WO2019061992A1 - Procédé d'optimisation de grille d'investigation, dispositif électronique et support d'informations lisible par ordinateur - Google Patents

Procédé d'optimisation de grille d'investigation, dispositif électronique et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2019061992A1
WO2019061992A1 PCT/CN2018/076179 CN2018076179W WO2019061992A1 WO 2019061992 A1 WO2019061992 A1 WO 2019061992A1 CN 2018076179 W CN2018076179 W CN 2018076179W WO 2019061992 A1 WO2019061992 A1 WO 2019061992A1
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WO
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Prior art keywords
hotspot
area
hot spot
time period
survey
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PCT/CN2018/076179
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English (en)
Chinese (zh)
Inventor
邓坤
王建明
肖京
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平安科技（深圳）有限公司
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Publication of WO2019061992A1 publication Critical patent/WO2019061992A1/fr

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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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Definitions

  • the present application relates to the field of computer information technology, and in particular, to a survey grid optimization method, an electronic device, and a computer readable storage medium.
  • the present application proposes a survey grid optimization method, an electronic device and a computer readable storage medium.
  • the existing survey grid can be optimized to improve the overall survey work efficiency.
  • the present application provides an electronic device including a memory and a processor, where the memory stores a search grid optimization system operable on the processor, the survey network
  • the grid optimization system is implemented by the processor to implement the following steps:
  • the grid of the survey area set in advance in the database is adjusted.
  • the obtaining of the hotspot area and the hotspot period of the auto insurance case comprises:
  • the hotspot area of the auto insurance case under different thresholds is analyzed, and the optimal threshold and the best hotspot area meeting the predetermined condition are obtained;
  • cluster analysis of the hot spot time segment is performed to obtain the hot spot time period of the car insurance case.
  • determining that the area is a hotspot area if the number of vehicle insurance reports in a certain area within a preset time period is greater than or equal to a preset threshold, determining that the area is a hotspot area;
  • the different thresholds required for the cluster analysis of the hotspot area include a first preset threshold, a second preset threshold, and a third preset threshold.
  • the hotspot area for calculating a car insurance case under different thresholds includes:
  • the optimal hotspot area is the most hotspot area group that meets the predetermined condition
  • the optimal threshold is a preset threshold corresponding to the optimal hotspot area.
  • the predetermined condition is a workload of the surveying personnel
  • the pre-set survey area grid in the adjustment database includes: adding survey personnel of the hot spot area and the hot spot time period, reducing survey personnel of non-hot spot areas and non-hot spot time periods, or merging non-hot spot areas.
  • the present application further provides a method for optimizing a survey grid, which is applied to an electronic device, and the method includes:
  • the pre-set survey area grid in the database is adjusted according to the case hotspot area and the hot spot time period obtained by the cluster analysis.
  • the obtaining of the hotspot area and the hotspot period of the auto insurance case comprises:
  • the hotspot area of the auto insurance case under different thresholds is analyzed, and the optimal threshold and the best hotspot area meeting the predetermined condition are obtained;
  • cluster analysis of the hot spot time segment is performed to obtain the hot spot time period of the car insurance case.
  • determining that the area is a hotspot area if the number of vehicle insurance reports in a certain area within a preset time period is greater than or equal to a preset threshold, determining that the area is a hotspot area;
  • the different thresholds required for the cluster analysis of the hotspot area include a first preset threshold, a second preset threshold, and a third preset threshold.
  • the hotspot area for calculating a car insurance case under different thresholds includes:
  • the optimal hotspot area is the hotspot area group with the largest number of the predetermined hot conditions, and the optimal threshold is a preset threshold corresponding to the best hotspot area;
  • the predetermined condition is a workload of the surveying personnel.
  • the grid of the survey area preset in the adjustment database includes: a survey person who increases the hotspot area and the hot spot time period of the case, and reduces the non-hot spot area and the non-hot spot time section of the survey personnel or merges the non-hot spot area.
  • the present application further provides a computer readable storage medium storing a survey grid optimization system, the survey grid optimization system being executable by at least one processor, The step of causing the at least one processor to perform the survey grid optimization method as described above.
  • the electronic device, the survey grid optimization method and the computer readable storage medium proposed by the present application perform cluster analysis on key information of the extracted vehicle insurance report (such as report city, region, and time distribution). Obtain the hotspot area of the auto insurance case and the hotspot time period, and adjust the grid of the existing survey area according to the results of the cluster analysis.
  • This application can optimize the existing survey grid and improve the overall survey work efficiency, and can be based on the latest network.
  • the grid data is dynamically updated.
  • 1 is a schematic diagram of an optional hardware architecture of an electronic device of the present application
  • FIG. 2 is a schematic diagram of a program module of an embodiment of a survey grid optimization system in an electronic device of the present application
  • FIG. 3 is a schematic diagram of an implementation process of an embodiment of a method for optimizing a survey grid according to the present application.
  • first, second and the like in the present application are for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. .
  • features defining “first” and “second” may include at least one of the features, either explicitly or implicitly.
  • the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. Nor is it within the scope of protection required by this application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the electronic device 2 of the present application.
  • the electronic device 2 may include, but is not limited to, a memory 21, a processor 22, and a network interface 23 that can communicate with each other through a system bus. It is pointed out that FIG. 1 only shows the electronic device 2 with the components 21-23, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.
  • the electronic device 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the electronic device 2 may be an independent server or a server cluster composed of multiple servers. .
  • the memory 21 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2.
  • the memory 21 may also be an external storage device of the electronic device 2, such as a plug-in hard disk equipped on the electronic device 2, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • the memory 21 may also include both an internal storage unit of the electronic device 2 and an external storage device thereof.
  • the memory 21 is generally used to store an operating system and various application software installed in the electronic device 2, such as program codes of the survey grid optimization system 20. Further, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 22 is typically used to control the overall operation of the electronic device 2, such as performing control and processing related to data interaction or communication with the electronic device 2.
  • the processor 22 is configured to run program code or process data stored in the memory 21, such as running the survey grid optimization system 20 and the like.
  • the network interface 23 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 2 and other electronic devices.
  • the network interface 23 is configured to connect the electronic device 2 to an external data platform through a network, and establish a data transmission channel and a communication connection between the electronic device 2 and an external data platform.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as network, Bluetooth, Wi-Fi, etc.
  • the survey grid optimization system 20 may be divided into one or more program modules, the one or more program modules being stored in the memory 21 and being processed by one or more processors. (Processing in the present embodiment for the processor 22) to complete the application.
  • the survey grid optimization system 20 can be divided into an extraction module 201, an acquisition module 202, and an optimization module 203.
  • a program module as referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function, which is more suitable than the program to describe the execution process of the survey grid optimization system 20 in the electronic device 2. The function of each program module 201-203 will be described in detail below.
  • the extraction module 201 is configured to extract pre-stored vehicle insurance report information in a database (such as the memory 21 of the electronic device 2).
  • the vehicle insurance report information includes, but is not limited to, information on the city, region, and time distribution (such as different time periods, working days, non-working days, holidays, etc.) of the automobile insurance report.
  • the obtaining module 202 is configured to perform cluster analysis on the extracted vehicle insurance report information, and obtain a hotspot area (such as a city center area) and a hotspot time period (such as 7:00-9:00 on the working day). 5:30-7:30 pm).
  • a hotspot area such as a city center area
  • a hotspot time period such as 7:00-9:00 on the working day. 5:30-7:30 pm.
  • the DBSCAN clustering algorithm may be used to perform cluster analysis on the extracted vehicle insurance report information.
  • the obtaining of the hotspot area and the hotspot period of the auto insurance case comprises the following steps:
  • different thresholds required for cluster analysis of the hotspot region include, but are not limited to, a first preset threshold (eg, 3 times), a second preset threshold (eg, 5 times), Three preset thresholds (such as 7 times).
  • all hotspots whose number of car insurance reports is greater than or equal to the first preset threshold are counted in the preset time period, and the first group of hotspots (such as 10) are obtained; the number of car insurance reports in the preset time period is greater than or equal to
  • the second hotspot area is obtained by using the second hotspot area (for example, 15); and the hotspot report period is greater than or equal to the third preset threshold in the preset preset period, and the third hotspot area is obtained.
  • the first group of hotspots such as 10
  • the optimal hotspot area is the hotspot area group (such as the second set of hotspot areas) that meets the predetermined condition.
  • the optimal threshold is a preset threshold corresponding to the best hotspot area. For example, if the best hotspot area is the second set of hotspot areas, the optimal threshold is a second preset threshold corresponding to the second set of hotspot areas.
  • the predetermined condition may be a workload of the surveying personnel.
  • the best hotspot area is determined to be the second hotspot area (the number is 15), and the optimal threshold is the second preset threshold (5 times). ).
  • the DBSCAN clustering algorithm may also be used to perform hotspot time segment clustering analysis, and details are not described herein again.
  • the optimization module 203 is configured to adjust a pre-set survey area grid in the database according to the case hotspot area and the hotspot time period acquired by the cluster analysis.
  • the preset survey area grid in the adjustment database includes: a survey person who increases the hotspot area and the hot spot time period of the case, and reduces the survey personnel of the non-hot spot area and the non-hot spot time period. Or merge non-hot spots.
  • the optimization module 203 is further configured to:
  • update grid data such as map data in the grid area
  • case data such as the latest accident point report data
  • the survey grid optimization system 20 proposed by the present application clusters the key information of the extracted vehicle insurance reports (such as the report city, region, and time distribution), and obtains hotspot areas and hotspots of the automobile insurance cases.
  • the time period and the division of the grid of the existing survey area are adjusted according to the results of the cluster analysis.
  • This application can optimize the existing survey grid, improve the overall survey work efficiency, and can dynamically update according to the latest grid data.
  • the present application also proposes a survey grid optimization method.
  • FIG. 3 it is a schematic flowchart of an implementation process of an embodiment of the search grid optimization method of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 3 may be changed according to different requirements, and some steps may be omitted.
  • the vehicle insurance report information pre-stored in the database (such as the memory 21 of the electronic device 2) is extracted.
  • the vehicle insurance report information includes, but is not limited to, information on the city, region, and time distribution (such as different time periods, working days, non-working days, holidays, etc.) of the automobile insurance report.
  • Step S32 performing cluster analysis on the extracted vehicle insurance report information, obtaining a hot spot area of the auto insurance case (such as a city center area) and a hotspot time period (eg, 7:00-9:00 on the working day, 5:30 pm) 7:30).
  • a hot spot area of the auto insurance case such as a city center area
  • a hotspot time period eg, 7:00-9:00 on the working day, 5:30 pm
  • the DBSCAN clustering algorithm may be used to perform cluster analysis on the extracted vehicle insurance report information.
  • the obtaining of the hotspot area and the hotspot period of the auto insurance case comprises the following steps:
  • different thresholds required for cluster analysis of the hotspot region include, but are not limited to, a first preset threshold (eg, 3 times), a second preset threshold (eg, 5 times), Three preset thresholds (such as 7 times).
  • all hotspots whose number of car insurance reports is greater than or equal to the first preset threshold are counted in the preset time period, and the first group of hotspots (such as 10) are obtained; the number of car insurance reports in the preset time period is greater than or equal to
  • the second hotspot area is obtained by using the second hotspot area (for example, 15); and the hotspot report period is greater than or equal to the third preset threshold in the preset preset period, and the third hotspot area is obtained.
  • the first group of hotspots such as 10
  • the optimal hotspot area is the hotspot area group (such as the second set of hotspot areas) that meets the predetermined condition.
  • the optimal threshold is a preset threshold corresponding to the best hotspot area. For example, if the best hotspot area is the second set of hotspot areas, the optimal threshold is a second preset threshold corresponding to the second set of hotspot areas.
  • the predetermined condition may be a workload of the surveying personnel.
  • the best hotspot area is determined to be the second hotspot area (the number is 15), and the optimal threshold is the second preset threshold (5 times). ).
  • the DBSCAN clustering algorithm may also be used to perform hotspot time segment clustering analysis, and details are not described herein again.
  • Step S33 Adjust the pre-set survey area grid in the database according to the case hotspot area and the hotspot time period obtained by the cluster analysis.
  • the preset survey area grid in the adjustment database includes: a survey person who increases the hotspot area and the hot spot time period of the case, and reduces the survey personnel of the non-hot spot area and the non-hot spot time period. Or merge non-hot spots.
  • the survey grid optimization method further includes the following steps:
  • update grid data such as map data in the grid area
  • case data such as the latest accident point report data
  • the survey grid optimization method proposed by the present application clusters the key information of the extracted vehicle insurance report (such as the report city, region, and time distribution), and obtains the hotspot area and the hot spot time period of the auto insurance case. And according to the results of the cluster analysis, the division of the grid of the existing survey area is adjusted.
  • This application can optimize the existing survey grid, improve the overall survey work efficiency, and can dynamically update according to the latest grid data.
  • the present application further provides a computer readable storage medium (such as a ROM/RAM, a magnetic disk, an optical disk), where the computer readable storage medium stores a survey grid optimization system 20, the survey The grid optimization system 20 can be executed by at least one processor 22 to cause the at least one processor 22 to perform the steps of the survey grid optimization method as described above.
  • a computer readable storage medium such as a ROM/RAM, a magnetic disk, an optical disk
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and can also be implemented by hardware, but in many cases, the former is A better implementation.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

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Abstract

La présente invention concerne un procédé d'optimisation d'une grille d'investigation, comprenant les étapes suivantes consistant : à extraire des informations de demande d'indemnisation d'assurance voiture pré-mémorisées dans une base de données; à effectuer une analyse de grappe des informations de demande d'indemnisation d'assurance voiture extraites afin d'obtenir une zone critique de cas d'assurance voiture et une période de temps; et à régler, en fonction de la zone critique de cas d'assurance voiture et de la période de temps obtenues après que l'analyse de grappe a été effectuée, une grille de zone d'investigation prédéfinie dans la base de données. La présente invention peut optimiser une grille d'investigation existante, ce qui permet d'améliorer l'efficacité d'investigation globale.
PCT/CN2018/076179 2017-09-30 2018-02-10 Procédé d'optimisation de grille d'investigation, dispositif électronique et support d'informations lisible par ordinateur WO2019061992A1 (fr)

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CN201710916177.XA CN107844851B (zh) 2017-09-30 2017-09-30 查勘网格优化方法、电子设备及计算机可读存储介质
CN201710916177.X 2017-09-30

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CN109543947A (zh) * 2018-10-19 2019-03-29 中国平安财产保险股份有限公司 基于查勘网格的任务分配的方法、装置及终端设备
CN109544098B (zh) * 2018-10-19 2024-08-02 中国平安财产保险股份有限公司 一种智能排班方法、装置、存储介质和终端设备
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CN110457653B (zh) * 2019-07-30 2023-09-01 青岛海信网络科技股份有限公司 一种警情热点区域的确定方法及装置
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CN110659997B (zh) * 2019-08-15 2023-06-27 中国平安财产保险股份有限公司 数据聚类识别方法、装置、计算机系统及可读存储介质
CN113032607A (zh) * 2019-12-09 2021-06-25 深圳云天励飞技术有限公司 关键人员分析方法、装置、电子设备及存储介质

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