CN111738558B - Visualization method, device, equipment and storage medium for behavior risk identification - Google Patents

Visualization method, device, equipment and storage medium for behavior risk identification Download PDF

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CN111738558B
CN111738558B CN202010461003.0A CN202010461003A CN111738558B CN 111738558 B CN111738558 B CN 111738558B CN 202010461003 A CN202010461003 A CN 202010461003A CN 111738558 B CN111738558 B CN 111738558B
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CN111738558A (en
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赵喆
曾永理
刘言曌
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Ping An Technology Shenzhen Co Ltd
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Abstract

The invention relates to the technical field of big data, and discloses a visualization method, a device, equipment and a storage medium for behavior risk identification, which are applied to the field of intelligent security and are used for visually displaying risk data and improving intuitiveness and rapidness of behavior risk identification. The visualization method for behavior risk identification comprises the following steps: collecting first behavior data, embedded point identification and target object identification of a target object from a terminal of a preset embedded point mechanism; performing data mining on the first row of data according to the embedded point identification, the target object identification and the preset feature type to obtain multidimensional behavior features of the target object; performing risk assessment on the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a plurality of assessment results; and when detecting that at least one evaluation result has risk, acquiring second behavior data of the target object, and processing the first behavior data and the second behavior data through a preset two-dimensional map application to obtain a target visual interface.

Description

Visualization method, device, equipment and storage medium for behavior risk identification
Technical Field
The present invention relates to the field of data visualization in big data technologies, and in particular, to a behavior risk identification visualization method, apparatus, device, and storage medium.
Background
With the increasing development of the internet, behavior risk identification and assessment are increasingly paid attention to by businesses, and a common way for identifying the behavior risk of staff in the industry is generally to determine risk attribute entries for staff behavior risk early warning, collect data of target staff, input attribute characteristics of the target staff into a staff behavior risk early warning model and obtain risk probability scores of the target staff.
The existing risk identification technology generally excavates single behavior data characteristics, corresponds to the same type of behavior risk scene, and needs to redevelop a new risk identification process aiming at different types of behavior risk scenes, so that the multi-dimensional risk identification efficiency is lower. In addition, the risk identification result usually aims at acquiring a certain risk scoring index, and the scoring result is difficult to cover all risk meanings, and cannot trace back the personnel behavior history, so that the problem that whether the risk identification result is reliable cannot be determined.
Disclosure of Invention
The invention mainly aims to solve the problems that the existing risk identification technology has low multi-dimensional behavior feature identification efficiency and is difficult to trace back the behavior history of personnel so as to not determine whether a risk judgment result is reliable.
To achieve the above object, a first aspect of the present invention provides a method for visualizing behavior risk identification, including: collecting first behavior data and identification data of a target object from a terminal of a preset buried point mechanism, wherein the identification data comprises a buried point identification and a target object identification; performing data mining on first row data of the target object according to the buried point identification, the target object identification and the preset feature type to obtain multidimensional behavior features of the target object; performing risk assessment on the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a plurality of assessment results; when detecting that at least one evaluation result is at risk, acquiring second behavior data of a target object, and processing the first behavior data of the target object and the second behavior data of the target object through a preset two-dimensional map application to generate and display a target visual interface, wherein the second behavior data is generated before the first behavior data is generated, and the target visual interface is used for displaying the first behavior data and/or risk behaviors represented by the second behavior data in a two-dimensional map mode.
Optionally, in a first implementation manner of the first aspect of the present invention, the collecting, from a terminal of a preset embedded point mechanism, first behavior data and identification data of a target object includes: receiving buried point data sent by a terminal of a preset buried point mechanism, wherein the terminal of the preset buried point mechanism comprises a mobile phone, a vehicle-mounted automatic diagnosis device and attendance checking equipment; and carrying out data analysis on the buried data according to a first preset data format to obtain first behavior data and identification data of the target object.
Optionally, in a second implementation manner of the first aspect of the present invention, the data mining on the first row of data of the target object according to the buried point identifier, the target object identifier and the preset feature type, to obtain a multidimensional behavior feature of the target object, includes: judging whether the first line data of the target object belongs to target positioning system data or not based on the buried point identification; if the first behavior data of the target object does not belong to the target positioning system data, carrying out service feature mining on the first behavior data of the target object according to the preset feature type to obtain first behavior features; if the first behavior data of the target object belong to the target positioning system data, extracting the characteristics of the first behavior data of the target object, and storing the extracted characteristics into a preset database to obtain second behavior characteristics, wherein the second behavior characteristics comprise target positioning system density characteristics, track synthesis characteristics and detention location characteristics; and combining the first behavior feature and the second behavior feature, and performing relation mapping on the combined feature data and the target object identifier to obtain the multidimensional behavior feature of the target object.
Optionally, in a third implementation manner of the first aspect of the present invention, the risk assessment is performed on the multidimensional behavior feature of the target object by a preset risk identification engine to obtain a plurality of assessment results, including: matching and identifying the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a risk rule data set, wherein the risk rule data set comprises a plurality of risk rules; and respectively calling a plurality of preset risk assessment models according to the plurality of risk rules, so that the plurality of preset risk assessment models carry out risk assessment on the multidimensional behavior characteristics of the target object to obtain a plurality of risk scores or a plurality of risk grades, and setting the plurality of risk scores or the plurality of risk grades as a plurality of assessment results.
Optionally, in a fourth implementation manner of the first aspect of the present invention, when detecting that at least one evaluation result is at risk, obtaining second behavior data of a target object, processing, by a preset two-dimensional map application, the first behavior data of the target object and the second behavior data of the target object, and generating and displaying a target visual interface, where the second behavior data is generated before the first behavior data is generated, and the target visual interface is configured to display, in a two-dimensional map manner, risk behaviors that are characterized by the first behavior data and/or the second behavior data, and includes: when detecting that the at least one evaluation result has risk, acquiring second behavior data of a target object according to the target object identifier, wherein the second behavior data is generated before the first behavior data is generated; data packaging is carried out on the first behavior data of the target object and the second behavior data of the target object according to a second preset data format, and packaged data are obtained; and calling a preset two-dimensional map application to perform layer rendering on the packaged data and display the packaged data to obtain a target visual interface, wherein the target visual interface is used for displaying the first behavior data and/or the risk behavior represented by the second behavior data in a two-dimensional map mode.
Optionally, in a fifth implementation manner of the first aspect of the present invention, when the risk of at least one evaluation result is detected, second behavior data of the target object is obtained, and the first behavior data of the target object and the second behavior data of the target object are processed by a preset two-dimensional map application, so as to generate and display a target visual interface, where the second behavior data is generated before the first behavior data is generated, and the target visual interface is used to display, in a two-dimensional map manner, the first behavior data and/or before a risk behavior characterized by the second behavior data, and the visualization method for behavior risk identification further includes: acquiring a behavior risk scene according to preset business requirements, and setting a behavior risk standard for the behavior risk scene to obtain a formulated behavior risk standard; acquiring sample data, training an initial behavior risk model based on the sample data and the formulated behavior risk standard, and obtaining a preset behavior risk model; adding the preset behavior risk model to the preset risk recognition engine, configuring a corresponding risk rule for the preset behavior risk model, wherein the preset behavior risk model is used for receiving the call of the preset risk recognition engine and outputting a corresponding evaluation result.
Optionally, in a sixth implementation manner of the first aspect of the present invention, when the risk of at least one evaluation result is detected, second behavior data of the target object is obtained, and the first behavior data of the target object and the second behavior data of the target object are processed by a preset two-dimensional map application, so as to generate and display a target visual interface, where the second behavior data is generated before the first behavior data is generated, and the target visual interface is used to display, in a two-dimensional map manner, the risk behavior represented by the first behavior data and/or the second behavior data, and the visualization method for behavior risk recognition further includes: acquiring a target picture, and combining the multidimensional behavior characteristics of the target object, the multiple evaluation results and the target picture based on a preset template to obtain a risk evaluation report, wherein the target picture is a picture containing the target visual interface; and sending the risk assessment report to a target terminal according to a preset mode, wherein the preset mode comprises a mail mode and a message pushing mode.
The second aspect of the present invention provides a visualization apparatus for behavior risk identification, including: the acquisition module is used for acquiring first behavior data and identification data of a target object from a terminal of a preset buried point mechanism, wherein the identification data comprises a buried point identification and a target object identification; the mining module is used for carrying out data mining on the first row of data of the target object according to the buried point identification, the target object identification and the preset feature type to obtain multidimensional behavior features of the target object; the evaluation module is used for performing risk evaluation on the multidimensional behavior characteristics of the target object through a preset risk recognition engine to obtain a plurality of evaluation results; the display module is used for acquiring second behavior data of a target object when detecting that at least one evaluation result has risks, and processing the first behavior data of the target object and the second behavior data of the target object through a preset two-dimensional map application to generate and display a target visual interface, wherein the second behavior data is generated before the first behavior data is generated, and the target visual interface is used for displaying the first behavior data and/or the risk behaviors represented by the second behavior data in a two-dimensional map mode.
Optionally, in a first implementation manner of the second aspect of the present invention, the acquisition module is specifically configured to: receiving buried point data sent by a terminal of a preset buried point mechanism, wherein the terminal of the preset buried point mechanism comprises a mobile phone, a vehicle-mounted automatic diagnosis device and attendance checking equipment; and carrying out data analysis on the buried data according to a first preset data format to obtain first behavior data and identification data of the target object.
Optionally, in a second implementation manner of the second aspect of the present invention, the mining module is specifically configured to: judging whether the first line data of the target object belongs to target positioning system data or not based on the buried point identification; if the first behavior data of the target object does not belong to the target positioning system data, carrying out service feature mining on the first behavior data of the target object according to the preset feature type to obtain first behavior features; if the first behavior data of the target object belong to the target positioning system data, extracting the characteristics of the first behavior data of the target object, and storing the extracted characteristics into a preset database to obtain second behavior characteristics, wherein the second behavior characteristics comprise target positioning system density characteristics, track synthesis characteristics and detention location characteristics; and combining the first behavior feature and the second behavior feature, and performing relation mapping on the combined feature data and the target object identifier to obtain the multidimensional behavior feature of the target object.
Optionally, in a third implementation manner of the second aspect of the present invention, the evaluation module is specifically configured to: matching and identifying the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a risk rule data set, wherein the risk rule data set comprises a plurality of risk rules; and respectively calling a plurality of preset risk assessment models according to the plurality of risk rules, so that the plurality of preset risk assessment models carry out risk assessment on the multidimensional behavior characteristics of the target object to obtain a plurality of risk scores or a plurality of risk grades, and setting the plurality of risk scores or the plurality of risk grades as a plurality of assessment results.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the display module is specifically configured to: when detecting that the at least one evaluation result has risk, acquiring second behavior data of a target object according to the target object identifier, wherein the second behavior data is generated before the first behavior data is generated; data packaging is carried out on the first behavior data of the target object and the second behavior data of the target object according to a second preset data format, and packaged data are obtained; and calling a preset two-dimensional map application to perform layer rendering on the packaged data and display the packaged data to obtain a target visual interface, wherein the target visual interface is used for displaying the first behavior data and/or the risk behavior represented by the second behavior data in a two-dimensional map mode.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the visualization device for behavior risk identification further includes: the setting module is used for acquiring a behavior risk scene according to preset service requirements, setting a behavior risk standard for the behavior risk scene and obtaining a formulated behavior risk standard; the training module is used for acquiring sample data, training an initial behavior risk model based on the sample data and the formulated behavior risk standard, and obtaining a preset behavior risk model; the configuration module is used for adding the preset behavior risk model into the preset risk recognition engine, configuring a corresponding risk rule for the preset behavior risk model, and receiving the call of the preset risk recognition engine and outputting a corresponding evaluation result.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the visualization device for behavior risk identification further includes: the combination module is used for acquiring a target picture, and combining the multidimensional behavior characteristics of the target object, the multiple evaluation results and the target picture based on a preset template to obtain a risk evaluation report, wherein the target picture is a picture containing the target visual interface; and the sending module is used for sending the risk assessment report to the target terminal according to a preset mode, wherein the preset mode comprises a mail mode and a message pushing mode.
A third aspect of the present invention provides a visualization device for behavioral risk identification, comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line; the at least one processor invokes the instructions in the memory to cause the behavioural risk identification visualization device to perform the behavioural risk identification visualization method described above.
A fourth aspect of the invention provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the above-described method of visualising behavioural risk identification.
In the technical scheme provided by the invention, first behavior data and identification data of a target object are acquired from a terminal of a preset buried point mechanism, wherein the identification data comprises a buried point identification and a target object identification; performing data mining on first row data of the target object according to the buried point identification, the target object identification and the preset feature type to obtain multidimensional behavior features of the target object; performing risk assessment on the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a plurality of assessment results; when detecting that at least one evaluation result is at risk, acquiring second behavior data of a target object, and processing the first behavior data of the target object and the second behavior data of the target object through a preset two-dimensional map application to generate and display a target visual interface, wherein the second behavior data is generated before the first behavior data is generated, and the target visual interface is used for displaying the first behavior data and/or risk behaviors represented by the second behavior data in a two-dimensional map mode. In the embodiment of the invention, the risk assessment model is decoupled from the risk scene by the risk identification engine, and the multidimensional behavior characteristics of the target object are identified and assessed, so that the risk identification rule has expandability, the risk identification efficiency is improved, in addition, the risk behavior of the target object is intuitively displayed by the visualized application based on the two-dimensional map, and the intuitiveness and rapidness of behavior risk identification are improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a visualization method for behavior risk identification in an embodiment of the present invention;
FIG. 2 is a schematic diagram of another embodiment of a visualization method for risk identification in an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a visualization device for risk identification according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another embodiment of a visualization device for risk identification in an embodiment of the present invention;
fig. 5 is a schematic diagram of an embodiment of a visualization device for risk identification in an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a behavior risk identification visualization method, device, equipment and storage medium, which are used for decoupling a risk assessment model from a risk scene through a risk identification engine, identifying and assessing multidimensional behavior characteristics of a target object, and improving risk identification efficiency.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and an embodiment of a visualization method for identifying a risk of behavior in an embodiment of the present invention includes:
101. first behavior data and identification data of a target object are collected from a terminal of a preset buried point mechanism, wherein the identification data comprise buried point identifications and target object identifications.
The preset point embedding mechanism is a data acquisition mechanism preset for meeting the requirements of quick, efficient and rich data application, and is used for acquiring and recording the process and the result of the target object behavior at the terminal and reporting the process and the result of the target object behavior to the server. The terminal of the preset embedded point mechanism comprises a mobile phone, a vehicle-mounted automatic diagnosis device and an attendance checking device, and further the server collects first behavior data and identification data of a target object through the mobile phone, the vehicle-mounted automatic diagnosis device and the attendance checking device, wherein the identification data comprises embedded point identification and target object identification, for example, the embedded point identification pay_fail is used for identifying an embedded point event of order payment failure, the target object identification can be user_A, and the embedded point identification and the target object identification can be in case English, numbers and underlined, but cannot begin with the numbers. The first row of data of the target object comprises target positioning system data and related business data, wherein the target positioning system data is Global Positioning System (GPS) data; the server stores the first behavior data of the target object, the buried point identification and the target object identification into a preset database.
It can be understood that the execution subject of the present invention may be a visualization device for behavior risk recognition, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
102. And carrying out data mining on the first row of data of the target object according to the buried point identification, the target object identification and the preset feature type to obtain the multidimensional behavior feature of the target object.
Further, the server stores first row data of the target object into a distributed database hbase according to the embedded point identification and the target object identification, mass data processing shortages and database space computing capacity provided by a distributed system infrastructure hadoop are utilized, the server mines multidimensional behavior characteristics of the target object in real time according to preset characteristic types, wherein the multidimensional behavior characteristics of the target object comprise target positioning system density characteristics, track synthesis characteristics, retention time length characteristics, retention place characteristics and preset service characteristics, the preset service characteristics comprise whether a super-dispatch area characteristic and whether a late arrival characteristic are included, further, the server stores the density characteristics, the track synthesis characteristics, the retention time length characteristics and the retention place characteristics in an object-relational database postgre and stores the density characteristics, the track synthesis characteristics, the retention time length characteristics and the retention place characteristics in a binary mode, and the preset service characteristics are generally stored by adopting preset field tables of the object-relational database mongdb or preset field tables of the relational database.
103. And carrying out risk assessment on the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a plurality of assessment results.
The preset risk identification engine adopts at least one risk identification application interface to carry out risk identification and evaluation processing on the multidimensional behavior characteristics of the target object. Specifically, the server identifies the multidimensional behavior feature of the target object through a preset risk identification engine to obtain a plurality of risk judgment rule standardized interfaces, sequentially processes the multidimensional behavior feature of the target object based on the plurality of risk judgment rule standardized interfaces to obtain a plurality of evaluation results, for example, 3 evaluation results A, B and C, and further, the server adopts the target object identifier to correlate the mapping relationship between the multidimensional behavior feature of the target object and the plurality of evaluation results.
It should be noted that, the server may directly call the preset risk recognition engine to perform recognition processing after uploading the first data of the target object, or may call the preset risk recognition engine to perform recognition processing at regular time according to a preset duration, where the preset duration may be 1 minute or 5 minutes, and the specific application is not limited herein.
104. When detecting that at least one evaluation result has risks, acquiring second behavior data of the target object, processing the first behavior data of the target object and the second behavior data of the target object through a preset two-dimensional map application, and generating and displaying a target visual interface, wherein the second behavior data is generated before the first behavior data is generated, and the target visual interface is used for displaying the first behavior data and/or risk behaviors represented by the second behavior data in a two-dimensional map mode.
When detecting that at least one evaluation result has risk, the server may read second behavior data of the target object from the preset database according to the target object identifier, for example, 3 evaluation results A, B and C, the evaluation result a and the evaluation result C have no risk, but the evaluation result a has risk, and the server acquires target positioning system data; the server performs data encapsulation on the first behavior data of the target object and the second behavior data of the target object, performs rendering processing on the encapsulated data through a preset two-dimensional map interface corresponding to a preset two-dimensional map application, obtains a target visual interface, and displays the target visual interface.
Further, the risk behaviors represented by the first behavior data and/or the second behavior data of the target object are intuitively displayed based on the target visual interface, so that the risk behaviors represented by the first behavior data and/or the second behavior data of the target object can be traced back and visualized, and fed back to the preset risk recognition engine through a preset confirmation mechanism, so that the preset risk recognition engine is optimized, and the risk recognition rate is quickly and efficiently improved.
In the embodiment of the invention, the risk assessment model is decoupled from the risk scene by the risk identification engine, and the multidimensional behavior characteristics of the target object are identified and assessed, so that the risk identification rule has expandability, the risk identification efficiency is improved, in addition, the risk behavior of the target object is intuitively displayed by the visualized application based on the two-dimensional map, and the intuitiveness and rapidness of behavior risk identification are improved. The scheme can be applied to the field of intelligent security, so that the construction of a smart city is promoted.
Referring to fig. 2, another embodiment of a visualization method for identifying a risk of behavior in an embodiment of the present invention includes:
201. first behavior data and identification data of a target object are collected from a terminal of a preset buried point mechanism, wherein the identification data comprise buried point identifications and target object identifications.
Specifically, firstly, a server receives buried point data sent by a terminal of a preset buried point mechanism, wherein the buried point data are data collected based on preset buried point events, for example, for car insurance business, buried point data of cases are operated in mobile phone business application when a surveyor goes out of danger, which pictures are uploaded at any time, how to adjust pay amount when a case is closed, account login records and survey photo shooting data, the terminal of the preset buried point mechanism comprises a mobile phone, a vehicle-mounted automatic diagnosis device and attendance checking equipment, optionally, the server sets buried points for the terminal to obtain the terminal of the preset buried point mechanism; the server receives the embedded point data sent by the terminal of the preset embedded point mechanism at regular time, for example, the target positioning system data or the attendance data is collected once every 10 seconds or 15 seconds, and the attendance data comprises a card punching time, a card punching place and a card punching object.
Secondly, the server analyzes the buried point data according to a first preset data format to obtain first behavior data and identification data of a target object, wherein the identification data comprises a buried point identification and a target object identification, the first preset data format is a data exchange format (javaScript object notation, JSON) of a JS object numbered musical notation, fields in the buried point data comprise buried point identification buryId, the target object identification userId, longitude and latitude uploading time gpsTime, longitude and latitude gps and moving speed, the longitude and latitude can be separated by commas, for example, identification data { "ids_data": "buryId": "23", "userId": "USER_A" }, and the first row of the target object is data { "gpsTime": "2020-1-12:54:11", "speed": "21.1", "gps": "112.3232,28.322423" }. Further, the server carries out association inquiry on the buried point identification in the buried point data and the identification of the preset service scene to obtain the preset service scene; and the server performs data encapsulation on the target behavior data based on the preset service scene and stores the target behavior data in a preset database.
202. And carrying out data mining on the first row of data of the target object according to the buried point identification, the target object identification and the preset feature type to obtain the multidimensional behavior feature of the target object.
Specifically, the server judges whether the first behavior data of the target object belongs to the target positioning system data or not based on the embedded point identification; if the first behavior data of the target object does not belong to the target positioning system data, the server performs service feature mining on the first behavior data of the target object according to a preset feature type to obtain first behavior features, wherein the first behavior features comprise whether to overstep a work area feature, whether to delay the work area feature and whether to query case features in running, screening and determining are performed according to the preset feature type, and the method is not limited in detail; if the first row data of the target object belongs to the target positioning system data, the server performs feature extraction on the first row data of the target object, and stores the extracted features into a preset database to obtain second behavior features, wherein the second behavior features comprise target positioning system density features, track synthesis features, detention location features and detention duration features, for example, the server can perform clustering calculation on the first row data of the target object by adopting a spatial clustering DBSCAN algorithm based on density noise, and the number of target positioning systems of each cluster is taken as the density of the clusters after clustering to obtain target positioning system density features, wherein the distance between the points adopts the earth surface distance, and the clustering critical value and the cluster point number are preset according to actual service requirements and are default to be 20 points in 50 meters. While the detention site feature and detention duration feature, each track is conveniently traversed through the server, a track with the speed less than 5km/h and the starting point and the end point distance of the target positioning system less than 500 m in 5 minutes is found, the first point of the track is used as the detention point by the server, and the track duration is used as the detention duration, for example, the detention feature is { behavinorfeature { "stayGps": "112.3232,28.322423", "stayDuration": "23" } }, wherein stayGps is used for indicating the detention site feature and stayDuration is used for indicating the detention duration feature; further, the server sorts the target positioning system data in the first behavior data of the target object according to the time sequence to obtain a track synthesis feature, and when the first behavior data of the new target object is not received within a preset time length, the server stores the track synthesis feature in a warehouse, wherein the preset time length can be 10 minutes; and finally, the server combines the first behavior feature and the second behavior feature, and performs relation mapping on the combined feature data and the target object identifier to obtain the multidimensional behavior feature of the target object.
203. And carrying out matching recognition on the multidimensional behavior characteristics of the target object through a preset risk recognition engine to obtain a risk rule data set, wherein the risk rule data set comprises a plurality of risk rules.
The server acquires preset service types corresponding to the multidimensional behavior characteristics of the target object; the server queries a preset rule data table according to the corresponding preset service types to obtain a risk rule data set, wherein the risk rule data set comprises a plurality of risk rules, such as a risk rule of whether to delay or not and a risk rule of whether to shift on time.
204. And respectively calling a plurality of preset risk assessment models according to a plurality of risk rules, so that the plurality of preset risk assessment models carry out risk assessment on the multidimensional behavior characteristics of the target object to obtain a plurality of risk scores or a plurality of risk grades, and setting the plurality of risk scores or the plurality of risk grades as a plurality of assessment results.
The server calls a plurality of preset risk assessment models according to a plurality of risk rules respectively, so that the plurality of preset risk assessment models carry out risk assessment on the multidimensional behavior characteristics of the target object to obtain a plurality of risk scores; further, the server determines a plurality of risk levels according to the risk scores, and sets the plurality of risk scores or the plurality of risk levels as a plurality of evaluation results. For example, when the checking-in device uploads the attendance data once, the server transmits the target object identifier userId to the preset risk recognition engine, the preset risk recognition engine determines that the corresponding risk rule is a rule for judging whether the corresponding risk rule is late, the server searches a shift table and a geographical boundary of a dispatching area on the same day through the corresponding preset risk evaluation model and the target object identifier userId, acquires the checking-in positioning data from the preset database according to the target object identifier userId, performs space containing operation according to the checking-in positioning data and the boundary of the dispatching area, compares the checking-in time with the shift table, and determines that the risk is late if the checking-in condition that the checking-in time is not before a certain shift in the dispatching area is met, namely, an evaluation result is obtained, and the evaluation result can be marked by adopting a risk score or a risk grade, for example, the risk grade is 3, and the risk score is 10.
Optionally, the server acquires a behavior risk scene according to a preset service requirement, and sets a behavior risk standard for the behavior risk scene; the server acquires sample data, trains an initial behavior risk model based on the sample data and the formulated behavior risk standard, and obtains a preset behavior risk model; the server adds a preset behavior risk model into the preset risk recognition engine, configures corresponding risk rules for the preset behavior risk model, and the preset behavior risk model is used for receiving the call of the preset risk recognition engine and outputting a corresponding evaluation result.
205. When detecting that at least one evaluation result has risks, acquiring second behavior data of the target object, processing the first behavior data of the target object and the second behavior data of the target object through a preset two-dimensional map application, and generating and displaying a target visual interface, wherein the second behavior data is generated before the first behavior data is generated, and the target visual interface is used for displaying the first behavior data and/or risk behaviors represented by the second behavior data in a two-dimensional map mode.
Specifically, when detecting that at least one evaluation result has risk, the server acquires second behavior data of the target object according to the target object identifier, wherein the second behavior data is generated before the first behavior data is generated; the server performs data encapsulation on the first behavior data of the target object and the second behavior data of the target object according to a second preset data format to obtain encapsulated data, and further, the server extracts a plurality of target positioning system data from the first behavior data of the target object and the second behavior data of the target object, wherein each of the plurality of target positioning system data comprises longitude and latitude coordinates, and the server performs data encapsulation based on the longitude and latitude coordinates, and the second preset data format is a geospatial information data exchange format GeoJSON based on a script object representation, namely, a format for encoding various geographic data structures; and the server calls a preset two-dimensional map application to conduct layer rendering and display on the packaged data to obtain a target visual interface, wherein the target visual interface is used for displaying the first behavior data and/or the risk behavior represented by the second behavior data of the target object in a two-dimensional map mode. For example, the two-dimensional map is used to display the vehicle track information of the target person A when the vehicle insurance service is processed by 2020-03-03, and the vehicle track information has the risk of not conforming to the preset running path for processing the vehicle insurance service. Further, the server optimizes the preset risk identification engine through a preset confirmation mechanism, so that the risk identification efficiency is improved rapidly.
206. And obtaining a target picture, and combining the multidimensional behavior characteristics of the target object, the multiple evaluation results and the target picture based on a preset template to obtain a risk evaluation report, wherein the target picture is a picture containing a target visual interface.
Specifically, the server receives a target picture sent by the terminal, and stores the target picture into a preset catalog, wherein the target picture is a picture containing a target visual interface; the server reads the multidimensional behavior characteristics and a plurality of evaluation results of the target object from a preset database according to the target object identification; and the server combines the multidimensional behavior characteristics of the target object, the multiple evaluation results and the target picture based on a preset template to obtain a risk evaluation report.
207. And sending the risk assessment report to the target terminal according to a preset mode, wherein the preset mode comprises a mail mode and a message pushing mode.
Specifically, the server queries from a preset database according to the target object identifier to obtain the terminal identifier of the target terminal; the server determines a preset mode according to the terminal identification of the target terminal, wherein the preset mode comprises a mail mode and a message pushing mode; and the server sends the risk assessment report to the target terminal according to the preset mode and the terminal identification.
In the embodiment of the invention, the risk assessment model is decoupled from the risk scene by the risk identification engine, and the multidimensional behavior characteristics of the target object are identified and assessed, so that the risk identification rule has expandability, the risk identification efficiency is improved, in addition, the risk behavior of the target object is intuitively displayed by the visualized application based on the two-dimensional map, and the intuitiveness and rapidness of behavior risk identification are improved. This scheme belongs to wisdom security protection field, can promote the construction in wisdom city through this scheme.
The foregoing describes a method for visualizing behavior risk identification in the embodiment of the present invention, and the following describes a visualization device for behavior risk identification in the embodiment of the present invention, referring to fig. 3, an embodiment of the visualization device for behavior risk identification in the embodiment of the present invention includes:
the acquisition module 301 is configured to acquire first behavior data and identification data of a target object from a terminal of a preset embedding point mechanism, where the identification data includes an embedding point identifier and a target object identifier;
the mining module 302 is configured to perform data mining on the first row of data of the target object according to the buried point identifier, the target object identifier and the preset feature type, so as to obtain a multidimensional behavior feature of the target object;
The evaluation module 303 is configured to perform risk evaluation on the multidimensional behavior feature of the target object through a preset risk recognition engine, so as to obtain a plurality of evaluation results;
the display module 304 is configured to obtain second behavior data of the target object when detecting that at least one evaluation result has a risk, and process, through a preset two-dimensional map application, the first behavior data of the target object and the second behavior data of the target object, and generate and display a target visual interface, where the second behavior data is generated before the first behavior data is generated, and the target visual interface is configured to display, according to a two-dimensional map, the first behavior data and/or a risk behavior represented by the second behavior data.
In the embodiment of the invention, the risk assessment model is decoupled from the risk scene by the risk identification engine, and the multidimensional behavior characteristics of the target object are identified and assessed, so that the risk identification rule has expandability, the risk identification efficiency is improved, in addition, the risk behavior of the target object is intuitively displayed by the visualized application based on the two-dimensional map, and the intuitiveness and rapidness of behavior risk identification are improved. The scheme can be applied to the field of intelligent security, so that the construction of a smart city is promoted.
Referring to fig. 4, another embodiment of a visualization device for identifying a risk of behavior in an embodiment of the present invention includes:
the acquisition module 301 is configured to acquire first behavior data and identification data of a target object from a terminal of a preset buried point mechanism, where the identification data includes a buried point identifier and a target object identifier;
the mining module 302 is configured to perform data mining on the first row of data of the target object according to the buried point identifier, the target object identifier and the preset feature type, so as to obtain a multidimensional behavior feature of the target object;
the evaluation module 303 is configured to perform risk evaluation on the multidimensional behavior feature of the target object through a preset risk recognition engine, so as to obtain a plurality of evaluation results;
the display module 304 is configured to obtain second behavior data of the target object when detecting that at least one evaluation result has a risk, and process, through a preset two-dimensional map application, the first behavior data of the target object and the second behavior data of the target object, and generate and display a target visual interface, where the second behavior data is generated before the first behavior data is generated, and the target visual interface is configured to display, according to a two-dimensional map, the first behavior data and/or a risk behavior represented by the second behavior data.
Optionally, the acquisition module 301 may be further specifically configured to:
receiving buried point data sent by a terminal of a preset buried point mechanism, wherein the terminal of the preset buried point mechanism comprises a mobile phone, a vehicle-mounted automatic diagnosis device and attendance checking equipment;
and carrying out data analysis on the buried data according to a first preset data format to obtain first behavior data and identification data of the target object.
Optionally, the mining module 302 may be further specifically configured to:
judging whether the first row of data of the target object belongs to the target positioning system data or not based on the buried point identification;
if the first behavior data of the target object does not belong to the target positioning system data, carrying out service feature mining on the first behavior data of the target object according to the preset feature type to obtain first behavior features;
if the first behavior data of the target object belong to the target positioning system data, extracting the characteristics of the first behavior data of the target object, and storing the extracted characteristics into a preset database to obtain second behavior characteristics, wherein the second behavior characteristics comprise target positioning system density characteristics, track synthesis characteristics and detention location characteristics;
and combining the first behavior feature and the second behavior feature, and performing relation mapping on the combined feature data and the target object identifier to obtain the multidimensional behavior feature of the target object.
Optionally, the evaluation module 303 may be further specifically configured to:
matching and identifying the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a risk rule data set, wherein the risk rule data set comprises a plurality of risk rules;
and respectively calling a plurality of preset risk assessment models according to a plurality of risk rules, so that the plurality of preset risk assessment models carry out risk assessment on the multidimensional behavior characteristics of the target object to obtain a plurality of risk scores or a plurality of risk grades, and setting the plurality of risk scores or the plurality of risk grades as a plurality of assessment results.
Optionally, the display module 304 may be further specifically configured to:
when detecting that at least one evaluation result has risk, acquiring second behavior data of the target object according to the target object identifier, wherein the second behavior data is generated before the first behavior data is generated;
data packaging is carried out on the first behavior data of the target object and the second behavior data of the target object according to a second preset data format, and packaged data are obtained;
and calling a preset two-dimensional map application to perform layer rendering on the packaged data and display the data to obtain a target visual interface, wherein the target visual interface is used for displaying the risk behaviors represented by the first behavior data and/or the second behavior data in a two-dimensional map mode.
Optionally, the visualization device for behavior risk identification further includes:
the setting module 305 is configured to obtain a behavior risk scenario according to a preset service requirement, and set a behavior risk standard for the behavior risk scenario to obtain a formulated behavior risk standard;
the training module 306 is configured to obtain sample data, and train the initial behavior risk model based on the sample data and the formulated behavior risk standard to obtain a preset behavior risk model;
the configuration module 307 is configured to add a preset behavior risk model to the preset risk recognition engine, configure a corresponding risk rule for the preset behavior risk model, and the preset behavior risk model is configured to accept the call of the preset risk recognition engine and output a corresponding evaluation result.
Optionally, the visualization device for behavior risk identification further includes:
the combination module 308 is configured to obtain a target picture, and combine the multidimensional behavior feature of the target object, the multiple evaluation results and the target picture based on a preset template to obtain a risk evaluation report, where the target picture is a picture including a target visual interface;
and the sending module 309 is configured to send the risk assessment report to the target terminal according to a preset manner, where the preset manner includes a mail manner and a message pushing manner.
In the embodiment of the invention, the risk assessment model is decoupled from the risk scene by the risk identification engine, and the multidimensional behavior characteristics of the target object are identified and assessed, so that the risk identification rule has expandability, the risk identification efficiency is improved, in addition, the risk behavior of the target object is intuitively displayed by the visualized application based on the two-dimensional map, and the intuitiveness and rapidness of behavior risk identification are improved. This scheme belongs to wisdom security protection field, can promote the construction in wisdom city through this scheme.
The above-mentioned fig. 3 and fig. 4 describe the visualization device for behavior risk recognition in the embodiment of the present invention in detail from the point of view of the modularized functional entity, and the following describes the visualization device for behavior risk recognition in the embodiment of the present invention in detail from the point of view of hardware processing.
Fig. 5 is a schematic structural diagram of a behavior risk recognition visualization device according to an embodiment of the present invention, where the behavior risk recognition visualization device 500 may be relatively different due to configuration or performance, and may include one or more processors (central processing units, CPU) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) storing application programs 533 or data 532. Wherein memory 520 and storage medium 530 may be transitory or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations in the visualization device 500 for behavior risk identification. Still further, the processor 510 may be configured to communicate with the storage medium 530 and execute a series of instruction operations in the storage medium 530 on the visualization device 500 for behavioral risk identification.
The visualization device 500 for behavioral risk identification may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input/output interfaces 560, and/or one or more operating systems 531, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the configuration of the visualization device for behavioral risk identification illustrated in fig. 5 does not constitute a limitation of the visualization device for behavioral risk identification, and may include more or fewer components than illustrated, or may combine certain components, or may be a different arrangement of components.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, which when executed on a computer, cause the computer to perform the steps of the behavior risk identification visualization method.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A method of visualizing behavioral risk identification, the method comprising:
collecting first behavior data and identification data of a target object from a terminal of a preset buried point mechanism, wherein the identification data comprises a buried point identification and a target object identification;
performing data mining on first row data of the target object according to the buried point identification, the target object identification and the preset feature type to obtain multidimensional behavior features of the target object;
performing risk assessment on the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a plurality of assessment results;
when detecting that at least one evaluation result has risks, acquiring second behavior data of a target object, and processing the first behavior data of the target object and the second behavior data of the target object through a preset two-dimensional map application to generate and display a target visual interface, wherein the second behavior data is generated before the first behavior data is generated, and the target visual interface is used for displaying the risk behaviors represented by the first behavior data and/or the second behavior data in a two-dimensional map mode;
The data mining of the first behavior data of the target object according to the buried point identifier, the target object identifier and the preset feature type is performed to obtain the multidimensional behavior feature of the target object, and the data mining method comprises the following steps:
judging whether the first line data of the target object belongs to target positioning system data or not based on the buried point identification;
if the first behavior data of the target object does not belong to the target positioning system data, carrying out service feature mining on the first behavior data of the target object according to the preset feature type to obtain first behavior features;
if the first behavior data of the target object belong to the target positioning system data, extracting the characteristics of the first behavior data of the target object, and storing the extracted characteristics into a preset database to obtain second behavior characteristics, wherein the second behavior characteristics comprise target positioning system density characteristics, track synthesis characteristics and detention location characteristics;
and combining the first behavior feature and the second behavior feature, and performing relation mapping on the combined feature data and the target object identifier to obtain the multidimensional behavior feature of the target object.
2. The visualization method for behavior risk identification according to claim 1, wherein the collecting the first behavior data and the identification data of the target object from the terminal of the preset buried point mechanism includes:
receiving buried point data sent by a terminal of a preset buried point mechanism, wherein the terminal of the preset buried point mechanism comprises a mobile phone, a vehicle-mounted automatic diagnosis device and attendance checking equipment;
and carrying out data analysis on the buried data according to a first preset data format to obtain first behavior data and identification data of the target object.
3. The method for visualizing behavior risk recognition according to claim 1, wherein performing risk assessment on the multidimensional behavior feature of the target object by a preset risk recognition engine to obtain a plurality of assessment results comprises:
matching and identifying the multidimensional behavior characteristics of the target object through a preset risk identification engine to obtain a risk rule data set, wherein the risk rule data set comprises a plurality of risk rules;
and respectively calling a plurality of preset risk assessment models according to the plurality of risk rules, so that the plurality of preset risk assessment models carry out risk assessment on the multidimensional behavior characteristics of the target object to obtain a plurality of risk scores or a plurality of risk grades, and setting the plurality of risk scores or the plurality of risk grades as a plurality of assessment results.
4. The visualization method for behavior risk identification according to claim 1, wherein when at least one evaluation result is detected to be at risk, second behavior data of a target object is obtained, the first behavior data of the target object and the second behavior data of the target object are processed through a preset two-dimensional map application, and a target visualization interface is generated and displayed, wherein the second behavior data is generated before the first behavior data is generated, and the target visualization interface is used for displaying the first behavior data and/or risk behaviors characterized by the second behavior data in a two-dimensional map manner, and the method comprises:
when detecting that the at least one evaluation result has risk, acquiring second behavior data of a target object according to the target object identifier, wherein the second behavior data is generated before the first behavior data is generated;
data packaging is carried out on the first behavior data of the target object and the second behavior data of the target object according to a second preset data format, and packaged data are obtained;
and calling a preset two-dimensional map application to perform layer rendering on the packaged data and display the packaged data to obtain a target visual interface, wherein the target visual interface is used for displaying the first behavior data and/or the risk behavior represented by the second behavior data in a two-dimensional map mode.
5. The visualization method for behavioral risk identification according to any one of claims 1-4, wherein, when the risk of at least one evaluation result is detected, second behavioral data of a target object is acquired, and the first behavioral data of the target object and the second behavioral data of the target object are processed by a preset two-dimensional map application to generate and display a target visualization interface, wherein the second behavioral data is generated before the first behavioral data is generated, and the target visualization interface is used to display the first behavioral data and/or the risk behaviors characterized by the second behavioral data in a two-dimensional map manner, and the visualization method for behavioral risk identification further comprises:
acquiring a behavior risk scene according to preset business requirements, and setting a behavior risk standard for the behavior risk scene to obtain a formulated behavior risk standard;
acquiring sample data, training an initial behavior risk model based on the sample data and the formulated behavior risk standard, and obtaining a preset behavior risk model;
adding the preset behavior risk model to the preset risk recognition engine, configuring a corresponding risk rule for the preset behavior risk model, wherein the preset behavior risk model is used for receiving the call of the preset risk recognition engine and outputting a corresponding evaluation result.
6. The visualization method for behavioral risk identification according to any one of claims 1-4, wherein, when the risk of at least one evaluation result is detected, second behavioral data of a target object is acquired, and the first behavioral data of the target object and the second behavioral data of the target object are processed by a preset two-dimensional map application to generate and display a target visualization interface, wherein the second behavioral data is generated before the first behavioral data is generated, and the target visualization interface is used for displaying the first behavioral data and/or the risk behaviors characterized by the second behavioral data in a two-dimensional map manner, and the visualization method for behavioral risk identification further comprises:
acquiring a target picture, and combining the multidimensional behavior characteristics of the target object, the multiple evaluation results and the target picture based on a preset template to obtain a risk evaluation report, wherein the target picture is a picture containing the target visual interface;
and sending the risk assessment report to a target terminal according to a preset mode, wherein the preset mode comprises a mail mode and a message pushing mode.
7. A behavior risk recognition visualization device, characterized in that the behavior risk recognition visualization device comprises:
the acquisition module is used for acquiring first behavior data and identification data of a target object from a terminal of a preset buried point mechanism, wherein the identification data comprises a buried point identification and a target object identification;
the mining module is used for carrying out data mining on the first row of data of the target object according to the buried point identification, the target object identification and the preset feature type to obtain multidimensional behavior features of the target object;
the evaluation module is used for performing risk evaluation on the multidimensional behavior characteristics of the target object through a preset risk recognition engine to obtain a plurality of evaluation results;
the display module is used for acquiring second behavior data of a target object when detecting that at least one evaluation result has risks, and processing the first behavior data of the target object and the second behavior data of the target object through a preset two-dimensional map application to generate and display a target visual interface, wherein the second behavior data is generated before the first behavior data is generated, and the target visual interface is used for displaying the first behavior data and/or risk behaviors represented by the second behavior data in a two-dimensional map mode;
The mining module is specifically used for:
judging whether the first line data of the target object belongs to target positioning system data or not based on the buried point identification;
if the first behavior data of the target object does not belong to the target positioning system data, carrying out service feature mining on the first behavior data of the target object according to the preset feature type to obtain first behavior features;
if the first behavior data of the target object belong to the target positioning system data, extracting the characteristics of the first behavior data of the target object, and storing the extracted characteristics into a preset database to obtain second behavior characteristics, wherein the second behavior characteristics comprise target positioning system density characteristics, track synthesis characteristics and detention location characteristics;
and combining the first behavior feature and the second behavior feature, and performing relation mapping on the combined feature data and the target object identifier to obtain the multidimensional behavior feature of the target object.
8. The behavioral risk identification visualization device of claim 7, wherein the behavioral risk identification visualization device further comprises:
the combination module is used for acquiring a target picture, and combining the multidimensional behavior characteristics of the target object, the multiple evaluation results and the target picture based on a preset template to obtain a risk evaluation report, wherein the target picture is a picture containing the target visual interface;
And the sending module is used for sending the risk assessment report to the target terminal according to a preset mode, wherein the preset mode comprises a mail mode and a message pushing mode.
9. A visualization device for behavioral risk identification, the visualization device for behavioral risk identification comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line;
the at least one processor invoking the instructions in the memory to cause the behavior risk identification visualization device to perform the behavior risk identification visualization method of any of claims 1-6.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a method of visualizing behavioural risk identification as claimed in any one of claims 1 to 6.
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