WO2021027407A1 - Procédé et appareil d'identification d'utilisateur présentant un risque, dispositif informatique et support d'informations - Google Patents

Procédé et appareil d'identification d'utilisateur présentant un risque, dispositif informatique et support d'informations Download PDF

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WO2021027407A1
WO2021027407A1 PCT/CN2020/098579 CN2020098579W WO2021027407A1 WO 2021027407 A1 WO2021027407 A1 WO 2021027407A1 CN 2020098579 W CN2020098579 W CN 2020098579W WO 2021027407 A1 WO2021027407 A1 WO 2021027407A1
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location data
location
user
preset
terminal
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PCT/CN2020/098579
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English (en)
Chinese (zh)
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丁露涛
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0609—Buyer or seller confidence or verification

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  • This application relates to the field of computer data processing, and in particular to a risk user identification method, device, computer equipment and computer-readable storage medium.
  • the embodiments of the present application provide a method, device, computer equipment, and storage medium for identifying risky users, aiming to solve the problems of low identification accuracy and slow identification speed of risky users.
  • an embodiment of the present application provides a risk user identification method, which includes:
  • the location data set corresponding to the terminal is acquired, the location data set includes the location information of at least two of the terminals;
  • centroid matches the reserved location data corresponding to the user, determining whether the centroid matches the preset risk location data
  • centroid matches the preset risk location data, it is determined that the user is a risk user and the order data corresponding to the user is determined as risk data.
  • an embodiment of the present application provides a risk user identification device, which includes:
  • the first obtaining unit is configured to obtain a location data set corresponding to the terminal if the order data sent by the user through the terminal is received, the location data set including the location information of at least two of the terminals;
  • the first clustering unit is configured to perform clustering processing on the position data set according to a preset first clustering algorithm to obtain a position data cluster corresponding to the position data set after the clustering processing;
  • the second clustering unit is configured to perform clustering processing on the position data clusters according to a preset second clustering algorithm to obtain the centroid corresponding to the position data clusters after the clustering processing;
  • the first determining unit is configured to determine whether the center of mass matches the reserved location data corresponding to the user;
  • a second determining unit configured to determine whether the center of mass matches the preset risk location data if the center of mass matches the reserved location data corresponding to the user;
  • the order determination unit is configured to determine that the user is a risk user and determine the order data corresponding to the user as risk data if the center of mass matches the preset risk location data.
  • an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes all Perform the following steps when describing the procedure:
  • the location data set corresponding to the terminal is acquired, the location data set includes the location information of at least two of the terminals;
  • centroid matches the reserved location data corresponding to the user, determining whether the centroid matches the preset risk location data
  • centroid matches the preset risk location data, it is determined that the user is a risk user and the order data corresponding to the user is determined as risk data.
  • the embodiments of the present application also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which when executed by a processor causes the processor to perform the following steps :
  • the location data set corresponding to the terminal is acquired, the location data set includes the location information of at least two of the terminals;
  • centroid matches the reserved location data corresponding to the user, determining whether the centroid matches the preset risk location data
  • centroid matches the preset risk location data, it is determined that the user is a risk user and the order data corresponding to the user is determined as risk data.
  • the position data set is clustered by the preset first clustering algorithm and the preset second clustering algorithm clustering to obtain the centroid; and then according to the centroid and the reserved position data corresponding to the user
  • the preset risk location data realizes the identification of risk users, which is not affected by human subjective factors throughout the process, which is beneficial to improve the accuracy and speed of identification of risk users.
  • FIG. 1 is a schematic flowchart of a risk user identification method provided by an embodiment of this application
  • FIG. 2 is a schematic diagram of an application scenario of a risk user identification method provided by an embodiment of this application;
  • FIG. 3 is a schematic diagram of another process of a risk user identification method provided by an embodiment of this application.
  • FIG. 4 is a schematic diagram of another process of a risk user identification method provided by an embodiment of this application.
  • FIG. 5 is another flowchart of a method for identifying risky users according to an embodiment of this application.
  • FIG. 6 is a schematic diagram of another process of a risk user identification method provided by an embodiment of this application.
  • FIG. 7 is a schematic block diagram of a risk user identification device provided by an embodiment of this application.
  • FIG. 8 is another schematic block diagram of a risk user identification device provided by an embodiment of this application.
  • FIG. 9 is another schematic block diagram of a risk user identification device provided by an embodiment of this application.
  • FIG. 10 is another schematic block diagram of a risk user identification device provided by an embodiment of this application.
  • FIG. 11 is another schematic block diagram of a risk user identification device provided by an embodiment of this application.
  • FIG. 12 is a schematic block diagram of a computer device provided by an embodiment of this application.
  • FIG. 1 is a schematic flowchart of a risk user identification method provided by an embodiment of the application.
  • the risk user identification method provided in the embodiment of the present application can be applied to the server 20.
  • the server 20 may be a server used in an enterprise to process order data for risk user identification.
  • the server 20 may be an independent server, or a server cluster composed of multiple servers.
  • the server 20 can establish a communication connection with the terminal 10 for data exchange.
  • the server 20 can establish a communication connection with the terminal 10 to receive order data sent by the terminal.
  • the terminal 10 may be an electronic terminal such as a mobile phone, a tablet computer, or a desktop computer.
  • the risk user identification method includes steps S110-S160.
  • the terminal can realize data interaction by establishing a communication connection with the server.
  • the user can send order data to the server by operating the terminal.
  • the order data may be order data of various commodities.
  • order data includes, but is not limited to: travel order data, takeaway order data, insurance order data, etc.
  • the location data set corresponding to the terminal includes location information of at least two of the terminals.
  • the acquiring of the location data set corresponding to the terminal may specifically be by acquiring multiple location information corresponding to the terminal, and the acquired set of multiple location information corresponding to the terminal is the location data set corresponding to the terminal.
  • step S110 includes but is not limited to steps S111-S112.
  • S111 If the order data sent by the user through the terminal is received, generate a location acquisition time range according to the sending time of the order data and a preset time period.
  • the preset time period can be set according to actual needs.
  • the preset time period is, for example, 7 days, 30 days, 60 days, and so on.
  • Generating the location acquisition time range according to the sending time of the order data and the preset time period specifically includes: subtracting the sending time of the order data from the preset time period to obtain the location acquisition start time; and The sending time of the order data is determined as the position acquisition end time; the time range between the position acquisition start time and the position acquisition end time is determined as the position acquisition time range.
  • Each terminal corresponds to a unique terminal identification code, and the terminal identification code is, for example, International Mobile Equipment Identity (IMEI).
  • the preset location database is used to store the acquired location information corresponding to the terminal.
  • the position information includes position coordinates and a coordinate acquisition time corresponding to the position coordinates, and the position coordinates include longitude coordinate information and latitude coordinate information.
  • the method for obtaining the location coordinates corresponding to the terminal includes, but is not limited to, a global positioning system (Global Positioning System, GPS), a mobile location base station system (Location Based Service, LBS), or a combination thereof.
  • the storage format of the position information may be "L1, L2; T"; where L1 represents longitude coordinate information, L2 represents latitude coordinate information, and T represents coordinate acquisition time.
  • the location information includes: 114.059818, 22.540215; 2019-1-14 16:52:55; among them, 114.059818 is the longitude coordinate information, 22.540215 is the latitude coordinate information, and 2019-1-1416:52:55 is the coordinate acquisition time.
  • the generating of the position data set according to the position information matching the position acquisition time range is specifically: determining whether the coordinate acquisition time corresponding to the position information in the preset position database is within the position acquisition time If the coordinate acquisition time corresponding to the position information in the preset position database is within the position acquisition time range, determine that the position information is the position information that matches the position acquisition time range; store the The location acquires location information matching the time range to form the location data set. If the coordinate acquisition time corresponding to the position information in the preset position database is not within the position acquisition time range, it is determined that the position information is position information that does not match the position acquisition time range.
  • step S210 may be further included before step S110.
  • S210 Acquire location information of the terminal according to a preset time interval, and store the location information in preset location data.
  • the preset time interval can be set according to actual needs.
  • the preset time interval is, for example, 5 minutes, 10 minutes, and 30 minutes. The smaller the preset time interval, the higher the recognition accuracy. Wherein, the preset time interval is less than or equal to the preset time period.
  • S120 Perform clustering processing on the location data set according to a preset first clustering algorithm to obtain a location data cluster corresponding to the location data set after the clustering processing.
  • the preset first clustering algorithm may be the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise, a density-based clustering method with noise).
  • the DBSCAN algorithm is a density-based spatial clustering algorithm. The algorithm divides areas with sufficient density into clusters, and finds clusters of arbitrary shapes in a noisy spatial database. The DBSCAN algorithm defines clusters as the largest collection of densely connected points.
  • the DBSCAN algorithm can operate normally.
  • the calculation parameters include the scanning radius Eps and the minimum number of points contained MinPts.
  • the scan radius Eps represents the range of the circular neighborhood centered on point P, where P is any unvisited data in the data set;
  • the minimum number of points included MinPts represents the neighborhood centered on point P The minimum number of points contained within the domain MinPts. If the number of points in the neighborhood with the point P as the center and the scanning radius Eps is not less than the minimum number of contained points MinPts, then the point P is called the core point.
  • the calculation parameters can be adjusted according to actual needs. If the minimum number of points contained in MinPts remains unchanged, and the scanning radius Eps is too large, most data points will be clustered into the same cluster; if the scanning radius Eps is too small, a cluster will be split. If the scanning radius Eps remains the same and the minimum included points MinPts is too large, it will cause the points in the same cluster to be determined as outliers. If the minimum included points MinPts is too small, a large number of core points will be found. In specific implementation, the scanning radius Eps can be set to 2 kilometers, and the minimum number of points contained MinPts can be set to 5.
  • S130 Perform clustering processing on the position data clusters according to a preset second clustering algorithm to obtain a centroid corresponding to the position data clusters after the clustering processing.
  • the preset second clustering algorithm may be the K-means algorithm (K-Means Clustering Algorithm, K-means clustering algorithm).
  • K-means algorithm uses pre-selected K objects as the initial cluster centers, and the value of K needs to be set in advance. Then calculate the distance between each object and each seed cluster center, and assign each object to the cluster center closest to it.
  • the cluster centers and the objects assigned to them represent a cluster. Once all objects have been allocated, the cluster center of each cluster will be recalculated based on the existing objects in the cluster. This process will be repeated until the termination condition is met.
  • the termination condition can be that no (or minimum number) of objects are reassigned to different clusters, or no (or minimum number) of cluster centers change again, or the sum of squared errors is locally minimum.
  • the number of the position data clusters may be one or more, and the position data clusters are clustered according to a preset second clustering algorithm to obtain the centroid corresponding to the position data clusters after the clustering processing. Specifically, clustering is performed on each position data cluster according to K-means to obtain the centroid corresponding to the position data cluster after the clustering processing. Among them, the value of K is set to 1 in advance.
  • S140 Determine whether the center of mass matches the reserved location data corresponding to the user.
  • the reserved position data corresponding to the user is position information pre-stored in the server by the user, and the reserved position data corresponding to the user includes but is not limited to home address information, office address information, and the like.
  • the preset risk location data type may be one or more, and the preset risk location data type may be determined according to the type of the order data and the preset type mapping relationship.
  • the preset type mapping relationship is used to determine the corresponding relationship between the order data type and the preset risk location data type.
  • the order data is insurance order data
  • the insurance policy type corresponding to the insurance order data is critical illness insurance.
  • the type of the preset risk location data is a hospital.
  • the centroid By judging whether the centroid matches the reserved location data corresponding to the user, it is judged whether the centroid obtained after clustering by the preset first clustering algorithm and the preset second clustering algorithm is smaller than the preset The error threshold. If the centroid matches the reserved location data corresponding to the user, it is determined that the obtained centroid is less than the preset error threshold, and risk user identification can be performed according to the obtained centroid. If the center of mass does not match the reserved position data corresponding to the user, and it is determined that the obtained center of mass is not less than the preset error threshold, a reminder message is sent to the manager to remind the manager to modify the calculation parameters to improve the obtained The accuracy of the center of mass improves the accuracy of risk user identification.
  • the preset error threshold can be set according to actual requirements, and the preset error threshold is, for example, 1 kilometer.
  • step S140 includes but is not limited to steps S141-S143.
  • Calculating the distance difference between the center of mass and the reserved position data corresponding to the user may be implemented by a first formula, and the first formula may be a Haversine formula. Wherein, the first formula is specifically:
  • S142 Determine whether the distance difference between the center of mass and the reserved location data corresponding to the user is less than a preset first difference threshold.
  • the preset first difference threshold may be set according to actual requirements, for example, the preset first difference threshold may be set to 1 km.
  • the centroid matches the reserved position data corresponding to the user. If the distance difference between the centroid and the reserved position data corresponding to the user is not less than the preset first difference threshold, it is determined that the centroid does not match the reserved position data corresponding to the user.
  • the center of mass matches the reserved location data corresponding to the user, it indicates that the center of mass obtained after clustering through the preset first clustering algorithm and the preset second clustering algorithm is less than the preset error threshold,
  • the obtained centroid has a high degree of reliability and can be used for risk user identification, so as to determine whether the centroid matches the preset risk location data.
  • step S150 includes but is not limited to steps S151-S153.
  • Calculating the distance difference between the center of mass and the preset risk location data can be implemented by a second formula, and the second formula can be a Haversine formula.
  • the second formula is specifically:
  • S152 Determine whether the distance difference between the centroid and the preset risk location data is less than a preset second difference threshold.
  • the preset second difference threshold may be set according to actual requirements, for example, the preset second difference threshold may be set to 1 km.
  • the centroid matches the preset risk location data. If the distance difference between the centroid and the preset risk location data is not less than the preset second difference threshold, it is determined that the centroid does not match the preset risk location data.
  • S160 If the center of mass matches the preset risk location data, determine that the user is a risk user and determine the order data corresponding to the user as risk data.
  • the order data is insurance order data
  • the centroid matches the preset risk location data, it indicates that the user has been active at the preset risk location (such as a hospital, etc.) before sending the insurance order data.
  • the risk of insuring with illness and then determine that the user is a risk user.
  • the order data corresponding to the user needs to be determined as risk data for subsequent monitoring or manual follow-up.
  • the order data is insurance order data
  • the user is a risk user, it indicates that the insurance order data has a high probability of fraud, and then the insurance policy corresponding to the user is determined as a risk insurance policy for reviewers to conduct Manual investigation reduces the risk of insurance policy fraud.
  • FIG. 7 is a schematic block diagram of a risk user identification device 100 provided by an embodiment of the present application. As shown in FIG. 7, corresponding to the above risk user identification method, the present application also provides a risk user identification device 100.
  • the risk user identification device 100 includes a unit for executing the above risk user identification method, and the device 100 may be configured in a server.
  • the server may be an independent server or a server cluster composed of multiple servers.
  • the device 100 includes a first obtaining unit 110, a first clustering unit 120, a second clustering unit 130, a first judging unit 140, a second judging unit, and an order determining unit 160.
  • the first obtaining unit 110 is configured to obtain a location data set corresponding to the terminal if the order data sent by the user through the terminal is received, the location data set including location information of at least two of the terminals.
  • the first obtaining unit 110 includes a first generating unit 111 and a second generating unit 112.
  • the first generating unit 111 is configured to generate a location acquisition time range according to the sending time of the order data and a preset time period if the order data sent by the user through the terminal is received.
  • the second generating unit 112 is configured to obtain the location information matching the location acquisition time range in a preset location database according to the terminal identification code corresponding to the terminal, and according to the location information matching the location acquisition time range The location information of generates the location data set.
  • the device 100 further includes a position storage unit 210.
  • the location storage unit 210 is configured to obtain location information of the terminal according to a preset time interval, and store the location information to preset location data.
  • the first clustering unit 120 is configured to perform clustering processing on the position data set according to a preset first clustering algorithm to obtain a position data cluster corresponding to the position data set after the clustering processing.
  • the second clustering unit 130 is configured to perform clustering processing on the position data clusters according to a preset second clustering algorithm to obtain the centroid corresponding to the position data clusters after the clustering processing.
  • the first determining unit 140 is configured to determine whether the user is a risk user according to the centroid, the reserved location data corresponding to the user, and preset risk location data.
  • the first judgment unit 140 includes a first calculation unit 141, a fourth judgment unit 142 and a second determination unit 143.
  • the first calculation unit 141 is configured to calculate the distance difference between the center of mass and the reserved position data corresponding to the user.
  • the fourth determining unit 142 is configured to determine whether the distance difference between the center of mass and the reserved position data corresponding to the user is less than a preset first difference threshold.
  • the second determining unit 143 is configured to determine a reservation corresponding to the centroid and the user if the distance difference between the centroid and the reserved position data corresponding to the user is less than a preset first difference threshold. The location data matches.
  • the second determining unit 150 is configured to determine whether the center of mass matches the preset risk location data if the center of mass matches the reserved location data corresponding to the user.
  • the second judgment unit 150 includes a second calculation unit 151, a fifth judgment unit 152 and a third determination unit 153.
  • the second calculation unit 151 is configured to calculate the distance difference between the center of mass and the preset risk position data if the center of mass matches the reserved position data corresponding to the user.
  • the fifth determining unit 152 is configured to determine whether the distance difference between the centroid and the preset risk location data is smaller than a preset second difference threshold.
  • the third determining unit 153 is configured to determine the center of mass and the preset risk location data if the distance difference between the center of mass and the preset risk location data is less than a preset second difference threshold. match.
  • the order determination unit 160 is configured to determine the order data corresponding to the user as risk data if the user is a risk user.
  • the above-mentioned apparatus 100 may be implemented in the form of a computer program, and the computer program may run on a computer device as shown in FIG.
  • FIG. 12 is a schematic block diagram of a computer device according to an embodiment of the present application.
  • the computer device 500 may be in a server.
  • the server may be an independent server or a server cluster composed of multiple servers.
  • the computer device 500 includes a processor 520, a memory, and a network interface 550 connected through a system bus 510, where the memory may include a non-volatile storage medium 530 and an internal memory 540.
  • the non-volatile storage medium 530 can store an operating system 531 and a computer program 532.
  • the processor 520 can execute a risk user identification method.
  • the processor 520 is used to provide calculation and control capabilities, and support the operation of the entire computer device 500.
  • the internal memory 540 provides an environment for the running of a computer program in a non-volatile storage medium.
  • the processor 520 can execute a risk user identification method.
  • the network interface 550 is used for network communication with other devices.
  • the schematic block diagram of the computer device is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied.
  • the specific computer device 500 It may include more or fewer components than shown in the figures, or combine certain components, or have a different component arrangement.
  • the processor 520 is configured to run a computer program stored in a memory to implement any embodiment of the risk user identification method described above.
  • the processor 520 may be a central processing unit (Central Processing Unit, CPU), and the processor 520 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSPs), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor.
  • the computer program may be stored in a storage medium, and the storage medium may be a computer-readable storage medium.
  • the computer program is executed by at least one processor in the computer system to implement the process steps of the foregoing method embodiment.
  • the computer-readable storage medium may be non-volatile or volatile.
  • the storage medium stores a computer program that, when executed by a processor, implements any embodiment of the risk user identification method described above.
  • the computer-readable storage medium may be a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a magnetic disk, or an optical disk, and other media that can store program codes.

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Abstract

L'invention concerne un procédé et un appareil d'identification d'utilisateur présentant un risque, un dispositif informatique et un support d'informations, appliqués au domaine du traitement de données et appartenant à la technologie de l'intelligence artificielle. Le procédé comprend les étapes suivantes : si des données de commande envoyées par un utilisateur par l'intermédiaire d'un terminal sont reçues, acquérir un ensemble de données de localisation correspondant au terminal ; réaliser un traitement de groupement sur l'ensemble de données de localisation selon un premier algorithme de groupement prédéfini pour obtenir une grappe de données de localisation correspondant à l'ensemble de données de localisation après le traitement de groupement ; effectuer un traitement de groupement sur la grappe de données de localisation selon un deuxième algorithme de groupement prédéfini pour obtenir un centroïde correspondant à la grappe de données de localisation après le traitement de groupement ; déterminer, en fonction du centroïde, des données de localisation réservées correspondant à l'utilisateur, et des données de localisation de risque prédéfinies, si l'utilisateur est un utilisateur présentant un risque ; et si l'utilisateur est un utilisateur présentant un risque, déterminer les données de commande correspondant à l'utilisateur comme étant des données risquées.
PCT/CN2020/098579 2019-08-13 2020-06-28 Procédé et appareil d'identification d'utilisateur présentant un risque, dispositif informatique et support d'informations WO2021027407A1 (fr)

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CN110689218A (zh) * 2019-08-13 2020-01-14 平安科技（深圳）有限公司 风险用户识别方法、装置、计算机设备及存储介质
CN111400663B (zh) * 2020-03-17 2022-06-14 深圳前海微众银行股份有限公司 模型训练方法、装置、设备及计算机可读存储介质
CN111553383A (zh) * 2020-03-30 2020-08-18 平安医疗健康管理股份有限公司 一种数据风险检测方法、装置及设备
CN112527936A (zh) * 2020-12-16 2021-03-19 平安科技（深圳）有限公司 灾难密度的统计方法、装置、计算机设备以及存储介质
CN112907257B (zh) * 2021-04-26 2024-03-26 中国工商银行股份有限公司 风险阈值确定方法、装置和电子设备
CN113722062A (zh) * 2021-08-10 2021-11-30 上海浦东发展银行股份有限公司 请求处理方法、装置、计算机设备和存储介质
CN114049946A (zh) * 2021-11-24 2022-02-15 北京京东拓先科技有限公司 订单处理方法和装置

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