first label value and the second label value.
In brief, in order to ensure the continuous relationship between different operation events, therefore, it is necessary to determine a time interval between every two adjacent operation events, to determine whether there is continuity between every two adjacent operation events by combining a preset interval threshold, and determine a first tag value of a first feature tag and a second tag value of a second feature tag corresponding to an operation event according to a size relationship between each time interval and the preset interval threshold, and further determine a behavior sequence of a target object by using the first tag value and the second tag value as attribute features of the target object.
In one exemplary embodiment, determining a first tag value that is a feature tag of an operational event according to a magnitude relationship includes: under the condition that each time interval is larger than a preset interval threshold, determining that a first label value of a feature label of an operation event with a later action execution time in each two adjacent operation events is an invalid value; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the first label value of the feature label of the operation event with the action execution time after in each two adjacent operation events is a valid value.
For example, if the interval between two actions exceeds 5 minutes, it is considered that the actions are not continuous, and when each time interval is less than or equal to 5 minutes, it indicates that the action corresponding to the operation event is in the middle of one action sequence and the first tag value of the feature tag is recorded as a valid value (e.g., 1), and if the interval is greater than 5 minutes, it indicates that two operation events are not continuous, the first tag value of the feature tag is recorded as an invalid value (e.g., 0).
In one exemplary embodiment, determining a second tag value that is a second feature tag of the operational event according to the magnitude relationship includes: under the condition that each time interval is larger than a preset interval threshold, determining that a second label value of a feature label of an operation event with a previous action execution time in each two adjacent operation events is an invalid value; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the second label value of the characteristic label of the operation event with the action execution time before in each two adjacent operation events is a valid value.
It should be noted that the second index value is a feature tag value corresponding to an operation event subsequent to the operation event and an operation event previous to the operation event, which is calculated on the basis of determining the first tag value.
In one exemplary embodiment, determining the behavior sequence of the target object according to the first tag value and the second tag value includes: combining the first tag value and the second tag value corresponding to each operation event to obtain a data item of the operation event; determining the execution sequence of each operation event in the behavior sequence according to the arrangement sequence of the first tag value and the second tag value in the data item, and identifying each operation event; and determining the behavior sequence of the target object according to the identification result.
In an exemplary embodiment, determining an execution order of each operation event in the action sequence according to an arrangement order of the first tag value and the second tag value in the data item, and identifying each operation event includes: under the condition that the data items are arranged by an invalid value and an effective value, marking a start identifier on the current operation event, and determining the current operation event as a start action behavior; under the condition that the data items are the effective values and the effective value arrangement, marking a current operation event with an identification, and determining the current operation event as an action-in-progress behavior; and under the condition that the data items are arranged by the effective values and the invalid values, marking an ending mark on the current operation event, and determining that the current operation event is an action ending behavior.
In one exemplary embodiment, determining the behavior sequence of the target object according to the identified result includes: recording the identification result of each operation event by using a target field; matching the identification result with the data records of different equipment obtained by the target object; and extracting the identification result and the action execution time corresponding to the operation event by using a preset data structure to obtain a behavior sequence corresponding to the operation event sequence for representing that the target object executes different devices.
In order to better understand the process of the method for generating the behavior sequence, the following describes a flow of the method for generating the behavior sequence with reference to two alternative embodiments.
In the optional embodiment of the invention, a method for calculating any length of behavior sequence of the intelligent household user is mainly provided, wherein each complete action sequence of the user is mined from mass data, then the user habit can be processed by using an algorithm, and the complete habit of the user is formed by cold start recommendation of all users, so that a foundation is provided for other algorithms and applications.
As an optional implementation manner, in the smart home internet of things recommendation system, a recommendation algorithm is performed, and it is implemented that a user action sequence is found, but the smart home system is different from a common shopping system and does not have the concept of session (session management or session control). The general shopping system generally uses the concept of session (implemented with multiple language frameworks and manners), records all actions of a user in a session, and performs related user behavior intention recognition according to the actions.
Optionally, a common data structure (equivalent to a data record in the embodiment of the present invention) of the internet of things gateway is as shown in table 1 below: the table records the original data of the smart home user, and records the time, the corresponding action and other attributes of different users operating different devices at different times. In the table, the header is a user ID, a device ID, an action occurrence time, and an action ID, and the last columns are other attributes.
TABLE 1
| Data of | User ID | Device ID | Time of transmission of action | Actual transmission time | |
| Record | |||||
| 1 | USER1 | DEVICE1 | TIME-STAMP1 | ACTION1 | OTHERS |
| Record 2 | USER2 | DEVICE2 | TIME-STAMP2 | ACTION1 | OTHERS |
| Record 3 | USER3 | DEVICE2 | TIME-STAMP3 | ACTION1 | OTHERS |
It should be noted that the user ID is the unique number of the user in all users, and may be numbered in various ways, such as a mobile phone number and other encrypted numbers generated by the mobile phone number; the device ID is a unique number in all devices, that is, each device has only one number, and the numbers of the same device but different devices may be different. The action occurrence time is a time occurring when a user uses a certain device and gives a certain instruction to the certain device, and it is noted that the time is not a time when the instruction is recorded but a time when the instruction is executed. If the command issue is not executed, no record is generated. The actually occurring action is an action actually performed when the user uses the device. If multiple actions occur at the same time, multiple records are generated. However, since the computer calculates time in milliseconds, it is substantially unlikely that multiple actions will occur at a time. Other attributes may record multiple attributes, such as user age, gender, how long the device was used, etc., to facilitate the recommendation of algorithms and data mining efforts.
Alternatively, FIG. 3 is a timing diagram of a sequence of data processing determination actions performed by a data structure according to an alternative embodiment of the invention.
Optionally, fig. 4 is a schematic diagram of a sequence of user time actions according to an alternative embodiment of the invention; table 1 is used to show the action and time relationship diagram of a certain user (corresponding to the target object in the embodiment of the present invention), and fig. 4 shows a block diagram of four actions a, b, c, d to show that a certain action occurs at a certain time, such as: and a step a. The directional arrows in the graph point to the next action after a certain action occurs, for example: the arrows go from action a- > action b. Indicating that an action a occurs followed by an action b.
Optionally, fig. 5 is a schematic diagram of calculating a time interval between any two consecutive actions of a user according to an alternative embodiment of the present invention; in fig. 5, each action calculates its own time interval, taking action d as an example, the calculation formula is: diff (diff)d＝dt-ct(ii) a Wherein, diffdTime interval representing an action d, dtIndicates the time when the action d occurred, ctRepresenting the time when action c occurs before action d. Null if there is no action between actions a (if the user is a new user)tIndicating that its value may be set to the computer system initial time or a very early time such as 1700 years. For any action i, and let t be the time of occurrenceiBy ti-1Indicating the moment, diff, of a previous action of a certain user at which a certain action occurrediThe general formula for the ith action calculation interval is:
diffi＝ti-ti-1；
alternatively, fig. 6 is a block diagram showing the value of the characteristic value mid _ beg after calculating the time interval diff according to an alternative embodiment of the present invention; fig. 7 is a schematic diagram (one) of the corresponding calculation results of a time-action sequence of a user giving the value of the characteristic value mid _ beg after calculating the time interval diff according to an alternative embodiment of the invention; for example, if the interval between two actions exceeds 5 minutes, the actions may be regarded as not being continuous. Mid is used for indicating that a specified number of characters are intercepted from a character string, and the beg returns an iterator pointing to a head element relative to a file head; end is relative to the file's end, the iterator of the elements behind the end. If the diff field is less than or equal to the threshold (e.g. 5 minutes per value) as determined in fig. 5, if the diff field is less than or equal to the threshold, it indicates that the record is the middle of an action sequence and records the tag mid _ beg as 1, otherwise it is 0.
Optionally, fig. 8 is a schematic diagram (ii) of a calculation result corresponding to a certain user time action sequence with a value of the feature value mid _ beg given after calculating the time interval diff according to an alternative embodiment of the present invention; in the calculation result in fig. 8, the latter value of the attribute of each record mid _ beg tag is calculated on the basis of the calculation result in fig. 7, and named begin _ state. That is, the value of mid _ beg corresponding to the action next to the action corresponding to the action of the user is calculated from the result of fig. 7, and this attribute is named as beg _ state. When there is no follow-up action for an action, the value of the attribute of beg _ state is null.
Optionally, fig. 9 is a schematic diagram of a state corresponding to a certain user time action sequence according to an alternative embodiment of the present invention; combining mid _ beg and begin _ tag in fig. 8 determines the data item status, for example: the identification of 01 as 'beg' is the beginning, when the data item status is 11, the identification of 'mid' is in progress, when the data item combination is 10, the identification of 'end' is the end, and a new field is defined to record the identification result. Further, the data may be compared with the original data, extracted as required data, and formed into a data structure such as a user ID, a sequence start time, and sequence data, to obtain sequence data (i.e., a behavior sequence in the embodiment of the present invention) as shown in table 2.
TABLE 2
| Data of | User ID | Time of start of sequence | Sequence value | |
| Record | ||||
| 1 | |
t1 | a、b、c | |
| Record 2 | User 2 | t2 |
It is noted that FIG. 10 is a flow diagram of a sequence of data processing determining actions performed by a data structure in accordance with an alternative embodiment of the present invention; the data result can be processed arbitrarily by the above method to generate a sequence. The method can be used as the input of an artificial intelligence algorithm and a machine learning algorithm, or can directly perform data mining, such as calculating the time of the user lasting the longest action sequence, the action quantity and the like. The data structure may be used as a basis for all user behavior pattern recognition.
In summary, with the alternative embodiment of the present invention, a behavior sequence of any length can be generated. The key point is the construction of a feature vector when a user sequence starts and ends, and the protection point is a calculation method mainly for the feature vector, because if the generation of a complete action sequence algorithm of a user is required to be calculated, the complete action sequence is a basic data structure of data mining, machine learning and an artificial intelligence algorithm. Without this structure or similar structures, tasks such as usage data mining, intent recognition, etc. may not be performed. Therefore, the behavior data corresponding to the original data can be connected with advanced knowledge mapping algorithm and the like. Without such a data structure, it would be difficult to import similar raw data into an advanced artificial intelligence algorithm. The method solves the first step from original data to various machine learning algorithms and artificial intelligence algorithms, and provides support for data structure for similar recommendation algorithms.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to perform the behavior sequence generation described in the embodiments of the present invention.
In this embodiment, a device for generating a behavior sequence is further provided, where the device is used to implement the foregoing embodiment and the preferred embodiments, and details of the description already made are omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 11 is a block diagram of a configuration of an apparatus for generating a sequence of behaviors according to an embodiment of the present invention, as shown in fig. 11, the apparatus including:
(1) an obtaining module 1102, configured to obtain data records of a target object for different devices, where the data records include: the target object is used for operating events of different devices and action execution time corresponding to the operating events;
(2) the sequence module 1104 is configured to determine an action execution sequence of the plurality of operation events according to a sequence of action execution times of the plurality of operation events;
(3) a determining module 1106, configured to determine a time interval between every two adjacent operation events according to the action execution sequence, and determine a behavior sequence of the target object according to the time interval, where the behavior sequence is used to indicate a plurality of operation events that are continuously executed by the target object within a preset time period.
By the device, data records of the target object to different devices are obtained, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events; determining the action execution sequence of the plurality of operation events according to the sequence of the action execution times of the plurality of operation events; determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period, that is, by extracting the behavior sequence corresponding to the data record of the target object to different devices, and further realizing the processing and mining of the original data under the condition of no special data requirement, therefore, the problems that the behavior sequence mining with any length cannot be carried out in the original data in the prior art so as to determine the operation behavior of the target object to different devices and the like can be solved, and further the behavior sequence belonging to the target object can be generated according to the data record of the target object to different devices, and the behavior habit of the target object can be better predicted by using a knowledge graph algorithm based on the behavior sequence, therefore, the operation experience of the target object on the intelligent equipment can be improved, and the first step from the original data to various machine learning algorithms and artificial intelligence algorithms becomes more convenient, efficient and accurate.
In an exemplary embodiment, the determining module is further configured to obtain a time interval between every two adjacent operation events, so as to obtain a plurality of time intervals; for each time interval in the multiple time intervals, determining the size relationship between each time interval and a preset interval threshold, and determining a first tag value of a first characteristic tag and a second tag value of a second characteristic tag of an operation event according to the size relationship; and determining the behavior sequence of the target object according to the first label value and the second label value.
In an exemplary embodiment, the determining module further includes: the first determining unit is used for determining that the first label value of the feature label of the operation event with the action execution time after the action execution time in every two adjacent operation events is an invalid value under the condition that each time interval is larger than a preset interval threshold value; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the first label value of the feature label of the operation event with the action execution time after in each two adjacent operation events is a valid value.
In an exemplary embodiment, the determining module further includes: the second determining unit is used for determining that a second label value of a feature label of an operation event with a previous action execution time in every two adjacent operation events is an invalid value under the condition that each time interval is larger than a preset interval threshold; and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the second label value of the characteristic label of the operation event with the action execution time before in each two adjacent operation events is a valid value.
In an exemplary embodiment, the determining module further includes: the identification unit is used for combining the first label value and the second label value corresponding to each operation event to obtain a data item of the operation event; determining the execution sequence of each operation event in the behavior sequence according to the arrangement sequence of the first tag value and the second tag value in the data item, and identifying each operation event; and determining the behavior sequence of the target object according to the identification result.
In an exemplary embodiment, the identification unit is further configured to, in a case that the data item is an arrangement of an invalid value and a valid value, mark a start identifier on the current operation event, and determine that the current operation event is a start action behavior; under the condition that the data items are the effective values and the effective value arrangement, marking a current operation event with an identification, and determining the current operation event as an action-in-progress behavior; and under the condition that the data items are arranged by the effective values and the invalid values, marking an ending mark on the current operation event, and determining that the current operation event is an action ending behavior.
In an exemplary embodiment, the identifying unit is further configured to record an identification result of each operation event by using the target field; matching the identification result with the data records of different equipment obtained by the target object; and extracting the identification result and the action execution time corresponding to the operation event by using a preset data structure to obtain a behavior sequence corresponding to the operation event sequence for representing that the target object executes different devices.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the device or assembly referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, and the two components can be communicated with each other. When an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
In an exemplary embodiment, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring data records of the target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events;
s2, determining the action execution sequence of the operation events according to the sequence of the action execution time of the operation events;
and S3, determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period.
In an exemplary embodiment, in the present embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
In an exemplary embodiment, in the present embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring data records of the target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events;
s2, determining the action execution sequence of the operation events according to the sequence of the action execution time of the operation events;
and S3, determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period.
In an exemplary embodiment, for specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation manners, and details of this embodiment are not described herein again.
It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, which may be centralized on a single computing device or distributed across a network of computing devices, and in one exemplary embodiment may be implemented using program code executable by a computing device, such that the steps shown and described may be executed by a computing device stored in a memory device and, in some cases, executed in a sequence different from that shown and described herein, or separately fabricated into individual integrated circuit modules, or multiple ones of them fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for generating a sequence of behaviors, comprising:
acquiring data records of a target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events;
determining the action execution sequence of the operation events according to the sequence of the action execution times of the operation events;
and determining the time interval of every two adjacent operation events according to the action execution sequence, and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period.
2. The method for generating the behavior sequence according to claim 1, wherein determining the behavior sequence of the target object according to the time interval comprises:
acquiring the time interval of every two adjacent operation events to obtain a plurality of time intervals;
for each time interval in the plurality of time intervals, determining the size relationship between each time interval and a preset interval threshold, and determining a first tag value of a first characteristic tag and a second tag value of a second characteristic tag of the operation event according to the size relationship;
and determining the behavior sequence of the target object according to the first label value and the second label value.
3. The method for generating the behavior sequence according to claim 2, wherein determining the first tag value as the feature tag of the operation event according to the magnitude relation comprises:
under the condition that each time interval is larger than a preset interval threshold, determining that a first tag value of a feature tag of an operation event with a later action execution time in each two adjacent operation events is an invalid value;
and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that the first label value of the feature label of the operation event with the action execution time after in each two adjacent operation events is an effective value.
4. The method for generating a sequence of behaviors of claim 2, wherein determining a second tag value that is a second feature tag of the operation event according to the magnitude relation comprises:
determining that a second tag value of a feature tag of an operation event with a previous action execution time in each two adjacent operation events is an invalid value under the condition that each time interval is larger than a preset interval threshold;
and under the condition that each time interval is smaller than or equal to a preset interval threshold, determining that a second label value of the feature label of the operation event with the action execution time before in each two adjacent operation events is an effective value.
5. The method for generating the behavior sequence according to claim 2, wherein determining the behavior sequence of the target object according to the first tag value and the second tag value comprises:
combining the first tag value and the second tag value corresponding to each operation event to obtain a data item of the operation event;
determining the execution sequence of each operation event in the behavior sequence according to the arrangement sequence of the first tag value and the second tag value in the data item, and identifying each operation event;
and determining the behavior sequence of the target object according to the identification result.
6. The method for generating a behavioral sequence according to claim 5, wherein determining an execution order of each operational event in the behavioral sequence according to an arrangement order of the first tag value and the second tag value in the data item, and identifying each operational event comprises:
under the condition that the data items are arranged by invalid values and valid values, marking a starting identifier on a current operation event, and determining that the current operation event is a starting action behavior;
under the condition that the data items are arranged by effective values and effective values, marking a current operation event with an identification, and determining the current operation event as an action-in-progress behavior;
and under the condition that the data items are arranged by effective values and invalid values, marking an ending mark on the current operation event, and determining that the current operation event is an action ending behavior.
7. The method for generating the behavior sequence according to claim 5, wherein determining the behavior sequence of the target object according to the result of the identification comprises:
recording the identification result of each operation event by using a target field, wherein the target field is;
matching the identification result with data records of different equipment obtained by the target object;
and extracting the identification result and the action execution time corresponding to the operation event by using a preset data structure to obtain a behavior sequence corresponding to an operation event sequence for representing that the target object executes different devices.
8. An apparatus for generating a sequence of behaviors, comprising:
the acquisition module is used for acquiring data records of a target object to different devices, wherein the data records comprise: the target object is used for operating events of different devices and action execution time corresponding to the operating events;
the sequence module is used for determining the action execution sequence of the operation events according to the sequence of the action execution times of the operation events;
and the determining module is used for determining the time interval of every two adjacent operation events according to the action execution sequence and determining the behavior sequence of the target object according to the time interval, wherein the behavior sequence is used for indicating a plurality of operation events continuously executed by the target object within a preset time period.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the generation of the sequence of actions of any of claims 1 to 7 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and the processor is arranged to execute the computer program to perform the generation of the sequence of behaviors as claimed in any one of claims 1 to 7.
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| CN109542944B (en) * | 2018-09-29 | 2023-07-25 | 广东工业大学 | Intelligent home user control behavior recommendation method based on time sequence causality analysis |
| CN109933502B (en) * | 2019-01-23 | 2022-05-20 | 平安科技（深圳）有限公司 | Electronic device, user operation record processing method and storage medium |
| CN113505272B (en) * | 2021-07-05 | 2024-05-03 | 珠海格力电器股份有限公司 | Control method and device based on behavior habit, electronic equipment and storage medium |
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