CN114862140A - Behavior analysis-based potential evaluation method, device, equipment and storage medium - Google Patents
Behavior analysis-based potential evaluation method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a potential evaluation method based on behavior analysis, which comprises the following steps: performing data screening on the user behavior data based on a random forest algorithm to obtain target behavior data; clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category; constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table; and performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result. In addition, the invention also relates to a block chain technology, and the behavior credit value can be stored in the node of the block chain. The invention also provides a potential evaluation device based on behavior analysis, electronic equipment and a storage medium. The invention can improve the efficiency of potential evaluation.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a potential evaluation method and device based on behavior analysis, electronic equipment and a storage medium.
Background
With the continuous development of the human agent industry, more and more fresh blood is rushed into the human agent industry. The process of training an agent who just enters the workplace is a very long process, and a simple potential evaluation needs to be carried out on the agent after the training process is finished, so that the workplace competitiveness and the subsequent development potential of the agent are analyzed. The existing potential evaluation method is generally rated and counted according to subjective rating standards, which consumes a great deal of manpower and material resources and causes low efficiency of potential evaluation.
Disclosure of Invention
The invention provides a potential evaluation method and device based on behavior analysis, electronic equipment and a storage medium, and mainly aims to improve the efficiency of potential evaluation.
In order to achieve the above object, the present invention provides a potential evaluation method based on behavior analysis, including:
acquiring user behavior data of a user in a preset scene, and performing data screening on the user behavior data based on a random forest algorithm to obtain target behavior data;
clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category;
constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table;
and performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
Optionally, the data screening of the user behavior data based on the random forest algorithm to obtain target behavior data includes:
extracting training behavior records in the user behavior data as a sample set, and extracting learning behavior data in the historical behavior information set as an index set;
randomly selecting a sub-sample set from the sample set, and randomly selecting a sub-index set from the index set;
constructing a random forest by using the subsample set and the sub-index set;
and selecting a preset number of operation behavior data as the target behavior data according to the index weight output by the random forest.
Optionally, the constructing a random forest by using the subsample set and the sub-index set includes:
sequentially selecting the behavior data in the sub-index set as a root node, and sequentially dividing the sub-sample set by using the behavior data to obtain a plurality of branch nodes of the root node;
and determining that the root node and a plurality of branch nodes of the root node form a decision tree, and summarizing all the decision trees to obtain the random forest.
Optionally, the clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category includes:
carrying out vector normalization processing on the target behavior data to obtain a target behavior vector;
calculating Euclidean distances between the target behavior vector and each category in a preset user standard category according to a preset Euclidean distance formula;
sequencing the obtained Euclidean distances to obtain a distance sequencing list;
and selecting Euclidean distances of a preset number in the distance ranking list as data points, calculating the occurrence frequency of each user standard category in the data points, and determining the user standard category with the highest occurrence frequency as the user behavior category of the target behavior data.
Optionally, the constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights includes:
respectively identifying the occurrence frequency of a plurality of reference behavior categories in a preset behavior database, and calculating the ratio of the occurrence frequency to the total number of behavior data in the behavior database;
and constructing a behavior reference table by taking the ratio as the behavior weight of the reference behavior category.
Optionally, the performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value includes:
identifying the data type to which the user behavior data belongs, and finding out the corresponding behavior weight according to the data type;
and multiplying the behavior reference value corresponding to the user behavior data by the behavior weight to obtain a behavior score value.
Optionally, the performing, according to the behavior score, a potential analysis on the user corresponding to the user behavior data to obtain a potential evaluation result includes:
judging the magnitude between the behavior score value and a preset behavior threshold value;
and when the behavior score value is larger than or equal to the behavior threshold value, inputting the user behavior data into a potential estimation model to obtain a potential estimation result.
In order to solve the above problems, the present invention also provides a potential evaluation device based on behavior analysis, the device comprising:
the data screening module is used for acquiring user behavior data of a user in a preset scene, and screening the user behavior data based on a random forest algorithm to obtain target behavior data;
the data clustering module is used for clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category;
the weight searching module is used for constructing a behavior reference table among a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table;
and the potential analysis module is used for performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the behavioral analysis based potential assessment method described above.
In order to solve the above problem, the present invention further provides a storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the potential evaluation method based on behavior analysis described above.
In the embodiment of the invention, the target behavior data is obtained by screening the data of the user behavior data based on the random forest algorithm, and the accuracy of the obtained target behavior data is ensured by the data screening. Clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category, searching out a behavior weight corresponding to the user behavior category according to the behavior reference table, wherein the behavior weight is used for measuring the proportion of the user behavior category, and performing behavior scoring according to the behavior weight, so that the obtained behavior scoring value is more accurate. And potential analysis is carried out on the user corresponding to the user behavior data according to the behavior score value to obtain a potential evaluation result, so that the efficiency of potential analysis is improved. Therefore, the potential evaluation method, the potential evaluation device, the electronic equipment and the storage medium based on the behavior analysis can solve the problem of low efficiency of potential evaluation.
Drawings
Fig. 1 is a schematic flow chart of a potential evaluation method based on behavior analysis according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart showing a detailed implementation of one of the steps in FIG. 1;
FIG. 3 is a schematic flow chart showing a detailed implementation of one of the steps in FIG. 2;
FIG. 4 is a schematic flow chart showing a detailed implementation of one of the steps in FIG. 1;
FIG. 5 is a schematic flow chart showing a detailed implementation of one of the steps in FIG. 1;
FIG. 6 is a schematic flow chart showing a detailed implementation of one of the steps in FIG. 1;
FIG. 7 is a schematic flow chart showing a detailed implementation of one of the steps in FIG. 1;
FIG. 8 is a functional block diagram of a potential evaluation device based on behavior analysis according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device implementing the potential evaluation method based on behavior analysis according to an embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a potential evaluation method based on behavior analysis. The execution subject of the potential evaluation method based on behavior analysis includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the potential evaluation method based on behavior analysis may be executed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a potential evaluation method based on behavior analysis according to an embodiment of the present invention is shown. In this embodiment, the potential evaluation method based on behavioral analysis includes the following steps S1-S4:
s1, obtaining user behavior data of a user in a preset scene, and performing data screening on the user behavior data based on a random forest algorithm to obtain target behavior data.
In the embodiment of the invention, the user behavior data in the preset scene refers to behavior data generated by actions of the agents extracted in different scenes of learning or working of the agents. For example, an agent may be active in various non-training class scenarios, such as offline salon activity, or may be active in learning various professional skills online for the agent.
Since the user behavior data generated by the user in the preset scene is generally large and redundant, the user behavior data needs to be subjected to data screening, and subsequent data processing is performed according to the target behavior data obtained after the data screening.
Specifically, referring to fig. 2, the data screening of the user behavior data based on the random forest algorithm to obtain target behavior data includes the following steps S11-S14:
s11, extracting training behavior records in the user behavior data as a sample set, and extracting learning behavior data in the historical behavior information set as an index set;
s12, randomly selecting a sub-sample set from the sample set, and randomly selecting a sub-index set from the index set;
s13, constructing a random forest by using the sub-sample set and the sub-index set;
and S14, selecting a preset number of operation behavior data as the target behavior data according to the index weight output by the random forest.
In detail, the random selection refers to selection with a put back in the sample set, that is, data in the selected subset sample set may be duplicated.
Further, referring to fig. 3, the constructing a random forest by using the sub-sample set and the sub-index set includes the following steps S101 to S102:
s101, sequentially selecting the behavior data in the sub-index set as a root node, and sequentially dividing the sub-sample set by using the behavior data to obtain a plurality of branch nodes of the root node;
s102, determining that the root node and the plurality of branch nodes of the root node form a decision tree, and summarizing all the decision trees to obtain the random forest.
In detail, data screening is carried out on the user behavior data based on a random forest algorithm to obtain target behavior data.
And S2, clustering the target behavior data by using a preset clustering algorithm to obtain the user behavior category.
In the embodiment of the invention, the preset clustering algorithm can be a K-Means clustering algorithm and can also be a KNN clustering algorithm. In the scheme, the clustering algorithm is a KNN clustering algorithm. The KNN clustering algorithm is a supervised classification algorithm.
Specifically, referring to fig. 4, the clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category includes the following steps S21-S24:
s21, carrying out vector normalization processing on the target behavior data to obtain a target behavior vector;
s22, calculating Euclidean distances between the target behavior vector and each category in the preset user standard categories according to a preset Euclidean distance formula;
s23, sequencing the obtained Euclidean distances to obtain a distance sequencing list;
s24, selecting Euclidean distances of a preset number in the distance ranking list as data points, calculating the occurrence frequency of each user standard category in the data points, and determining the user standard category with the highest occurrence frequency as the user behavior category of the target behavior data.
Further, the calculating the euclidean distance between the target behavior vector and each category in the preset user standard categories according to a preset euclidean distance formula includes:
the preset Euclidean distance formula is as follows:
where ρ is the Euclidean distance, x 1 And x 2 Is the vector coordinate value of the target behavior vector, y 1 And y 2 And the coordinate value is the coordinate value of the preset user standard category.
In an optional embodiment of the present invention, taking the real-time behavior information of the agent a in the video APP as an example, each of the user standard categories "learning theory knowledge" and "learning practice knowledge" includes two target behavior data: "agent 1, mouse click number 50, praise number 50, learning theoretical knowledge", "agent 2, mouse click number 100, praise number 50, learning theoretical knowledge"; the method comprises the steps of calculating Euclidean distances between an agent A and four target behavior data, sorting the four target behavior data according to the Euclidean distances, and selecting the first three Euclidean distances as data points, wherein the occurrence frequency of learning theoretical knowledge is 2/3, the occurrence frequency of learning practical knowledge is 1/3, and the type of a user A is determined to be learning theoretical knowledge.
S3, constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table.
In the embodiment of the present invention, referring to fig. 5, the constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights includes the following steps S31 to S32:
s31, respectively identifying the occurrence frequency of the reference behavior categories in a preset behavior database, and calculating the ratio of the occurrence frequency to the total number of behavior data in the behavior database;
and S32, constructing a behavior reference table by taking the ratio as the behavior weight of the reference behavior category.
In detail, the preset behavior database contains behavior data of a plurality of different types, the frequency of occurrence of the data of the reference behavior type is identified in the behavior database, meanwhile, the total number of the plurality of types of behavior data in the behavior database is counted, the frequency of occurrence of the data of the reference behavior type and the total number of the plurality of types of behavior data are subjected to ratio processing, and the ratio is used as the weight of the reference behavior type. And in the same way, calculating to obtain weights corresponding to the reference behavior categories, and generating the behavior reference table according to the reference behavior categories and the weights.
Specifically, the behavior weight corresponding to the user behavior category is searched according to the behavior reference table, for example, if the behavior weight corresponding to the user behavior category a needs to be searched, a weight value corresponding to the user behavior category a is searched in the behavior reference table.
And S4, performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
In the embodiment of the present invention, referring to fig. 6, the performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value includes the following steps S41-S42:
s41, identifying the data type to which the user behavior data belongs, and finding out the corresponding behavior weight according to the data type;
and S42, multiplying the behavior reference score corresponding to the user behavior data by the behavior weight to obtain a behavior score.
In detail, the behavior reference score corresponding to the user behavior data refers to a preset evaluation score of each user behavior data.
Specifically, referring to fig. 7, performing a potential analysis on the user corresponding to the user behavior data according to the behavior score value to obtain a potential evaluation result includes the following steps S401 to S402:
s401, judging the size between the behavior score value and a preset behavior threshold value;
s402, when the behavior score value is larger than or equal to the behavior threshold value, inputting the user behavior data into a potential estimation model to obtain a potential estimation result.
In detail, the potential estimation model may be a bidirectional long-short term memory network, or a support vector machine model. The potential evaluation result refers to judging whether the user corresponding to the user behavior data has potential, wherein the potential evaluation result may be "potential of the user" or "potential of the user temporarily absent".
In the embodiment of the invention, the target behavior data is obtained by screening the data of the user behavior data based on the random forest algorithm, and the accuracy of the obtained target behavior data is ensured by the data screening. Clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category, searching out a behavior weight corresponding to the user behavior category according to the behavior reference table, wherein the behavior weight is used for measuring the proportion of the user behavior category, and performing behavior scoring according to the behavior weight, so that the obtained behavior scoring value is more accurate. And potential analysis is carried out on the user corresponding to the user behavior data according to the behavior score value to obtain a potential evaluation result, so that the efficiency of potential analysis is improved. Therefore, the potential evaluation method based on behavior analysis provided by the invention can solve the problem of low efficiency of potential evaluation.
Fig. 8 is a functional block diagram of a potential evaluation apparatus based on behavior analysis according to an embodiment of the present invention.
The potential evaluation device 100 based on behavior analysis according to the present invention can be installed in an electronic device. According to the implemented functions, the potential evaluation device 100 based on behavior analysis may include a data filtering module 101, a data clustering module 102, a weight searching module 103 and a potential analysis module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data screening module 101 is configured to acquire user behavior data of a user in a preset scene, and perform data screening on the user behavior data based on a random forest algorithm to obtain target behavior data;
the data clustering module 102 is configured to cluster the target behavior data by using a preset clustering algorithm to obtain a user behavior category;
the weight searching module 103 is configured to construct a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and search out a behavior weight corresponding to the user behavior category according to the behavior reference table;
the potential analysis module 104 is configured to perform behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and perform potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
In detail, the potential evaluation device 100 based on behavior analysis has the following specific implementation of the modules:
the method comprises the steps of firstly, obtaining user behavior data of a user in a preset scene, and carrying out data screening on the user behavior data based on a random forest algorithm to obtain target behavior data.
In the embodiment of the invention, the user behavior data in the preset scene refers to behavior data generated by actions of the agents extracted in different scenes of learning or working of the agents. For example, an agent may be active in various non-training class scenarios, such as offline salon activity, or may be active in learning various professional skills online for the agent.
Since the user behavior data generated by the user in the preset scene is generally large and redundant, the user behavior data needs to be subjected to data screening, and subsequent data processing is performed according to the target behavior data obtained after the data screening.
Specifically, the data screening of the user behavior data based on the random forest algorithm to obtain target behavior data includes:
extracting training behavior records in the user behavior data as a sample set, and extracting learning behavior data in the historical behavior information set as an index set;
randomly selecting a sub-sample set from the sample set, and randomly selecting a sub-index set from the index set;
constructing a random forest by using the subsample set and the sub-index set;
and selecting a preset number of operation behavior data as the target behavior data according to the index weight output by the random forest.
In detail, the random selection refers to selection with a put back in the sample set, that is, data in the selected subset sample set may be duplicated.
Further, the constructing a random forest by using the sub-sample set and the sub-index set comprises:
sequentially selecting the behavior data in the sub-index set as a root node, and sequentially dividing the sub-sample set by using the behavior data to obtain a plurality of branch nodes of the root node;
and determining that the root node and a plurality of branch nodes of the root node form a decision tree, and summarizing all the decision trees to obtain the random forest.
In detail, data screening is carried out on the user behavior data based on a random forest algorithm to obtain target behavior data.
And step two, clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category.
In the embodiment of the invention, the preset clustering algorithm can be a K-Means clustering algorithm and can also be a KNN clustering algorithm. In the scheme, the clustering algorithm is a KNN clustering algorithm. The KNN clustering algorithm is a supervised classification algorithm.
Specifically, the clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category includes:
carrying out vector normalization processing on the target behavior data to obtain a target behavior vector;
calculating Euclidean distances between the target behavior vector and each category in a preset user standard category according to a preset Euclidean distance formula;
sequencing the obtained Euclidean distances to obtain a distance sequencing list;
and selecting Euclidean distances of a preset number in the distance ranking list as data points, calculating the occurrence frequency of each user standard category in the data points, and determining the user standard category with the highest occurrence frequency as the user behavior category of the target behavior data.
Further, the calculating the euclidean distance between the target behavior vector and each category in the preset user standard categories according to a preset euclidean distance formula includes:
the preset Euclidean distance formula is as follows:
wherein ρ is the Euclidean distance, x 1 And x 2 Is the vector coordinate value of the target behavior vector, y 1 And y 2 And the coordinate value is the coordinate value of the preset user standard category.
In an optional embodiment of the present invention, taking the real-time behavior information of the agent a in the video APP as an example, each of the user standard categories "learning theory knowledge" and "learning practice knowledge" includes two target behavior data: "agent 1, mouse click number 50, praise number 50, learning theoretical knowledge", "agent 2, mouse click number 100, praise number 50, learning theoretical knowledge"; the method comprises the steps of calculating Euclidean distances between an agent A and four target behavior data, sorting the four target behavior data according to the Euclidean distances, and selecting the first three Euclidean distances as data points, wherein the occurrence frequency of learning theoretical knowledge is 2/3, the occurrence frequency of learning practical knowledge is 1/3, and the type of a user A is determined to be learning theoretical knowledge.
And step three, constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table.
In the embodiment of the present invention, the constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights includes:
respectively identifying the occurrence frequency of a plurality of reference behavior categories in a preset behavior database, and calculating the ratio of the occurrence frequency to the total number of behavior data in the behavior database;
and constructing a behavior reference table by taking the ratio as the behavior weight of the reference behavior category.
In detail, the preset behavior database contains behavior data of a plurality of different types, the frequency of occurrence of the data of the reference behavior type is identified in the behavior database, meanwhile, the total number of the plurality of types of behavior data in the behavior database is counted, the frequency of occurrence of the data of the reference behavior type and the total number of the plurality of types of behavior data are subjected to ratio processing, and the ratio is used as the weight of the reference behavior type. And in the same way, calculating to obtain weights corresponding to the reference behavior categories, and generating the behavior reference table according to the reference behavior categories and the weights.
Specifically, the behavior weight corresponding to the user behavior category is searched out according to the behavior reference table, for example, if the behavior weight corresponding to the user behavior category a needs to be searched, a weight value corresponding to the user behavior category a is searched out from the behavior reference table.
And fourthly, performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
In this embodiment of the present invention, the performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value includes:
identifying the data type to which the user behavior data belongs, and finding out the corresponding behavior weight according to the data type;
and multiplying the behavior reference value corresponding to the user behavior data by the behavior weight to obtain a behavior score value.
In detail, the behavior reference score corresponding to the user behavior data refers to a preset evaluation score of each user behavior data.
Specifically, the potential analysis of the user corresponding to the user behavior data according to the behavior score value to obtain a potential evaluation result includes:
judging the magnitude between the behavior score value and a preset behavior threshold value;
and when the behavior score value is larger than or equal to the behavior threshold value, inputting the user behavior data into a potential estimation model to obtain a potential estimation result.
In detail, the potential estimation model may be a bidirectional long-short term memory network, or a support vector machine model. The potential evaluation result refers to judging whether the user corresponding to the user behavior data has potential, wherein the potential evaluation result may be "potential of the user" or "potential of the user temporarily absent".
In the embodiment of the invention, the target behavior data is obtained by screening the data of the user behavior data based on the random forest algorithm, and the accuracy of the obtained target behavior data is ensured by the data screening. Clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category, searching out a behavior weight corresponding to the user behavior category according to the behavior reference table, wherein the behavior weight is used for measuring the proportion of the user behavior category, and performing behavior scoring according to the behavior weight, so that the obtained behavior scoring value is more accurate. And potential analysis is carried out on the user corresponding to the user behavior data according to the behavior score value to obtain a potential evaluation result, so that the efficiency of potential analysis is improved. Therefore, the potential evaluation device based on behavior analysis provided by the invention can solve the problem of low efficiency of potential evaluation.
Fig. 9 is a schematic structural diagram of an electronic device implementing a potential evaluation method based on behavior analysis according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a potential evaluation program based on behavior analysis, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a potential evaluation program based on behavior analysis, and the like) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a potential evaluation program based on behavior analysis, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 9 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 9 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The potential assessment program based on behavioral analysis stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, can implement:
acquiring user behavior data of a user in a preset scene, and performing data screening on the user behavior data based on a random forest algorithm to obtain target behavior data;
clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category;
constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table;
and performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1 may be stored in a storage medium if they are implemented in the form of software functional units and sold or used as separate products. The storage medium may be volatile or nonvolatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a storage medium, which is readable and stores a computer program that, when executed by a processor of an electronic device, can implement:
acquiring user behavior data of a user in a preset scene, and performing data screening on the user behavior data based on a random forest algorithm to obtain target behavior data;
clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category;
constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table;
and performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for assessing potential based on behavioral analysis, the method comprising:
acquiring user behavior data of a user in a preset scene, and performing data screening on the user behavior data based on a random forest algorithm to obtain target behavior data;
clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category;
constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table;
and performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
2. The potential evaluation method based on behavior analysis as claimed in claim 1, wherein the data screening of the user behavior data based on the random forest algorithm to obtain target behavior data comprises:
extracting training behavior records in the user behavior data as a sample set, and extracting learning behavior data in the historical behavior information set as an index set;
randomly selecting a sub-sample set from the sample set, and randomly selecting a sub-index set from the index set;
constructing a random forest by using the subsample set and the sub-index set;
and selecting a preset number of operation behavior data as the target behavior data according to the index weight output by the random forest.
3. A potential evaluation method based on behavioral analysis according to claim 2, wherein the constructing a random forest using the set of subsamples and the set of sub-indices comprises:
sequentially selecting the behavior data in the sub-index set as a root node, and sequentially dividing the sub-sample set by using the behavior data to obtain a plurality of branch nodes of the root node;
and determining that the root node and a plurality of branch nodes of the root node form a decision tree, and summarizing all the decision trees to obtain the random forest.
4. The potential evaluation method based on behavior analysis according to claim 1, wherein the clustering the target behavior data by using a preset clustering algorithm to obtain the user behavior category comprises:
carrying out vector normalization processing on the target behavior data to obtain a target behavior vector;
calculating Euclidean distances between the target behavior vector and each category in a preset user standard category according to a preset Euclidean distance formula;
sequencing the obtained Euclidean distances to obtain a distance sequencing list;
and selecting Euclidean distances of a preset number in the distance ranking list as data points, calculating the occurrence frequency of each user standard category in the data points, and determining the user standard category with the highest occurrence frequency as the user behavior category of the target behavior data.
5. The potential evaluation method based on behavior analysis as claimed in claim 1, wherein the constructing a behavior reference table between a plurality of preset reference behavior categories and behavior weights comprises:
respectively identifying the occurrence frequency of a plurality of reference behavior categories in a preset behavior database, and calculating the ratio of the occurrence frequency to the total number of behavior data in the behavior database;
and constructing a behavior reference table by taking the ratio as the behavior weight of the reference behavior category.
6. The potential evaluation method based on behavior analysis according to claim 1, wherein the behavior scoring the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value comprises:
identifying the data type to which the user behavior data belongs, and finding out the corresponding behavior weight according to the data type;
and multiplying the behavior reference value corresponding to the user behavior data by the behavior weight to obtain a behavior score value.
7. The potential evaluation method based on behavior analysis according to any one of claims 1 to 6, wherein the potential analysis of the user corresponding to the user behavior data according to the behavior score value to obtain a potential evaluation result comprises:
judging the magnitude between the behavior score value and a preset behavior threshold value;
and when the behavior score value is larger than or equal to the behavior threshold value, inputting the user behavior data into a potential estimation model to obtain a potential estimation result.
8. A potential assessment apparatus based on behavioral analysis, the apparatus comprising:
the data screening module is used for acquiring user behavior data of a user in a preset scene, and screening the user behavior data based on a random forest algorithm to obtain target behavior data;
the data clustering module is used for clustering the target behavior data by using a preset clustering algorithm to obtain a user behavior category;
the weight searching module is used for constructing a behavior reference table among a plurality of preset reference behavior categories and behavior weights, and searching out the behavior weights corresponding to the user behavior categories according to the behavior reference table;
and the potential analysis module is used for performing behavior scoring on the user behavior data according to the behavior weight corresponding to the user behavior category to obtain a behavior scoring value, and performing potential analysis on the user corresponding to the user behavior data according to the behavior scoring value to obtain a potential evaluation result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a method for behavioral analysis based potential assessment according to any one of claims 1 to 7.
10. A storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a behavioral analysis-based potential assessment method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115422452A (en) * | 2022-08-30 | 2022-12-02 | 温州佳润科技发展有限公司 | Smart home control method, device, equipment and storage medium based on big data |
| CN115809406A (en) * | 2023-02-03 | 2023-03-17 | 佰聆数据股份有限公司 | Power consumer fine-grained classification method, device, equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115422452A (en) * | 2022-08-30 | 2022-12-02 | 温州佳润科技发展有限公司 | Smart home control method, device, equipment and storage medium based on big data |
| CN115422452B (en) * | 2022-08-30 | 2024-05-31 | 山西凯特通讯有限责任公司 | Smart home control method, device, equipment and storage medium based on big data |
| CN115809406A (en) * | 2023-02-03 | 2023-03-17 | 佰聆数据股份有限公司 | Power consumer fine-grained classification method, device, equipment and storage medium |
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