Risk area generation method and device, computer equipment and storage medium

Abstract

The invention belongs to the field of artificial intelligence and financial science and technology, and relates to a risk area generation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining target claim settlement case data in a historical time period, and carrying out the preprocessing of the claim settlement case data, and obtaining sample data; performing feature extraction on the sample data to obtain feature matrix data; obtaining geographic data corresponding to the administrative division; performing data laying processing on the feature matrix data based on geographic data to obtain target feature data; performing iterative clustering processing on the target feature data based on a clustering algorithm to obtain a clustering result; obtaining a target clustering center point from the clustering result and a target clustering radius corresponding to the target clustering center point; and generating a target risk area based on the target clustering center point and the target clustering radius. In addition, the target risk area can be stored in the block chain. According to the invention, the processing efficiency of risk area division is improved, and the accuracy of risk area division is ensured.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence development technology and financial technology, and in particular to methods, devices, computer equipment and storage media for generating risk areas. Background Art

[0002] In the field of insurance claims, with the continuous expansion of insurance business and the sharp increase in the number of cases, the efficiency and accuracy of the claims system have become the focus of insurance companies. Among them, the location of the case is one of the key information for claims processing, and its geographical attributes have an important impact on the nature of the case and the judgment of the compensation conclusion. However, the traditional claims operation model has many disadvantages when dealing with a large number of cases, especially when facing a group of cases with similar geographical characteristics and case nature.

[0003] In the existing technology, the identification and handling of cases with regional risks mainly rely on manual delineation of risk areas. Although this method can achieve centralized management and risk warning of cases in a specific area to a certain extent, it has significant shortcomings: First, for cases occurring in similar locations, if there is no automatic identification and classification mechanism, each case needs to be handled one by one according to the standard process, which not only consumes a lot of manpower and material resources, but may also lead to fraudulent claims due to untimely or improper handling, causing economic losses and reputation damage to the company; second, when manually dividing risk areas, it is often difficult to accurately define the boundaries of the area, resulting in some cases being misclassified or omitted, which in turn leads to low accuracy in the division of risk areas. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a method, device, computer equipment and storage medium for generating risk areas, so as to solve the technical problems of low processing efficiency and low accuracy in the division of risk areas in the existing method of manually defining risk areas in the field of insurance claims.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a method for generating a risk area, which adopts the following technical solution:

[0006] Obtain target claims case data within a historical time period, and pre-process the claims case data to obtain sample data; wherein the claims case data is closed and the payment conclusion is risk rejection;

[0007] Performing feature extraction on the sample data to obtain corresponding feature matrix data;

[0008] Obtain geographic data corresponding to administrative divisions;

[0009] Performing data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data;

[0010] Performing iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results;

[0011] Obtaining a corresponding target cluster center point and a target cluster radius corresponding to the target cluster center point from the clustering result;

[0012] A corresponding target risk area is generated based on the target cluster center point and the target cluster radius.

[0013] Furthermore, the step of performing data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data specifically includes:

[0014] Call preset geographic information processing tools;

[0015] Obtaining the danger location in the feature matrix data;

[0016] Based on the geographic information processing tool, spatially connect the accident location in the feature matrix data with the geographic data to obtain corresponding processed data;

[0017] The processed data is used as the target feature data.

[0018] Furthermore, the step of performing iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain a corresponding clustering result specifically includes:

[0019] Determine a target clustering algorithm from a plurality of preset clustering algorithms;

[0020] Determining the number of cluster centers based on the administrative divisions;

[0021] Selecting a plurality of initial cluster centers matching the number of cluster centers from the target feature data based on a preset initialization strategy;

[0022] Based on the target clustering algorithm and the initial clustering center, the target feature data is iteratively clustered to obtain the corresponding clustering result.

[0023] Furthermore, the step of generating a corresponding target risk area based on the target cluster center point and the target cluster radius specifically includes:

[0024] Taking the target cluster center point as the center of the circle and the target cluster radius corresponding to the cluster center as the radius, drawing a first risk area of ​​a specified shape;

[0025] Adjusting the risk area based on the geographic data to obtain a corresponding second risk area;

[0026] The second risk area is used as the target risk area.

[0027] Furthermore, after the step of generating a corresponding target risk area based on the target cluster center point and the target cluster radius, the method further includes:

[0028] Determine whether a newly entered designated claim case has been received;

[0029] If yes, obtain the accident location information of the designated claim case;

[0030] Determine whether the accident location information is within a preset risk area set;

[0031] If so, selecting a designated risk area corresponding to the risk location information from the risk area set;

[0032] Obtain the historical number of claims rejection cases in the specified risk area;

[0033] Determining whether the number of historical claims rejection cases is greater than a preset number threshold;

[0034] If so, it is determined that the designated claim case has a fraud risk, and a fraud risk assessment result corresponding to the designated claim case is generated.

[0035] Furthermore, after the step of generating the fraud risk assessment result corresponding to the designated claim case, the method further includes:

[0036] Obtain risk type data of all historical claim rejection cases in the specified risk area;

[0037] Perform risk type analysis on the risk type data, and calculate the designated risk type with the highest proportion;

[0038] Get pre-set risk advice templates;

[0039] Generating a designated risk recommendation corresponding to the designated claim case based on the designated risk type and the risk recommendation template;

[0040] Send the specified risk recommendations to relevant processing personnel.

[0041] Furthermore, after the step of generating a corresponding target risk area based on the target cluster center point and the target cluster radius, the method further includes:

[0042] Based on the preset scheduled tasks, query the newly closed claims case data from the specified database;

[0043] Generate corresponding designated sample data based on the newly closed claims case data and the target claims case data;

[0044] Get the preset clustering update strategy;

[0045] Performing cluster analysis on the specified sample data based on the cluster update strategy to obtain corresponding cluster analysis results;

[0046] The cluster analysis results are stored.

[0047] In order to solve the above technical problems, the embodiment of the present application further provides a device for generating a risk area, which adopts the following technical solution:

[0048] The first processing module is used to obtain target claim case data within a historical time period and pre-process the claim case data to obtain sample data; wherein the claim case data is the claim case data that has been closed and the payment conclusion is risk rejection;

[0049] An extraction module, used to extract features from the sample data to obtain corresponding feature matrix data;

[0050] A first acquisition module is used to acquire geographic data corresponding to administrative divisions;

[0051] A second processing module is used to perform data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data;

[0052] A clustering module, used to perform iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results;

[0053] A second acquisition module, used to acquire the corresponding target cluster center point and the target cluster radius corresponding to the target cluster center point from the clustering result;

[0054] The first generating module is used to generate a corresponding target risk area based on the target cluster center point and the target cluster radius.

[0055] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0056] Obtain target claims case data within a historical time period, and pre-process the claims case data to obtain sample data; wherein the claims case data is closed and the payment conclusion is risk rejection;

[0057] Performing feature extraction on the sample data to obtain corresponding feature matrix data;

[0058] Obtain geographic data corresponding to administrative divisions;

[0059] Performing data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data;

[0060] Performing iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results;

[0061] Obtaining a corresponding target cluster center point and a target cluster radius corresponding to the target cluster center point from the clustering result;

[0062] A corresponding target risk area is generated based on the target cluster center point and the target cluster radius.

[0063] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0064] Obtain target claims case data within a historical time period, and pre-process the claims case data to obtain sample data; wherein the claims case data is closed and the payment conclusion is risk rejection;

[0065] Performing feature extraction on the sample data to obtain corresponding feature matrix data;

[0066] Obtain geographic data corresponding to administrative divisions;

[0067] Performing data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data;

[0068] Performing iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results;

[0069] Obtaining a corresponding target cluster center point and a target cluster radius corresponding to the target cluster center point from the clustering result;

[0070] A corresponding target risk area is generated based on the target cluster center point and the target cluster radius.

[0071] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0072] The present application first obtains target claims case data within a historical time period, and pre-processes the claims case data to obtain sample data; then performs feature extraction on the sample data to obtain corresponding feature matrix data; then obtains geographic data corresponding to the administrative division; and performs data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data; subsequently, iteratively clusters the target feature data based on a preset clustering algorithm to obtain corresponding clustering results; further obtains the corresponding target cluster center point from the clustering results, as well as the target cluster radius corresponding to the target cluster center point; finally, generates the corresponding target risk area based on the target cluster center point and the target cluster radius. The present application obtains target claims case data within a historical time period, pre-processes the claims case data to obtain sample data, then performs feature extraction on the sample data to obtain feature matrix data, and then performs data overlay processing on the feature matrix data based on geographic data to obtain target feature data. Subsequently, the target feature data is iteratively clustered based on a preset clustering algorithm to obtain clustering results. Finally, according to the target cluster center point and target cluster radius in the obtained clustering results, the corresponding target risk area can be accurately generated, which effectively improves the processing efficiency of risk area division and ensures the accuracy of risk area division. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0074] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0075] Figure 2 A flowchart of an embodiment of a method for generating a risk area according to the present application;

[0076] Figure 3 is a structural schematic diagram of an embodiment of a device for generating a risk area according to the present application;

[0077] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0078] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0079] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0080] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0081] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0082] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0083] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer, a desktop computer, etc.

[0084] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0085] It should be noted that the method for generating the risk area provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the device for generating the risk area is generally arranged in the server / terminal device.

[0086] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0087] Continue to refer Figure 2 , a flowchart of an embodiment of a method for generating a risk area according to the present application is shown. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted. The method for generating a risk area provided in the embodiment of the present application can be applied to any scenario where risk area generation is required, and the method for generating a risk area can be applied to products in these scenarios, for example, the generation process of risk areas in the field of finance and insurance. The method for generating a risk area includes the following steps:

[0088] Step S201, obtaining target claim case data within a historical time period, and preprocessing the claim case data to obtain sample data; wherein the claim case data is claim case data that has been closed and the payment conclusion is risk rejection.

[0089] In this embodiment, the method for generating a risk area is executed on an electronic device (eg Figure 1The server / terminal device shown in the figure) can obtain the target claim case data through a wired connection or a wireless connection. It should be pointed out that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, Wi-Fi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (Ultra Wi DeBand) connection, and other wireless connection methods currently known or to be developed in the future. The executive subject of this application is specifically a risk area generation system, or simply referred to as a system. In the claim case data generated in the claim processing process, there are a large amount of historical case closing data, and the case closing data includes the location of the accident and the final payment conclusion (normal payment, abandonment of zero settlement, risk rejection). Among them, there is no specific limitation on the time selection of the above-mentioned historical time period, for example, the past two years can be selected. The above-mentioned claim case data is the claim case data that has been closed and the payment conclusion is risk rejection. In addition, the above-mentioned preprocessing may include missing value filling processing, outlier processing, etc. By preprocessing the claim case data, the data accuracy of the obtained sample data can be guaranteed.

[0090] Step S202: extract features from the sample data to obtain corresponding feature matrix data.

[0091] In this embodiment, the sample data is subjected to feature extraction to extract the corresponding key features, namely the feature matrix data. The feature matrix data may include case number, location of the accident (latitude and longitude or specific address), compensation conclusion, and risk type.

[0092] Step S203, obtaining geographic data corresponding to the administrative divisions.

[0093] In this embodiment, the above-mentioned geographic data refers to the geographic data of the national administrative divisions, including data such as regional boundaries, longitude and latitude information, etc.

[0094] Step S204: performing data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data.

[0095] In this embodiment, the specific implementation process of performing data overlay processing on the feature matrix data based on the geographic data to obtain the corresponding target feature data will be described in further detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0096] Step S205: performing iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results.

[0097] In this embodiment, the above-mentioned specific implementation process of iteratively clustering the target feature data based on a preset clustering algorithm to obtain the corresponding clustering results will be further described in detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0098] Step S206, obtaining the corresponding target cluster center point and the target cluster radius corresponding to the target cluster center point from the clustering result.

[0099] In this embodiment, the above clustering results are composed of cluster centers, cluster radius, the number of claim rejection cases within the cluster, and risk types. Information extraction can be performed on the clustering results to obtain the required target cluster center and the target cluster radius corresponding to the target cluster center.

[0100] Step S207: generating a corresponding target risk area based on the target cluster center point and the target cluster radius.

[0101] In this embodiment, the specific implementation process of generating the corresponding target risk area based on the target cluster center point and the target cluster radius will be described in further detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0102] The present application first obtains target claims case data within a historical time period, and pre-processes the claims case data to obtain sample data; then performs feature extraction on the sample data to obtain corresponding feature matrix data; then obtains geographic data corresponding to the administrative division; and performs data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data; subsequently, iteratively clusters the target feature data based on a preset clustering algorithm to obtain corresponding clustering results; further obtains the corresponding target cluster center point from the clustering results, as well as the target cluster radius corresponding to the target cluster center point; finally, generates the corresponding target risk area based on the target cluster center point and the target cluster radius. The present application obtains target claims case data within a historical time period, pre-processes the claims case data to obtain sample data, then performs feature extraction on the sample data to obtain feature matrix data, and then performs data overlay processing on the feature matrix data based on geographic data to obtain target feature data. Subsequently, the target feature data is iteratively clustered based on a preset clustering algorithm to obtain clustering results. Finally, according to the target cluster center point and target cluster radius in the obtained clustering results, the corresponding target risk area can be accurately generated, which effectively improves the processing efficiency of risk area division and ensures the accuracy of risk area division.

[0103] In some optional implementations, step S204 includes the following steps:

[0104] Call preset geographic information processing tools.

[0105] In this embodiment, the geographic information processing tool may specifically adopt a GIS library.

[0106] Obtain the danger location in the feature matrix data.

[0107] In this embodiment, the feature matrix data may include key fields such as case number, location of occurrence, compensation conclusion, risk type, etc. The location of occurrence may be in latitude and longitude format.

[0108] Based on the geographic information processing tool, the accident locations in the feature matrix data are spatially connected with the geographic data to obtain corresponding processed data.

[0109] In this embodiment, the danger locations in the above-mentioned feature matrix data are spatially connected (SpatialJoin) with the above-mentioned geographic data according to the above-mentioned geographic information processing tool to ensure that each danger location can correspond to a specific administrative division or geographical location, thereby obtaining corresponding processed data.

[0110] The processed data is used as the target feature data.

[0111] The present application calls a preset geographic information processing tool; then obtains the danger location in the feature matrix data; then spatially connects the danger location in the feature matrix data with the geographic data based on the geographic information processing tool to obtain corresponding processed data; and subsequently uses the processed data as the target feature data. The present application obtains the danger location in the feature matrix data, and then spatially connects the danger location in the feature matrix data with the geographic data based on the use of the geographic information processing tool, so as to quickly and accurately complete the data overlay processing of the feature matrix data, improve the processing accuracy and processing intelligence of the data overlay processing, and ensure the data accuracy of the obtained target feature data.

[0112] In some optional implementations of this embodiment, step S205 includes the following steps:

[0113] Determine the target clustering algorithm from multiple preset clustering algorithms.

[0114] In this embodiment, the above-mentioned multiple clustering algorithms may at least include K-means algorithm, DBSCAN algorithm, FCM clustering algorithm, etc. An algorithm may be randomly selected from the multiple clustering algorithms as the above-mentioned target clustering algorithm, and preferably the K-means algorithm may be used.

[0115] The number of cluster centers is determined based on the administrative divisions.

[0116] In this embodiment, the number of institutions in the administrative division is obtained and the number of institutions in the administrative division is used as the number K of cluster centers, that is, the number of cluster centers mentioned above.

[0117] A plurality of initial cluster centers matching the number of cluster centers are selected from the target feature data based on a preset initialization strategy.

[0118] In this embodiment, the initialization strategy may adopt K-means++ or other initialization strategies. The initialization strategy may be used to randomly select K initial cluster centers from the target feature data.

[0119] Based on the target clustering algorithm and the initial clustering center, the target feature data is iteratively clustered to obtain the corresponding clustering result.

[0120] In this embodiment, the process of iterative clustering processing of the target feature data includes: in each iteration based on the target clustering algorithm, the distance from each sample point in the target feature data to the K initial cluster centers is calculated, and the sample point is assigned to the nearest cluster center. The sample points in each cluster are averaged (or other suitable centroid calculation methods are used) to obtain a new cluster centroid. The changes of the new and old cluster centroids are compared. If the change is less than a preset threshold, the iteration is stopped; otherwise, the iteration is continued until the clustering processing is completed and the corresponding clustering results are obtained. Among them, each cluster center point (latitude and longitude), cluster radius (may need to be estimated based on cluster shape and density), the number of rejected cases in the cluster and the risk type (determined by statistically analyzing the risk type distribution of cases in the cluster) can be extracted, and the obtained cluster center point, cluster radius, the number of rejected cases in the cluster and the risk type are integrated to obtain the corresponding clustering results.

[0121] The present application determines the target clustering algorithm from a plurality of preset clustering algorithms; then determines the number of cluster centers based on the administrative divisions; then selects a plurality of initial cluster centers matching the number of cluster centers from the target feature data based on a preset initialization strategy; and then performs iterative clustering processing on the target feature data based on the target clustering algorithm and the initial cluster centers to obtain the corresponding clustering results. The present application determines the number of cluster centers based on the administrative divisions, and selects a plurality of initial cluster centers matching the number of cluster centers from the target feature data based on a preset initialization strategy, and then performs iterative clustering processing on the target feature data based on the target clustering algorithm determined from a plurality of clustering algorithms and the use of the initial cluster centers, so as to achieve rapid and accurate generation of corresponding clustering results, ensure the accuracy of the obtained clustering results, and facilitate the subsequent accurate generation of corresponding target risk areas based on the obtained clustering results, thereby improving the screening accuracy of target risk areas.

[0122] In some optional implementations, step S207 includes the following steps:

[0123] The target cluster center point is used as the center of the circle, and the target cluster radius corresponding to the cluster center is used as the radius to draw a first risk area of ​​a specified shape.

[0124] In this embodiment, the specified shape may be a circle, or a more complex shape such as a polygon.

[0125] The risk area is adjusted based on the geographic data to obtain a corresponding second risk area.

[0126] In this embodiment, the obtained risk area can be superimposed with the above-mentioned geographic data to ensure accurate representation of the risk area, thereby completing the adjustment process of the risk area and obtaining the corresponding second risk area.

[0127] The second risk area is used as the target risk area.

[0128] The present application uses the target cluster center point as the center of the circle and the target cluster radius corresponding to the cluster center as the radius to draw a first risk area of ​​a specified shape; then adjusts the risk area based on the geographic data to obtain a corresponding second risk area; and subsequently uses the second risk area as the target risk area. The present application uses the target cluster center point as the center of the circle and the target cluster radius corresponding to the cluster center as the radius to draw a first risk area of ​​a specified shape; then adjusts the risk area based on the geographic data to obtain a corresponding second risk area; subsequently, the second risk area is used as the target risk area. The present application uses the target cluster center point as the center of the circle and the target cluster radius corresponding to the cluster center as the radius to draw a first risk area of ​​a specified shape; then adjusts the risk area based on the geographic data to obtain a corresponding second risk area; thereby, the corresponding target risk area can be automatically and accurately generated, the efficiency of generating the target risk area is improved, and the accuracy of the generated target risk area is ensured.

[0129] In some optional implementations, after step S207, the electronic device may further perform the following steps:

[0130] Determine whether a newly entered designated claim case has been received.

[0131] In this embodiment, the above-mentioned designated claim case is a newly received claim case to be processed.

[0132] If so, obtain the accident location information of the designated claim case.

[0133] In this embodiment, the case data of the designated claim case is obtained, and then the longitude and latitude information of the accident location, that is, the accident location information, is extracted from the case data.

[0134] Determine whether the risk location information is within a preset risk area set.

[0135] In this embodiment, the above risk area set is a set of target risk areas generated after cluster analysis of the above target claim case data. A GIS library or spatial indexing technology (such as R-tree) can be used to quickly determine whether the location information of the specified claim case is located in any defined risk area included in any of the above risk area sets.

[0136] If so, a designated risk area corresponding to the risk location information is selected from the risk area set.

[0137] In this embodiment, if the danger location information is within the risk area set, a designated risk area matching the danger location information is further selected from the risk area set. The danger location information is within the designated risk area.

[0138] Get the historical number of claim rejections for the specified risk area.

[0139] In this embodiment, the historical number of claim rejection cases in a specified risk area can be obtained by querying the information on the number of claim rejection cases in the specified risk area.

[0140] Determine whether the number of historical claims rejection cases is greater than a preset quantity threshold.

[0141] In this embodiment, there is no specific limitation on the value of the quantity threshold, which can be set according to actual risk assessment requirements. For example, the quantity threshold can be set to twice the average number of rejected claims.

[0142] If so, it is determined that the designated claim case has a fraud risk, and a fraud risk assessment result corresponding to the designated claim case is generated.

[0143] In this embodiment, the content of the fraud risk assessment result may include that the designated claim case has a fraud risk. In addition, the designated claim case may be marked as a fraud risk in the system, for example, marking the designated claim case as "having a fraud risk".

[0144] The present application determines whether a newly entered designated claim case is received; if so, obtains the information of the location of the accident of the designated claim case; then determines whether the location of the accident is within a preset risk area set; if so, screens out the designated risk area corresponding to the location of the accident from the risk area set; then obtains the number of historical claims rejection cases in the designated risk area; subsequently determines whether the number of historical claims rejection cases is greater than a preset number threshold; if so, determines that the designated claim case has a fraud risk, and generates a fraud risk assessment result corresponding to the designated claim case. When the present application receives a newly entered designated claim case, if it is detected that the location of the accident of the designated claim case is within a designated risk area in the risk area set, and it is detected that the number of historical claims rejection cases in the designated risk area is greater than a preset number threshold, it will automatically determine that the designated claim case has a fraud risk, and generate a fraud risk assessment result corresponding to the designated claim case, thereby accurately and efficiently identifying the fraud risk of the claim case, improving the efficiency and accuracy of identifying the fraud risk of the claim case, and ensuring the accuracy of the obtained fraud risk assessment result.

[0145] In some optional implementations of this embodiment, after the step of generating the fraud risk assessment result corresponding to the designated claim case, the electronic device may further perform the following steps:

[0146] Obtain risk type data for all historical claim rejection cases within the specified risk area.

[0147] In this embodiment, the risk type data of all historical claim rejection cases contained in the above-mentioned designated risk area can be retrieved from a pre-built database, and it can be ensured that the data has been classified and counted by risk type.

[0148] Perform risk type analysis on the risk type data and calculate the designated risk type with the highest proportion.

[0149] In this embodiment, the proportion of each risk type in the historical claim rejection cases in the above-mentioned designated risk area is calculated, and the risk type with the highest proportion is identified as the main risk type of the designated risk area, that is, the above-mentioned designated risk type.

[0150] Get pre-set risk proposal templates.

[0151] In this embodiment, the above-mentioned risk recommendation template is a template file constructed according to the actual risk recommendation generation requirements. There is no specific limitation on the content of the template file. By way of example, the template content of the risk recommendation template includes: "Based on historical data analysis, the most common fraud risk type in this risk area is [placeholder]. Please pay special attention to reviewing relevant evidence."

[0152] A designated risk recommendation corresponding to the designated claim case is generated based on the designated risk type and the risk recommendation template.

[0153] In this embodiment, the designated risk type can be filled into the corresponding position (placeholder) in the above-mentioned risk suggestion template, thereby generating a designated risk suggestion corresponding to the designated claim case.

[0154] Send the specified risk recommendations to relevant processing personnel.

[0155] In this embodiment, the processing personnel may be the claims personnel responsible for the above-mentioned designated claims cases. In addition, the accuracy of fraud risk identification and risk type recommendations may be reviewed regularly, and preset thresholds and analysis methods may be adjusted according to actual conditions. Feedback from claims personnel on risk type recommendations may also be collected to continuously optimize system performance.

[0156] This application obtains the risk type data of all historical claim rejection cases in the designated risk area; then performs risk type analysis on the risk type data, and counts the designated risk type with the highest proportion; then obtains a preset risk suggestion template; subsequently generates a designated risk suggestion corresponding to the designated claim case based on the designated risk type and the risk suggestion template, and sends the designated risk suggestion to the relevant processing personnel. After detecting that the designated claim case has a fraud risk, this application will also intelligently obtain the risk type data of all historical claim rejection cases in the designated risk area, and perform risk type analysis on the risk type data, and count the designated risk type with the highest proportion, and then generate a designated risk suggestion corresponding to the designated claim case based on the designated risk type and the preset risk suggestion template, so as to realize the automatic generation of designated risk suggestions and improve the generation efficiency of designated risk suggestions. The designated risk suggestions will also be sent to the relevant processing personnel to provide them with valuable risk type suggestions, thereby helping to improve the work efficiency of the processing personnel and enhance risk management capabilities.

[0157] In some optional implementations of this embodiment, after step S207, the electronic device may further perform the following steps:

[0158] Based on the preset scheduled tasks, query the newly closed claims case data from the specified database.

[0159] In this embodiment, a scheduled task can be set by using a Cron job or a scheduling framework to query the newly closed claims case data from a designated database on a regular basis every day. The designated database is a pre-built database dedicated to storing closed claims case data.

[0160] Generate corresponding designated sample data based on the newly closed claim case data and the target claim case data.

[0161] In this embodiment, the newly closed claim case data can be added to the target claim case data to obtain the corresponding designated sample data. In addition, the data structure required for data processing and cluster analysis can be updated at the same time.

[0162] Get the preset cluster update strategy.

[0163] In this embodiment, the cluster update strategy is specifically a process of generating risk areas corresponding to the above steps S201 to S207, which will not be described in detail here.

[0164] Based on the clustering update strategy, cluster analysis is performed on the designated sample data to obtain corresponding clustering analysis results.

[0165] In this embodiment, cluster analysis processing is re-executed on the designated sample data based on the policy content of the cluster update policy to obtain new clustering results and new risk areas, and the obtained new clustering results and new risk areas are used as the above cluster analysis results.

[0166] The cluster analysis results are stored.

[0167] In this embodiment, there is no specific limitation on the storage method of the above cluster analysis results, and it can be selected according to actual storage needs, for example, local database storage, cloud server storage, blockchain storage, etc. can be selected.

[0168] This application queries the newly closed claims case data from the designated database based on the preset scheduled task; then generates the corresponding designated sample data based on the newly closed claims case data and the target claims case data; then obtains the preset clustering update strategy; and performs clustering analysis on the designated sample data based on the clustering update strategy to obtain the corresponding clustering analysis results; and subsequently stores and processes the clustering analysis results. This application queries the newly closed claims case data from the designated database based on the use of scheduled tasks, and generates the corresponding designated sample data based on the newly closed claims case data and the target claims case data; then performs clustering analysis on the designated sample data based on the use of clustering update strategies to obtain the corresponding clustering analysis results, which effectively ensures the timeliness and accuracy of the obtained clustering analysis results, and the whole process is automated, without the need for manual supervision, which can effectively release the manpower maintenance costs. In addition, the clustering analysis results will also be stored to ensure the data security of the clustering analysis results.

[0169] In some optional implementations, the user information obtained is subject to the user's consent and complies with relevant laws and policies.

[0170] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0171] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned target risk areas, the above-mentioned target risk areas can also be stored in a node of a blockchain.

[0172] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0173] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0174] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0176] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0177] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a device for generating a risk area. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0178] like Figure 3 As shown, the risk area generation device 300 described in this embodiment includes: a first processing module 301 , an extraction module 302 , a first acquisition module 303 , a second processing module 304 , a clustering module 305 , a second acquisition module 306 and a first generation module 307 .

[0179] in:

[0180] The first processing module 301 is used to obtain target claim case data within a historical time period and pre-process the claim case data to obtain sample data; wherein the claim case data is the claim case data that has been closed and the payment conclusion is risk rejection;

[0181] An extraction module 302 is used to extract features from the sample data to obtain corresponding feature matrix data;

[0182] The first acquisition module 303 is used to acquire geographic data corresponding to administrative divisions;

[0183] The second processing module 304 is used to perform data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data;

[0184] The clustering module 305 is used to perform iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results;

[0185] A second acquisition module 306 is used to acquire the corresponding target cluster center point and the target cluster radius corresponding to the target cluster center point from the clustering result;

[0186] The first generating module 307 is used to generate a corresponding target risk area based on the target cluster center point and the target cluster radius.

[0187] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for generating a risk area in the above-mentioned embodiment, and are not described in detail here.

[0188] In some optional implementations of this embodiment, the second processing module 304 includes:

[0189] The calling submodule is used to call the preset geographic information processing tools;

[0190] An acquisition submodule, used to acquire the danger location in the feature matrix data;

[0191] A processing submodule, for spatially connecting the danger locations in the feature matrix data with the geographic data based on the geographic information processing tool to obtain corresponding processed data;

[0192] The first determining submodule is used to use the processed data as the target feature data.

[0193] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for generating a risk area in the above-mentioned embodiment, and are not described in detail here.

[0194] In some optional implementations of this embodiment, the clustering module 305 includes:

[0195] A second determination submodule is used to determine a target clustering algorithm from a plurality of preset clustering algorithms;

[0196] A third determination submodule is used to determine the number of cluster centers based on the administrative divisions;

[0197] A selection submodule, configured to select a plurality of initial cluster centers matching the number of cluster centers from the target feature data based on a preset initialization strategy;

[0198] The clustering submodule is used to perform iterative clustering processing on the target feature data based on the target clustering algorithm and the initial clustering center to obtain the corresponding clustering result.

[0199] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for generating a risk area in the above-mentioned embodiment, and are not described in detail here.

[0200] In some optional implementations of this embodiment, the first generating module 307 includes:

[0201] A drawing submodule, used to draw a first risk area of ​​a specified shape by taking the target cluster center point as the center of the circle and the target cluster radius corresponding to the cluster center as the radius;

[0202] An adjustment submodule, configured to adjust the risk area based on the geographic data to obtain a corresponding second risk area;

[0203] The third determination submodule is configured to take the second risk area as the target risk area.

[0204] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for generating a risk area in the above-mentioned embodiment, and are not described in detail here.

[0205] In some optional implementations of this embodiment, the device for generating the risk area further includes:

[0206] The first judgment module is used to judge whether a newly entered designated claim case has been received;

[0207] A third acquisition module is used to obtain the location information of the designated claim case if yes;

[0208] The second judgment module is used to judge whether the risk location information is within a preset risk area set;

[0209] A screening module, for screening out, from the risk area set, a designated risk area corresponding to the risk location information;

[0210] A fourth acquisition module is used to obtain the historical amount of claim rejection cases in the designated risk area;

[0211] The third judgment module is used to judge whether the number of historical claims rejection cases is greater than a preset number threshold;

[0212] The second generating module is used to determine if the designated claim case has a fraud risk and generate a fraud risk assessment result corresponding to the designated claim case.

[0213] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for generating a risk area in the above-mentioned embodiment, and are not described in detail here.

[0214] In some optional implementations of this embodiment, the device for generating the risk area further includes:

[0215] A fifth acquisition module is used to acquire risk type data of all historical claim rejection cases in the designated risk area;

[0216] A first analysis module is used to perform risk type analysis on the risk type data and calculate the designated risk type with the highest proportion;

[0217] The sixth acquisition module is used to obtain a preset risk recommendation template;

[0218] A third generating module, configured to generate a specified risk suggestion corresponding to the specified claim case based on the specified risk type and the risk suggestion template;

[0219] The sending module is used to send the specified risk suggestion to relevant processing personnel.

[0220] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for generating a risk area in the above-mentioned embodiment, and are not described in detail here.

[0221] In some optional implementations of this embodiment, the device for generating the risk area further includes:

[0222] A judgment module, used to judge whether a business behavior operation on the target page triggered by the user is received;

[0223] The query module is used to query the newly closed claims case data from the specified database based on the preset scheduled tasks;

[0224] A fourth generating module, configured to generate corresponding designated sample data based on the newly closed claim case data and the target claim case data;

[0225] A seventh acquisition module, used to acquire a preset clustering update strategy;

[0226] A second analysis module, used to perform cluster analysis on the specified sample data based on the cluster update strategy to obtain corresponding cluster analysis results;

[0227] A storage module is used to store and process the cluster analysis results.

[0228] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for generating a risk area in the above-mentioned embodiment, and are not described in detail here.

[0229] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0230] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital processors (DSPs), embedded devices, etc.

[0231] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0232] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the method for generating risk areas, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0233] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the method for generating the risk area.

[0234] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0235] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0236] In an embodiment of the present application, target claims case data within a historical time period is acquired, and the claims case data is preprocessed to obtain sample data, and then feature extraction is performed on the sample data to obtain feature matrix data, and then data overlay processing is performed on the feature matrix data based on geographic data to obtain target feature data, and then the target feature data is iteratively clustered based on a preset clustering algorithm to obtain a clustering result, and finally, according to the target cluster center point and target cluster radius in the obtained clustering result, the corresponding target risk area can be accurately generated, which effectively improves the processing efficiency of risk area division and ensures the accuracy of risk area division.

[0237] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the risk area generation method as described above.

[0238] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0239] In an embodiment of the present application, target claims case data within a historical time period is acquired, and the claims case data is preprocessed to obtain sample data, and then feature extraction is performed on the sample data to obtain feature matrix data, and then data overlay processing is performed on the feature matrix data based on geographic data to obtain target feature data, and then the target feature data is iteratively clustered based on a preset clustering algorithm to obtain a clustering result, and finally, according to the target cluster center point and target cluster radius in the obtained clustering result, the corresponding target risk area can be accurately generated, which effectively improves the processing efficiency of risk area division and ensures the accuracy of risk area division.

[0240] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0241] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for generating a risk area, characterized in that: The steps include: Obtain target claims case data within a historical time period, and pre-process the claims case data to obtain sample data; wherein the claims case data is closed and the payment conclusion is risk rejection; Performing feature extraction on the sample data to obtain corresponding feature matrix data; Obtain geographic data corresponding to administrative divisions; Performing data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data; Performing iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results; Obtaining a corresponding target cluster center point and a target cluster radius corresponding to the target cluster center point from the clustering result; A corresponding target risk area is generated based on the target cluster center point and the target cluster radius.

2. The method for generating a risk area according to claim 1, characterized in that: The step of performing data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data specifically includes: Call preset geographic information processing tools; Obtaining the danger location in the feature matrix data; Based on the geographic information processing tool, spatially connect the accident location in the feature matrix data with the geographic data to obtain corresponding processed data; The processed data is used as the target feature data.

3. The method for generating a risk area according to claim 1, characterized in that: The step of performing iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain a corresponding clustering result specifically includes: Determine a target clustering algorithm from a plurality of preset clustering algorithms; Determining the number of cluster centers based on the administrative divisions; Selecting a plurality of initial cluster centers matching the number of cluster centers from the target feature data based on a preset initialization strategy; Based on the target clustering algorithm and the initial clustering center, the target feature data is iteratively clustered to obtain the corresponding clustering result.

4. The method for generating a risk area according to claim 1, characterized in that: The step of generating a corresponding target risk area based on the target cluster center point and the target cluster radius specifically includes: Taking the target cluster center point as the center of the circle and the target cluster radius corresponding to the cluster center as the radius, drawing a first risk area of ​​a specified shape; Adjusting the risk area based on the geographic data to obtain a corresponding second risk area; The second risk area is used as the target risk area.

5. The method for generating a risk area according to claim 1, characterized in that: After the step of generating a corresponding target risk area based on the target cluster center point and the target cluster radius, the method further includes: Determine whether a newly entered designated claim case has been received; If yes, obtain the accident location information of the designated claim case; Determine whether the accident location information is within a preset risk area set; If so, selecting a designated risk area corresponding to the risk location information from the risk area set; Obtain the historical number of claims rejection cases in the specified risk area; Determining whether the number of historical claims rejection cases is greater than a preset number threshold; If so, it is determined that the designated claim case has a fraud risk, and a fraud risk assessment result corresponding to the designated claim case is generated.

6. The method for generating a risk area according to claim 5, characterized in that: After the step of generating the fraud risk assessment result corresponding to the designated claim case, the method further includes: Obtain risk type data of all historical claim rejection cases in the specified risk area; Perform risk type analysis on the risk type data, and calculate the designated risk type with the highest proportion; Get pre-set risk advice templates; Generating a designated risk recommendation corresponding to the designated claim case based on the designated risk type and the risk recommendation template; Send the specified risk recommendations to relevant processing personnel.

7. The method for generating a risk area according to claim 1, characterized in that: After the step of generating a corresponding target risk area based on the target cluster center point and the target cluster radius, the method further includes: Based on the preset scheduled tasks, query the newly closed claims case data from the specified database; Generate corresponding designated sample data based on the newly closed claims case data and the target claims case data; Get the preset clustering update strategy; Performing cluster analysis on the specified sample data based on the cluster update strategy to obtain corresponding cluster analysis results; The cluster analysis results are stored.

8. A device for generating a risk area, characterized in that: include: The first processing module is used to obtain target claim case data within a historical time period and pre-process the claim case data to obtain sample data; wherein the claim case data is the claim case data that has been closed and the payment conclusion is risk rejection; An extraction module, used to extract features from the sample data to obtain corresponding feature matrix data; A first acquisition module is used to acquire geographic data corresponding to administrative divisions; A second processing module is used to perform data overlay processing on the feature matrix data based on the geographic data to obtain corresponding target feature data; A clustering module, used to perform iterative clustering processing on the target feature data based on a preset clustering algorithm to obtain corresponding clustering results; A second acquisition module, used to acquire the corresponding target cluster center point and the target cluster radius corresponding to the target cluster center point from the clustering result; The first generating module is used to generate a corresponding target risk area based on the target cluster center point and the target cluster radius.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the method for generating a risk area according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the method for generating a risk area according to any one of claims 1 to 7 are implemented.

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