WO2018184539A1 - 车险理赔数据分析方法和系统 - Google Patents

车险理赔数据分析方法和系统 Download PDF

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Publication number
WO2018184539A1
WO2018184539A1 PCT/CN2018/081797 CN2018081797W WO2018184539A1 WO 2018184539 A1 WO2018184539 A1 WO 2018184539A1 CN 2018081797 W CN2018081797 W CN 2018081797W WO 2018184539 A1 WO2018184539 A1 WO 2018184539A1
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WIPO (PCT)
Prior art keywords
risk
insurance
license plate
data
location
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PCT/CN2018/081797
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English (en)
French (fr)
Inventor
陈金良
林梓棱
林立辉
叶木旺
曾永理
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平安科技（深圳）有限公司
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Publication of WO2018184539A1 publication Critical patent/WO2018184539A1/zh

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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
    • 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 technology, and in particular, to a method and system for analyzing car insurance claims data.
  • a method for analyzing car insurance claims data comprising:
  • the claim server Returning the high risk license plate number and the high risk insurance location to the plurality of claim servers respectively, so that the claim server generates the verification when receiving the vehicle insurance report information carrying the high risk license plate number and/or the high risk insurance place Whether to spoof the prompt information and send the prompt information to the claim terminal.
  • a method for analyzing car insurance claims data comprising:
  • the auto insurance claim data includes at least a license plate number and a place of risk of the insured vehicle;
  • a prompt message for verifying whether to swindle is generated.
  • a car insurance claims data analysis system comprising a plurality of claims servers and industry servers, wherein:
  • a claim server configured to obtain auto insurance claim data, where the auto insurance claim data includes at least a license plate number and a place of risk of the insured vehicle;
  • the industry server is configured to receive the auto insurance claim data uploaded by the plurality of claims servers, perform a big data risk analysis on the auto insurance claim data, and obtain a high-risk license plate number corresponding to the auto insurance, and a high-risk insurance place;
  • the claim server is further configured to receive a high-risk license plate number returned by the industry server, a high-risk insurance location, and generate a verification when receiving the vehicle insurance report information carrying the high-risk license plate number and/or the high-risk insurance location. Defrauding the prompt information and sending the prompt information to the claim terminal.
  • a server comprising a memory and one or more processors, the memory storing computer readable instructions, the computer readable instructions being executed by the processor, causing the one or more processors to perform the following step:
  • the claim server Returning the high risk license plate number and the high risk insurance location to the plurality of claim servers respectively, so that the claim server generates the verification when receiving the vehicle insurance report information carrying the high risk license plate number and/or the high risk insurance place Whether to spoof the prompt information and send the prompt information to the claim terminal.
  • a server comprising a memory and one or more processors, the memory storing computer readable instructions, the computer readable instructions being executed by the processor, causing the one or more processors to perform the following step:
  • the auto insurance claim data includes at least a license plate number and a place of risk of the insured vehicle;
  • a prompt message for verifying whether to swindle is generated.
  • One or more non-transitory computer readable storage mediums storing computer readable instructions, when executed by one or more processors, cause one or more processors to perform the steps of:
  • the claim server Returning the high risk license plate number and the high risk insurance location to the plurality of claim servers respectively, so that the claim server generates the verification when receiving the vehicle insurance report information carrying the high risk license plate number and/or the high risk insurance place Whether to spoof the prompt information and send the prompt information to the claim terminal.
  • One or more non-transitory computer readable storage mediums storing computer readable instructions, when executed by one or more processors, cause one or more processors to perform the steps of:
  • the auto insurance claim data includes at least a license plate number and a place of risk of the insured vehicle;
  • a prompt message for verifying whether to swindle is generated.
  • 1 is an application environment diagram of a car insurance claim data analysis method according to one or more embodiments
  • FIG. 2 is a flow chart of a method for analyzing car insurance claims data in accordance with one or more embodiments
  • FIG. 3 is a schematic structural diagram of an industry server according to one or more embodiments.
  • FIG. 5 is a schematic structural diagram of a car insurance claim data analysis system according to one or more embodiments.
  • the car insurance claim data analysis method provided in the embodiment of the present application can be applied to the application environment as shown in FIG.
  • the claims server 102 establishes a connection with the industry server 104 over the network.
  • the claims server 102 establishes a connection with the claims terminal over the network.
  • the claims server 102 stores the car insurance claim data.
  • the auto insurance claims data includes the license plate number of the insurance vehicle, the time of the insurance, the location of the insurance, and the reasons for the risk.
  • the claims server 102 can be affiliated with different insurance companies.
  • the claims server 102 of the plurality of insurance companies uploads the auto insurance claim data to the industry server 104.
  • the industry server 104 performs a big data risk analysis on the car insurance claim data, and obtains a high-risk license plate number and a high-risk risk location corresponding to the car insurance.
  • the industry server 104 returns the high risk license plate number and the high risk insurance location to the plurality of claims servers 102, respectively. If a high-risk license plate number and/or a high-risk risk location is placed in the auto insurance report information, it means that there is a possibility of fraudulent insurance.
  • the claim server 102 receives the vehicle insurance report information carrying the high-risk license plate number and/or the high-risk insurance place, the prompt information for verifying whether to swindle is generated, and the prompt information is sent to the claim terminal 106.
  • a vehicle risk claim data analysis method is provided.
  • the method is applied to an industry server as an example, and specifically includes the following steps:
  • Step 202 Receive car insurance claim data uploaded by multiple claim servers, and the car insurance claim data includes at least a license plate number and a place of risk of the insured vehicle.
  • Step 204 Perform a big data risk analysis on the car insurance claim data, and obtain a high-risk license plate number and a high-risk risk location corresponding to the car insurance.
  • Step 206 Return the high-risk license plate number and the high-risk risk detection location to the plurality of claims servers respectively, so that the claim server generates verification to confirm whether to swindle the vehicle when receiving the vehicle insurance report information carrying the high-risk license plate number and/or the high-risk insurance location.
  • the prompt message and send the prompt information to the claim terminal.
  • the database is deployed on the claims server, and the car insurance claims data is stored in the database.
  • the auto insurance claims data includes the license plate number of the insurance vehicle, the time of the insurance, the location of the insurance, and the reasons for the risk.
  • the car insurance claims data stored on the claims server of different insurance companies is different.
  • the claims server of multiple insurance companies uploads auto insurance claims data to the industry server.
  • the industry server saves the received car insurance claims data.
  • the industry server performs big data risk analysis on the auto insurance claim data according to the preset frequency.
  • the preset frequency can be once a month, once a quarter, or once a year.
  • the risk analysis of industry servers includes high-risk license plate number analysis and high-risk risk location analysis.
  • the step of performing a big data risk analysis on the auto insurance claim data includes: obtaining a license plate number in the auto insurance claim data and a corresponding risk time; performing big data analysis on the plurality of license plate numbers and the time of the risk; If the number of accidents of a license plate number exceeds the first preset risk number within the preset time period, the license plate number is recorded as a high risk license plate number.
  • the industry server obtains the license plate number and the corresponding risk time in the car insurance claim data according to the preset frequency.
  • the industry server performs big data analysis on the multiple license plate numbers and time of the acquisition. If the number of times the same license plate number exceeds the first preset risk number within the preset time period, the industry server records the license plate number as a high risk license plate number. If a high-risk license plate number is issued in the auto insurance report information, it means that there is a possibility of fraudulent insurance.
  • the step of performing a big data risk analysis on the auto insurance claim data includes: obtaining a license plate number, a place of the insurance, and a cause of the risk in the auto insurance claim data; and performing a large risk location and a risk cause corresponding to the plurality of license plate numbers.
  • Data analysis; the cause of the risk includes intentional manufacturing site; the reason for obtaining the risk is the characteristic of the place of the insurance corresponding to the car accident case on the intentional manufacturing site; if the risk location with the same characteristics and the reason for the risk is the number of car accident cases on the intentional manufacturing site is greater than the default case
  • the characteristics of the dangerous place are recorded, and the characteristic risk location is recorded as a high risk risk place.
  • the industry server analyzes the license plate number, the place of the insurance and the cause of the risk in the car insurance claim data, and analyzes the risk location and the cause of the risk corresponding to the multiple license plate numbers.
  • the reason why the industry server obtains the risk is the characteristic of the place of the insurance corresponding to the car accident case on the intentional manufacturing site.
  • Features include: road teeth, shoulders, road piles and columns. Due to the partial fraud insurance auto insurance claims, the accident scene will be intentionally created at the location with the above characteristics. Therefore, if the risk location of the same feature is caused by the fact that the number of auto accident cases on the intentional manufacturing site is greater than the preset case amount, the industry server records the characteristics of the risk location, and records the risk location with the feature as a high risk risk. location. If there is a risky high-risk location in the auto insurance report information, it means that there is a possibility of fraudulent insurance.
  • the industry server sends high-risk license plate numbers and high-risk risk locations from big data analysis to multiple claims servers.
  • the claims server receives high-risk license plate numbers and high-risk risk locations and saves them.
  • the claim server receives the high-risk license plate number and/or the high-risk insurance place in the car insurance report information
  • the claim server generates a prompt message for verifying whether the fraud is swindled, and sends the prompt information to the claim terminal.
  • the claimant checks the auto insurance case to find out the false auto insurance case in time, thereby reducing the auto insurance loss of the insurance company.
  • the claim servers of the plurality of insurance companies send the respective car insurance claims data to the industry server, and the industry server performs a big data risk analysis on the car insurance claims data of the plurality of insurance companies, and obtains the high-risk license plate number corresponding to the car insurance and the high. Risk location.
  • the industry server can use the K-means algorithm (a cluster analysis algorithm) to iteratively calculate the license plate number as a data object, identify the high-risk license plate number, and perform iterative calculation with the latitude and longitude of the risk location as the data object. Identify high-risk locations.
  • the high-risk license plate number and high-risk risk location are comprehensively analyzed. If the auto insurance report information carries a high-risk license plate number and/or a high-risk insurance location, it means that there is a possibility of fraudulent insurance.
  • Industry servers send high-risk license plates and high-risk locations to multiple claims servers.
  • the auto insurance report information received by the claim server includes a high-risk license plate number and/or a high-risk insurance location, a prompt message for verifying whether the fraud is secured is generated, and the prompt information is sent to the claim terminal. Therefore, the claimant can be prompted to strengthen the verification of the auto insurance case, so as to timely discover the auto insurance case of the fraud insurance and reduce the auto insurance loss of the insurance company.
  • the step of performing a big data risk analysis on the car insurance claim data further includes: obtaining a place of risk in the car insurance claim data; performing big data analysis on the plurality of places of risk; and if the number of times of the risk in the same place of the risk exceeds The second preset number of accidents is recorded as the location of the accident.
  • the big data risk analysis of the auto insurance claims data of the industry server can analyze the high-risk location of the accident in addition to the high-risk license plate number and the high-risk risk location.
  • the industry server obtains the risk location in the massive auto insurance claims data, and performs big data analysis on multiple insurance locations.
  • two or more places of danger within the preset range of the same road section can be regarded as the same place of danger.
  • the preset range can be 1 km. If the number of out-of-risk accidents in the same insurance location exceeds the second preset number of insurance trips, the industry server records the location of the accident as a high-incident location.
  • the industry server can save the voice prompt information of the accident-prone location, and can also send it to the claims server of multiple insurance companies.
  • An in-vehicle terminal is installed on the vehicle, and the in-vehicle terminal can establish a connection with the industry server through the network, or establish a connection with the claim server of the insurance company that insures the automobile insurance.
  • the vehicle terminal acquires the location information of the vehicle in real time and uploads its location information to the industry server or the claims server.
  • the industry server or the claims server compares the location information of the vehicle with the location information of the high-incident location.
  • the industry server or the claims server sends the voice prompt information to the vehicle-mounted terminal, so that the vehicle-mounted terminal performs the real-time operation. Voice prompts. This prompts the driver to drive carefully and avoid accidents.
  • the vehicle terminal can also download location information and language prompt information corresponding to the high-incidence location of the accident from the industry server or the claims server.
  • the vehicle-mounted terminal acquires the location information of the vehicle in real time, and when the vehicle travels to the high-incidence location of the accident, the vehicle-mounted terminal performs a voice prompt.
  • the method before receiving the auto insurance claim data uploaded by the plurality of claims servers, the method further includes: receiving the accident liability identification data sent by the traffic police server, and saving the accident liability determination data; and receiving the accident responsibility query request sent by the claims server
  • the accident liability inquiry request carries the time of the insurance and the license plate number of the vehicle in danger; according to the time of the insurance and the license plate number of the vehicle in which the insurance is issued, the inquiry is made in the accident liability identification data, and the accident liability corresponding to the license plate number is obtained; the accident liability corresponding to the license plate number is obtained.
  • the claim server performs a loss calculation on the insured vehicle according to the accident liability corresponding to the license plate number.
  • the industry server establishes a connection with the traffic police server. If the liability for the accident in the auto insurance case is not clear, the traffic police is required to come to the scene of the accident to determine the liability of the accident.
  • the traffic police uploads the accident liability identification data to the traffic police server through the traffic police terminal.
  • the traffic police server obtains the accident liability identification data corresponding to the auto insurance case and sends it to the industry server.
  • the industry server saves the accident liability identification data corresponding to the auto insurance case.
  • the claims server When an insurance company conducts claims processing for a car insurance case, it is necessary to check the accident liability of the traffic police for the car insurance case with unclear liability for the accident.
  • the claims server generates an incident liability query request and sends an incident liability query request to the industry server.
  • the industry server queries the accident liability inquiry request carrying the risk time and the license plate number of the insured vehicle in the accident liability identification data, obtains the corresponding accident liability of the license plate number in the auto insurance case, and returns the accident liability to the claim server.
  • the claim server calculates the loss of the insured vehicle according to the license number corresponding to the license number in the auto insurance case.
  • the data sharing of the accident responsibility identification is performed between the traffic police server and the industry server, so that each insurance company can timely obtain the accident liability corresponding to the license plate number in the automobile insurance case when the automobile insurance claims are made, thereby facilitating the insurance companies.
  • the industry server includes a processor 301, an internal memory 302, a non-volatile storage medium 303, and a network interface 304 connected by a system bus.
  • the non-volatile storage medium 303 of the industry server stores an operating system 3031 and an operating system and computer readable instructions, and the non-volatile storage medium 303 may be a computer readable non-volatile storage medium.
  • the computer readable instructions are used to implement a car insurance claim data analysis method.
  • the processor 301 of the industry server 300 is used to provide computing and control capabilities to support the operation of the entire industry server.
  • the network interface 304 of the industry server 300 is configured to communicate with an external claims server via a network connection, such as receiving car insurance claims data uploaded by the claims server, and transmitting a high-risk license plate number to the claims server.
  • the industry server 300 can be implemented with a stand-alone server or a server cluster composed of multiple servers. Those skilled in the art can understand that the structure shown in FIG. 3 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation of an industry server to which the solution of the present application is applied.
  • the specific industry server may It includes more or fewer components than those shown in the figures, or some components are combined, or have different component arrangements.
  • a car insurance claim data analysis method is provided.
  • the method is applied to the claim server as an example, and specifically includes the following steps:
  • Step 402 Acquire auto insurance claim data, and the auto insurance claim data includes at least a license plate number and a place of risk of the insured vehicle.
  • Step 404 Upload the auto insurance claim data to the industry server, so that the industry server performs a big data risk analysis on the auto insurance claim data, and obtains a high-risk license plate number and a high-risk risk location corresponding to the auto insurance.
  • Step 406 Receive a high-risk license plate number and a high-risk risk detection location returned by the industry server.
  • Step 408 When receiving the auto insurance claim requesting the high risk license plate number and/or the high risk insurance location, generate prompt information for verifying whether to swindle.
  • the claim server obtains the auto insurance claim data, including the license plate number of the insurance vehicle, the time of the insurance, the location of the insurance, and the reason for the risk.
  • the car insurance claims data stored on the claims server of different insurance companies is different.
  • the claims server of multiple insurance companies uploads auto insurance claims data to the industry server.
  • the industry server saves the received car insurance claims data.
  • the industry server performs big data risk analysis on the auto insurance claim data according to the preset frequency.
  • the risk analysis of industry servers includes high-risk license plate number analysis and high-risk risk location analysis. Specifically, the industry server obtains the license plate number and the corresponding risk time in the car insurance claim data, and performs big data analysis on multiple license plate numbers and the time of the outage, if the number of times of the same license plate number in the preset time period exceeds the first pre-prevention If the number of insurances is set, the license plate number will be recorded as a high-risk license plate number. The industry server can also obtain the license plate number, the place of the insurance and the cause of the risk in the car insurance claim data, and analyze the risk location and the cause of the risk corresponding to the multiple license plate numbers.
  • the reasons for the risk include intentional manufacturing.
  • the reason why the industry server obtains the risk is the characteristic of the place of the insurance corresponding to the car accident case on the intentional manufacturing site. If the risk location with the same characteristics and the reason for the risk is that the number of car accident cases on the intentional manufacturing site is greater than the preset case amount, the place of the risk is recorded. Characteristics, and record the location of the risk location as a high-risk location.
  • the industry server sends high-risk license plate numbers and high-risk risk locations from big data analysis to multiple claims servers.
  • the claims server receives high-risk license plate numbers and high-risk risk locations and saves them.
  • the claim server receives the high-risk license plate number and/or the high-risk insurance place in the car insurance report information
  • the claim server generates a prompt message for verifying whether the fraud is swindled, and sends the prompt information to the claim terminal. This allows the claimant to verify the auto insurance case in order to find the false auto insurance case in time. Thereby reducing the insurance company's car insurance losses.
  • the claim servers of the plurality of insurance companies send the respective car insurance claims data to the industry server, and the industry server performs a big data risk analysis on the car insurance claims data of the plurality of insurance companies, and obtains the high-risk license plate number corresponding to the car insurance and the high. Risk location. Since the big data risk analysis is based on the auto insurance claims data of multiple insurance companies, the high-risk license plate number and high-risk risk location are comprehensively analyzed. If the auto insurance report information carries a high-risk license plate number and/or a high-risk insurance location, it means that there is a possibility of fraudulent insurance. Industry servers send high-risk license plates and high-risk locations to multiple claims servers.
  • the auto insurance report information received by the claim server includes a high-risk license plate number and/or a high-risk insurance location
  • a prompt message for verifying whether the fraud is secured is generated, and the prompt information is sent to the claim terminal. Therefore, the claimant can be prompted to strengthen the verification of the auto insurance case, so as to timely discover the auto insurance case of the fraud insurance and reduce the auto insurance loss of the insurance company.
  • the method before the step of acquiring the car insurance claim data, further includes: receiving the car insurance report information uploaded by the client terminal; the car insurance report information includes the time of the insurance and the license plate number of the insured vehicle; and the license plate number according to the time of the insurance and the vehicle in danger. Generating an incident liability inquiry request, and sending the accident liability inquiry request to the industry server, so that the industry server queries the saved accident liability identification data; receives the accident liability corresponding to the license plate number returned by the industry server, and the accident vehicle according to the accident liability Perform a loss calculation.
  • the customer when a car accident is transmitted, the customer can upload the car insurance report information through the client terminal.
  • the auto insurance application installed in the client terminal may be an application provided by an insurance company or a third-party application other than an insurance company, for example, WeChat. Customers can enter the auto insurance report page through the WeChat public account, and then upload the auto insurance report information.
  • the client terminal uploads the auto insurance report information, in order to facilitate the customer to complete the report quickly.
  • the auto insurance application obtains basic customer information, such as name, license plate number, etc., and the application can directly input the customer basic information into the report page.
  • the client terminal can also capture its location, obtain the corresponding link name according to its location information, and input the link name into the report page.
  • some auto insurance report information can be automatically input through the application program, without the need for the customer to input one by one, which provides convenience for the customer to report the case quickly.
  • the claim server After receiving the report information, the claim server generates a car insurance task and sends the car insurance task to the corresponding claim terminal.
  • the claimant will conduct on-the-spot investigation and damage determination through the claims terminal.
  • the claims server When an insurance company conducts claims processing for a car insurance case, it is necessary to check the accident liability of the traffic police for the car insurance case with unclear liability for the accident.
  • the claims server generates an incident liability query request and sends an incident liability query request to the industry server.
  • the industry server queries the accident liability inquiry request carrying the risk time and the license plate number of the insured vehicle in the accident liability identification data, obtains the corresponding accident liability of the license plate number in the auto insurance case, and returns the accident liability to the claim server.
  • the claim server calculates the loss of the insured vehicle according to the license number corresponding to the license number in the auto insurance case.
  • the data sharing of the accident responsibility identification is performed between the traffic police server and the industry server, so that each insurance company can timely obtain the accident liability corresponding to the license plate number in the automobile insurance case when the automobile insurance claims are made, thereby facilitating the insurance companies.
  • FIGS. 2 and 4 are sequentially displayed as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Except as explicitly stated herein, the execution of these steps is not strictly limited, and the steps may be performed in other orders. Moreover, at least some of the steps in FIGS. 2 and 4 may include a plurality of sub-steps or stages, which are not necessarily performed at the same time, but may be executed at different times, or The order of execution of the stages is also not necessarily sequential, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
  • a car insurance claims data analysis system including a plurality of claims servers 502 and industry servers 504, wherein:
  • the claim server 502 is configured to obtain auto insurance claim data, and the auto insurance claim data includes at least a license plate number and a risk location of the insured vehicle.
  • the industry server 504 is configured to receive the car insurance claim data uploaded by the plurality of claims servers, perform a big data risk analysis on the car insurance claim data, and obtain a high-risk license plate number and a high-risk risk location corresponding to the car insurance.
  • the claim server 502 is further configured to receive the high-risk license plate number returned by the industry server 504, the high-risk insurance location, and generate a prompt to verify whether to swindle the insurance when receiving the vehicle insurance report information carrying the high-risk license plate number and/or the high-risk insurance location. Information and send the prompt message to the claim terminal.
  • system further includes: a client terminal 506 and a traffic police server 508, wherein:
  • the client terminal 506 is configured to report the car insurance report information to the claim server; the car insurance report information includes the time of the insurance and the license plate number of the vehicle in danger.
  • the traffic police server 508 is configured to obtain the accident liability identification data of the automobile insurance case, and send the accident liability identification data to the industry server.
  • the claim server 502 is further configured to generate an incident liability inquiry request according to the time of the insurance and the license plate number of the insured vehicle, and send the accident liability inquiry request to the industry server 504.
  • the industry server 504 is further configured to query the accident liability identification data according to the time of the insurance and the license plate number of the insured vehicle, and obtain the accident liability corresponding to the license plate number.
  • the claim server 502 is further configured to receive an accident liability corresponding to the license plate number returned by the industry server 504, and perform a loss calculation on the insured vehicle according to the accident liability.
  • the industry server 504 is further configured to obtain a place of risk in the car insurance claim data; perform big data analysis on the plurality of places of risk; if the number of times of the risk in the same place of the risk exceeds the number of times of the second preset risk, The location of the accident is recorded as the location of the accident; the system further includes: an in-vehicle terminal 510 for capturing the location of the vehicle in real time, uploading the location of the vehicle to the industry server 504; and when the vehicle is traveling to the location of the accident, the industry server 504 is also used to The in-vehicle terminal 510 transmits the prompt information of the accident occurrence location; the in-vehicle terminal 510 is further configured to receive the prompt information of the accident high-risk location, and perform the voice playback of the prompt information.
  • the industry server 504 can also send the prompt information of the accident occurrence location to the claim server 502 of each insurance company, and the claim server 502 establishes a connection with the vehicle terminal 510.
  • the vehicle terminal 510 uploads the vehicle position to the claim server 502 in real time, and when the vehicle travels to the accident high-risk location, the claim server 502 sends the prompt information of the accident high-spot location to the vehicle-mounted terminal 510; the vehicle-mounted terminal 510 is further configured to receive the prompt information of the accident-incidence location. And prompt the message for voice playback. This reminds the driver to drive carefully and avoid accidents.
  • a server comprising a memory and one or more processors having stored therein computer readable instructions that, when executed by a processor, cause one or more processors to perform the steps of the various method embodiments described above.
  • One or more non-volatile storage media storing computer readable instructions, when executed by one or more processors, cause one or more processors to perform the steps in the various method embodiments described above.
  • Non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory can include random access memory (RAM) or external cache memory.
  • RAM is available in a variety of formats, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronization chain.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • Synchlink DRAM SLDRAM
  • Memory Bus Radbus
  • RDRAM Direct RAM
  • DRAM Direct Memory Bus Dynamic RAM
  • RDRAM Memory Bus Dynamic RAM

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Abstract

一种车险理赔数据分析方法，包括：接收多个理赔服务器上传的车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及将所述高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得所述理赔服务器在接收到携带所述高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。

Description

车险理赔数据分析方法和系统
本申请要求于2017年4月7日提交中国专利局，申请号为2017102253287，申请名称为“车险理赔数据分析方法和系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及计算机技术领域，特别是涉及一种车险理赔数据分析方法和系统。
背景技术
随着汽车的普及，车险也逐渐融入到人们的生活中。当被保险的车辆发生交通事故时，保险公司会对出险车辆进行理赔。面对每天数以万计的交通事故，每家保险公司都会产生大量的理赔数据。目前，不同的保险公司理赔数据是没有相互公开的。因此，每家保险公司在对理赔数据进行风险分析时，只能针对自己的理赔数据进行分析。也就是说，每家保险公司只是对部分交通事故产生的理赔数据进行分析，所做出的骗保防范措施很可能存在漏洞。因此，如何对车险的理赔数据进行全面的风险分析从而进行有效防范骗保成为一个待解决的技术问题。
发明内容
根据本申请公开的各种实施例，提供一种车险理赔数据分析方法和系统。
一种车险理赔数据分析方法，包括：
接收多个理赔服务器上传的车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及
将所述高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得所述理赔服务器在接收到携带所述高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送 至理赔终端。
一种车险理赔数据分析方法，包括：
获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
将所述车险理赔数据上传至行业服务器，以使得所述行业服务器对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；
接收所述行业服务器返回的高风险车牌号、高风险出险地点；及
当接收到携带所述高风险车牌号和/或高风险出险地点的车险理赔请求时，生成核实是否骗保的提示信息。
一种车险理赔数据分析系统，所述系统包括多个理赔服务器和行业服务器，其中：
理赔服务器，用于获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
行业服务器，用于接收多个理赔服务器上传的车险理赔数据，对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及
所述理赔服务器还用于接收所述行业服务器返回的高风险车牌号、高风险出险地点，当接收到携带所述高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。
一种服务器，包括存储器和一个或多个处理器，所述存储器中储存有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述一个或多个处理器执行以下步骤：
接收多个理赔服务器上传的车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及
将所述高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得所述理赔服务器在接收到携带所述高风险车牌号和/或高风险出险地点 的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。
一种服务器，包括存储器和一个或多个处理器，所述存储器中储存有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述一个或多个处理器执行以下步骤：
获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
将所述车险理赔数据上传至行业服务器，以使得所述行业服务器对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；
接收所述行业服务器返回的高风险车牌号、高风险出险地点；及
当接收到携带所述高风险车牌号和/或高风险出险地点的车险理赔请求时，生成核实是否骗保的提示信息。
一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行以下步骤：
接收多个理赔服务器上传的车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及
将所述高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得所述理赔服务器在接收到携带所述高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。
一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行以下步骤：
获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
将所述车险理赔数据上传至行业服务器，以使得所述行业服务器对所述 车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；
接收所述行业服务器返回的高风险车牌号、高风险出险地点；及
当接收到携带所述高风险车牌号和/或高风险出险地点的车险理赔请求时，生成核实是否骗保的提示信息。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为根据一个或多个实施例中的车险理赔数据分析方法应用环境图；
图2为根据一个或多个实施例中车险理赔数据分析方法的流程图；
图3为根据一个或多个实施例中行业服务器的结构示意图；
图4为又一个实施例中车险理赔数据分析方法的流程图；
图5为根据一个或多个实施例中车险理赔数据分析系统的结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请实施例中所提供的车险理赔数据分析方法，可以应用于如图1所示的应用环境中。理赔服务器102通过网络与行业服务器104建立连接。理赔服务器102通过网络与理赔终端建立连接。理赔服务器102存储了车险理赔数据。车险理赔数据包括出险车辆的车牌号、出险时间、出险地点以及出险原因等。所述理赔服务器102可以隶属于不同的保险公司。多个保险公司的理赔服务器102将车险理赔数据上传至行业服务器104。行业服务器104 对车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号和高风险出险地点。行业服务器104将高风险车牌号和高风险出险地点分别返回至多个理赔服务器102。如果在车险报案信息中出险高风险车牌号和/或高风险出险地点，则意味着存在骗保的可能性。当理赔服务器102在接收到携带高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将提示信息发送至理赔终端106。
在一个实施例中，如图2所示，提供了一种车险理赔数据分析方法，以该方法应用于行业服务器为例进行说明，具体包括以下步骤：
步骤202，接收多个理赔服务器上传的车险理赔数据，车险理赔数据至少包括出险车辆的车牌号和出险地点。
步骤204，对车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号和高风险出险地点。
步骤206，将高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得理赔服务器在接收到携带高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将提示信息发送至理赔终端。
理赔服务器上部署了数据库，数据库中存储了车险理赔数据。车险理赔数据包括出险车辆的车牌号、出险时间、出险地点以及出险原因等。不同保险公司的理赔服务器上所存储的车险理赔数据不同。多个保险公司的理赔服务器将车险理赔数据上传至行业服务器。行业服务器将接收到的车险理赔数据进行保存。
行业服务器按照预设频率对车险理赔数据进行大数据风险分析。预设频率可以是一个月一次，也可以一个季度一次，也可以是一年一次。行业服务器的风险分析包括高风险车牌号分析和高风险出险地点分析。
在其中一个实施例中，对车险理赔数据进行大数据风险分析的步骤，包括：获取车险理赔数据中的车牌号和对应的出险时间；对多个车牌号和出险时间进行大数据分析；若同一个车牌号在预设时间段内的出险次数超过第一预设出险次数，则将车牌号记录为高风险车牌号。
行业服务器按照预设频率获取车险理赔数据中的车牌号和对应的出险时间。行业服务器对获取到的多个车牌号和出险时间进行大数据分析。若同一 个车牌号在预设时间段内的出险次数超过第一预设出险次数，则行业服务器将车牌号记录为高风险车牌号。如果在车险报案信息中出险高风险车牌号，则意味着存在骗保的可能性。
在其中一个实施例中，对车险理赔数据进行大数据风险分析的步骤，包括：获取车险理赔数据中的车牌号、出险地点和出险原因；对多个车牌号对应的出险地点和出险原因进行大数据分析；出险原因包括故意制造现场；获取出险原因为故意制造现场的车险案件所对应的出险地点的特征；若具有相同特征的出险地点且出险原因为故意制造现场的车险案件数量大于预设案件量时，则记录出险地点的特征，并将具有特征的出险地点记录为高风险出险地点。
行业服务器对车险理赔数据中的车牌号、出险地点和出险原因，对多个车牌号对应的出险地点和出险原因进行大数据分析。行业服务器获取出险原因为故意制造现场的车险案件所对应的出险地点的特征。特征包括：路牙、路肩、路桩和柱子等。由于部分骗保的车险理赔会在具有上述特征的地点故意制造事故现场。因此，如果具有相同特征的出险地点且出险原因为故意制造现场的车险案件的数量大于预设案件量时，行业服务器记录该出险地点的特征，并且将具有该特征的出险地点记录为高风险出险地点。如果在车险报案信息中出险高风险出险地点，则意味着存在骗保的可能性。
行业服务器将大数据分析得到的高风险车牌号和高风险出险地点分别发送至多个理赔服务器。理赔服务器接收高风险车牌号和高风险出险地点，并进行保存。当理赔服务器接收到车险报案信息中携带了高风险车牌号和/或高风险出险地点时，理赔服务器生成核验是否骗保的提示信息，并将提示信息发送至理赔终端。由此使得理赔人员对该起车险案件进行核验，以便及时发现骗保的虚假车险案件，从而减少保险公司的车险损失。
本实施例中，多个保险公司的理赔服务器将各自的车险理赔数据发送至行业服务器，行业服务器对多个保险公司的车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号以及高风险出险地点。具体的，行业服务器可以采用K-means算法(一种聚类分析算法)，以车牌号作为数据对象进行迭代计算，识别出高风险车牌号，以及以出险地点的经纬度作为数据对象进行迭代计算，识别出高风险地点。
由于大数据风险分析是基于多个保险公司的车险理赔数据进行的，因此高风险车牌号和高风险出险地点是经过全面的分析得出的。如果车险报案信息中携带了高风险车牌号和/或高风险出险地点，则意味着存在骗保的可能。行业服务器将高风险车牌号以及高风险出险地点发送至多个理赔服务器。当理赔服务器接收到的车险报案信息中包括高风险车牌号和/或高风险出险地点时，则生成核验是否骗保的提示信息，并将提示信息发送至理赔终端。由此能够提示理赔人员对该车险案件要加强核验，以便及时发现骗保的车险案件，降低保险公司的车险损失。
在一个实施例中，对车险理赔数据进行大数据风险分析的步骤，还包括：获取车险理赔数据中的出险地点；对多个出险地点进行大数据分析；若在同一个出险地点的出险次数超过第二预设出险次数，则将出险地点记录为事故高发地点。
本实施例中，行业服务器对车险理赔数据的大数据风险分析除了会得到高风险车牌号与高风险出险地点之外，还可以分析得到事故高发地点。具体的，行业服务器在海量的车险理赔数据中获取出险地点，对多个出险地点进行大数据分析。其中，在同一路段的预设范围内的两个或两个以上的出险地点可以视为同一个出险地点。例如，预设范围可以是1公里。如果在同一个出险地点的出险次数超过第二预设出险次数，则行业服务器将该出险地点记录为事故高发地点。行业服务器可以将事故高发地点的语音提示信息进行保存，还可以发送至多个保险公司的理赔服务器。
车辆上安装了车载终端，车载终端可以通过网络与行业服务器建立连接，或者与其投保车险的保险公司的理赔服务器建立连接。车载行驶过程中，车载终端实时获取车辆的位置信息，并且将其位置信息上传至行业服务器或理赔服务器。行业服务器或理赔服务器将车辆的位置信息与事故高发地点的位置信息进行比对，当确认车辆行驶至事故高发地点时，行业服务器或理赔服务器向车载终端发送语音提示信息，以使得车载终端进行实时语音提示。以此提示驾驶员谨慎驾驶，避免事故发生。
此外，为了能够有效节省网络流量，车载终端还可以从行业服务器或者理赔服务器下载事故高发地点对应的位置信息以及语言提示信息。车辆行驶过程中，车载终端实时获取车辆的位置信息，当车辆行驶至事故高发地点时， 车载终端进行语音提示。
在一个实施例中，在接收多个理赔服务器上传的车险理赔数据之前，还包括：接收交警服务器发送的事故责任认定数据，并对事故责任认定数据进行保存；接收理赔服务器发送的事故责任查询请求，事故责任查询请求中携带了出险时间和出险车辆的车牌号；根据出险时间和出险车辆的车牌号在事故责任认定数据中进行查询，得到车牌号对应的事故责任；将车牌号对应的事故责任返回至理赔服务器，以使得理赔服务器根据车牌号对应的事故责任对出险车辆进行定损计算。
本实施例中，行业服务器与交警服务器建立连接。如果车险案件中的事故责任不清晰，需要交警来事故现场进行事故责任认定。交警通过交警终端将事故责任认定数据上传至交警服务器。交警服务器获取车险案件对应的事故责任认定数据发送至行业服务器。行业服务器对车险案件对应的事故责任认定数据进行保存。
当保险公司对车险案件进行理赔处理时，对于事故责任不清晰的车险案件需要查询交警认定的事故责任。理赔服务器生成事故责任查询请求，并且将事故责任查询请求发送至行业服务器。行业服务器根据事故责任查询请求中携带了出险时间和出险车辆的车牌号在事故责任认定数据中进行查询，得到车牌号在该车险案件中对应的事故责任，并且将其事故责任返回至理赔服务器。理赔服务器根据车牌号在该车险案件中对应的事故责任对出险车辆进行定损计算。
本实施例中，通过交警服务器与行业服务器之间进行事故责任认定数据共享，方便各个保险公司在进行车险理赔时能够及时获取到车牌号在车险案件中对应的事故责任，以此方便各个保险公司能够快速有效的进行定损计算。
在一个实施例中，如图3所示，行业服务器包括通过系统总线连接的处理器301、内存储器302、非易失性存储介质303和网络接口304。其中，行业服务器的非易失性存储介质303中存储有操作系统3031和操作系统和计算机可读指令，非易失性存储介质303可以是计算机可读非易失性存储介质。该计算机可读指令用于实现一种车险理赔数据分析方法。行业服务器300的处理器301用于提供计算和控制能力，支撑整个行业服务器的运行。行业服务器300的网络接口304用于据以与外部的理赔服务器通过网络连接通信， 比如接收理赔服务器上传的车险理赔数据，向理赔服务器发送高风险车牌号等。行业服务器300可以用独立的服务器或者是多个服务器组成的服务器集群来实现。本领域技术人员可以理解，图3中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的行业服务器的限定，具体的行业服务器可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，如图4所示，提供了一种车险理赔数据分析方法，以该方法应用于理赔服务器为例进行说明，具体包括以下步骤：
步骤402，获取车险理赔数据，车险理赔数据至少包括出险车辆的车牌号和出险地点。
步骤404，将车险理赔数据上传至行业服务器，以使得行业服务器对车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点。
步骤406，接收行业服务器返回的高风险车牌号、高风险出险地点。
步骤408，当接收到携带高风险车牌号和/或高风险出险地点的车险理赔请求时，生成核实是否骗保的提示信息。
本实施例中，理赔服务器获取车险理赔数据，包括出险车辆的车牌号、出险时间、出险地点以及出险原因等。不同保险公司的理赔服务器上所存储的车险理赔数据不同。多个保险公司的理赔服务器将车险理赔数据上传至行业服务器。行业服务器将接收到的车险理赔数据进行保存。
行业服务器按照预设频率对车险理赔数据进行大数据风险分析。行业服务器的风险分析包括高风险车牌号分析和高风险出险地点分析。具体的，行业服务器获取车险理赔数据中的车牌号和对应的出险时间，对多个车牌号和出险时间进行大数据分析，若同一个车牌号在预设时间段内的出险次数超过第一预设出险次数，则将车牌号记录为高风险车牌号。行业服务器还可以获取车险理赔数据中的车牌号、出险地点和出险原因，对多个车牌号对应的出险地点和出险原因进行大数据分析，出险原因包括故意制造现场。行业服务器获取出险原因为故意制造现场的车险案件所对应的出险地点的特征，若具有相同特征的出险地点且出险原因为故意制造现场的车险案件数量大于预设案件量时，则记录出险地点的特征，并将具有特征的出险地点记录为高风险 出险地点。
行业服务器将大数据分析得到的高风险车牌号和高风险出险地点分别发送至多个理赔服务器。理赔服务器接收高风险车牌号和高风险出险地点，并进行保存。当理赔服务器接收到车险报案信息中携带了高风险车牌号和/或高风险出险地点时，理赔服务器生成核验是否骗保的提示信息，并将提示信息发送至理赔终端。由此使得理赔人员对该起车险案件进行核验，以便及时发现骗保的虚假车险案件。从而减少保险公司的车险损失。
本实施例中，多个保险公司的理赔服务器将各自的车险理赔数据发送至行业服务器，行业服务器对多个保险公司的车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号以及高风险出险地点。由于大数据风险分析是基于多个保险公司的车险理赔数据进行的，因此高风险车牌号和高风险出险地点是经过全面的分析得出的。如果车险报案信息中携带了高风险车牌号和/或高风险出险地点，则意味着存在骗保的可能。行业服务器将高风险车牌号以及高风险出险地点发送至多个理赔服务器。当理赔服务器接收到的车险报案信息中包括高风险车牌号和/或高风险出险地点时，则生成核验是否骗保的提示信息，并将提示信息发送至理赔终端。由此能够提示理赔人员对该车险案件要加强核验，以便及时发现骗保的车险案件，降低保险公司的车险损失。
在一个实施例中，在获取车险理赔数据的步骤之前，还包括：接收客户终端上传的车险报案信息；车险报案信息中包括出险时间和出险车辆的车牌号；根据出险时间和出险车辆的车牌号生成事故责任查询请求，将事故责任查询请求发送至行业服务器，以使得行业服务器在已保存的事故责任认定数据中进行查询；接收行业服务器返回的车牌号对应的事故责任，根据事故责任对出险车辆进行定损计算。
本实施例中，当发送车险事故时，客户可以通过客户终端上传车险报案信息。客户终端中安装的车险应用程序可以是保险公司提供的应用程序，也可以是保险公司之外的第三方应用程序，例如，微信。客户可以通过微信公众号进入车险报案页面，继而上传车险报案信息。
当客户终端上传车险报案信息时，为了便于客户快速完成报案。在客户终端登录车险应用程序后，车险应用程序获取客户基本信息，例如，姓名、 车牌号等，应用程序可以直接将客户基本信息输入至报案页面中。客户终端还可以捕捉其所在位置，根据其位置信息获取相应的路段名称，并将路段名称输入至报案页面中。在报案过程中，可以通过应用程序对部分车险报案信息进行自动输入，无需客户逐一进行输入，为客户快速报案提供了方便。
理赔服务器接收到报案信息之后，会生成车险任务，并且将车险任务发送至对应的理赔终端。理赔人员会通过理赔终端进行现场查勘和定损等理赔处理。当保险公司对车险案件进行理赔处理时，对于事故责任不清晰的车险案件需要查询交警认定的事故责任。理赔服务器生成事故责任查询请求，并且将事故责任查询请求发送至行业服务器。行业服务器根据事故责任查询请求中携带了出险时间和出险车辆的车牌号在事故责任认定数据中进行查询，得到车牌号在该车险案件中对应的事故责任，并且将其事故责任返回至理赔服务器。理赔服务器根据车牌号在该车险案件中对应的事故责任对出险车辆进行定损计算。
本实施例中，通过交警服务器与行业服务器之间进行事故责任认定数据共享，方便各个保险公司在进行车险理赔时能够及时获取到车牌号在车险案件中对应的事故责任，以此方便各个保险公司能够快速有效的进行定损计算。
应该理解的是，虽然图2与图4的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2与图4中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
在一个实施例中，如图5所示，提供了一种车险理赔数据分析系统，包括多个理赔服务器502和行业服务器504，其中：
理赔服务器502，用于获取车险理赔数据，车险理赔数据至少包括出险车辆的车牌号和出险地点。
行业服务器504，用于接收多个理赔服务器上传的车险理赔数据，对车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出 险地点。
理赔服务器502还用于接收行业服务器504返回的高风险车牌号、高风险出险地点，当接收到携带高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将提示信息发送至理赔终端。
在其中一个实施例中，该系统还包括：客户终端506和交警服务器508，其中：
客户终端506，用于向理赔服务器上传的车险报案信息；车险报案信息中包括出险时间和出险车辆的车牌号。
交警服务器508，用于获取车险案件的事故责任认定数据，将事故责任认定数据发送至行业服务器。
理赔服务器502还用于根据出险时间和出险车辆的车牌号生成事故责任查询请求，将事故责任查询请求发送至行业服务器504。
行业服务器504还用于根据出险时间和出险车辆的车牌号在事故责任认定数据中进行查询，得到车牌号对应的事故责任。
理赔服务器502还用于接收行业服务器504返回的车牌号对应的事故责任，根据事故责任对出险车辆进行定损计算。
在其中一个实施例中，行业服务器504还用于获取车险理赔数据中的出险地点；对多个出险地点进行大数据分析；若在同一个出险地点的出险次数超过第二预设出险次数，则将出险地点记录为事故高发地点；该系统还包括：车载终端510，用于实时捕捉车辆位置，将车辆位置上传至行业服务器504；当车辆行驶至事故高发地点时，行业服务器504还用于向车载终端510发送事故高发地点的提示信息；车载终端510还用于接收事故高发地点的提示信息，并将提示信息进行语音播放。
进一步的，行业服务器504还可以将事故高发地点的提示信息发送至各个保险公司的理赔服务器502，理赔服务器502与车载终端510建立连接。车载终端510将车辆位置实时上传至理赔服务器502，当当车辆行驶至事故高发地点时，理赔服务器502向车载终端510发送事故高发地点的提示信息；车载终端510还用于接收事故高发地点的提示信息，并将提示信息进行语音播放。以此提醒驾驶员谨慎驾驶，避免事故发生。
一种服务器，包括存储器和一个或多个处理器，存储器中储存有计算机 可读指令，计算机可读指令被处理器执行时，使得一个或多个处理器执行上述各个方法实施例中的步骤。
一个或多个存储有计算机可读指令的非易失性存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行上述各个方法实施例中的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机可读指令来指令相关的硬件来完成，所述的计算机可读指令可存储于一非易失性计算机可读取存储介质中，该计算机可读指令在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种车险理赔数据分析方法，包括：
    接收多个理赔服务器上传的车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
    对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及
    将所述高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得所述理赔服务器在接收到携带所述高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。
  2. 根据权利要求1所述的方法，其特征在于，所述车险理赔数据包括出险车辆的车牌号和出险时间；所述对所述车险理赔数据进行大数据风险分析包括：
    获取所述车险理赔数据中的车牌号和对应的出险时间；
    对多个车牌号和出险时间进行大数据分析；及
    若同一个车牌号在预设时间段内的出险次数超过第一预设出险次数，则将所述车牌号记录为高风险车牌号。
  3. 根据权利要求1所述的方法，其特征在于，所述对所述车险理赔数据进行大数据风险分析包括：
    获取所述车险理赔数据中的车牌号、出险地点和出险原因；
    对多个车牌号对应的出险地点和出险原因进行大数据分析；所述出险原因包括故意制造现场；
    获取出险原因为故意制造现场的车险案件所对应的出险地点的特征；及
    若具有相同特征的出险地点且出险原因为故意制造现场的车险案件数量大于预设案件量时，则记录所述出险地点的特征，并将具有所述特征的出险地点记录为高风险出险地点。
  4. 根据权利要求2或3所述的方法，其特征在于，所述对所述车险理赔数据进行大数据风险分析还包括：
    获取所述车险理赔数据中的出险地点；
    对多个出险地点进行大数据分析；及
    若在同一个出险地点的出险次数超过第二预设出险次数，则将所述出险地点记录为事故高发地点。
  5. 根据权利要求1所述的方法，其特征在于，在所述接收多个理赔服务器上传的车险理赔数据之前，还包括：
    接收交警服务器发送的事故责任认定数据，并对所述事故责任认定数据进行保存；
    接收理赔服务器发送的事故责任查询请求，所述事故责任查询请求中携带了出险时间和出险车辆的车牌号；
    根据所述出险时间和出险车辆的车牌号在所述事故责任认定数据中进行查询，得到所述车牌号对应的事故责任；及
    将所述车牌号对应的事故责任返回至理赔服务器，以使得所述理赔服务器根据所述车牌号对应的事故责任对所述出险车辆进行定损计算。
  6. 一种车险理赔数据分析方法，包括：
    获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
    将所述车险理赔数据上传至行业服务器，以使得所述行业服务器对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；
    接收所述行业服务器返回的高风险车牌号、高风险出险地点；及
    当接收到携带所述高风险车牌号和/或高风险出险地点的车险理赔请求时，生成核实是否骗保的提示信息。
  7. 根据权利要求6所述的方法，其特征在于，在所述获取车险理赔数据之前，还包括：
    接收客户终端上传的车险报案信息；所述车险报案信息中包括出险时间和出险车辆的车牌号；
    根据所述出险时间和所述出险车辆的车牌号生成事故责任查询请求，将所述事故责任查询请求发送至行业服务器，以使得所述行业服务器在已保存的事故责任认定数据中进行查询；及
    接收所述行业服务器返回的所述车牌号对应的事故责任，根据所述事故责任对所述出险车辆进行定损计算。
  8. 一种车险理赔数据分析系统，其特征在于，所述系统包括多个理赔服务器和行业服务器，其中：
    理赔服务器，用于获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
    行业服务器，用于接收多个理赔服务器上传的车险理赔数据，对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；
    所述理赔服务器还用于接收所述行业服务器返回的高风险车牌号、高风险出险地点，当接收到携带所述高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。
  9. 根据权利要求8所述的系统，其特征在于，所述系统还包括：
    客户终端，用于向理赔服务器上传的车险报案信息；所述车险报案信息中包括出险时间和出险车辆的车牌号；
    交警服务器，用于获取车险案件的事故责任认定数据，将所述事故责任认定数据发送至所述行业服务器；
    所述理赔服务器还用于根据所述出险时间和所述出险车辆的车牌号生成事故责任查询请求，将所述事故责任查询请求发送至所述行业服务器；
    所述行业服务器还用于根据所述出险时间和出险车辆的车牌号在所述事故责任认定数据中进行查询，得到所述车牌号对应的事故责任；
    所述理赔服务器还用于接收所述行业服务器返回的所述车牌号对应的事故责任，根据所述事故责任对所述出险车辆进行定损计算。
  10. 根据权利要求8所述的系统，其特征在于，所述行业服务器还用于获取所述车险理赔数据中的出险地点；对多个出险地点进行大数据分析；若在同一个出险地点的出险次数超过第二预设出险次数，则将所述出险地点记录为事故高发地点；
    所述系统还包括：
    车载终端，用于实时捕捉车辆位置，将车辆位置上传至行业服务器；
    当车辆行驶至所述事故高发地点时，所述行业服务器还用于向所述车载终端发送事故高发地点的提示信息；
    所述车载终端还用于接收所述事故高发地点的提示信息，并将所述提示信息进行语音播放。
  11. 一种服务器，包括存储器及一个或多个处理器，所述存储器中储存有计算机可读指令，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    接收多个理赔服务器上传的车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
    对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及
    将所述高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得所述理赔服务器在接收到携带所述高风险车牌号和/或高风险出险地点的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。
  12. 根据权利要求11所述的服务器，其特征在于，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器还执行以下步骤：
    获取所述车险理赔数据中的车牌号、出险地点和出险原因；
    对多个车牌号对应的出险地点和出险原因进行大数据分析；所述出险原因包括故意制造现场；
    获取出险原因为故意制造现场的车险案件所对应的出险地点的特征；及
    若具有相同特征的出险地点且出险原因为故意制造现场的车险案件数量大于预设案件量时，则记录所述出险地点的特征，并将具有所述特征的出险地点记录为高风险出险地点。
  13. 根据权利要求11所述的服务器，其特征在于，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器还执行以下步骤：
    获取所述车险理赔数据中的出险地点；
    对多个出险地点进行大数据分析；及
    若在同一个出险地点的出险次数超过第二预设出险次数，则将所述出险地点记录为事故高发地点。
  14. 一种服务器，包括存储器及一个或多个处理器，所述存储器中储存有计算机可读指令，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
    将所述车险理赔数据上传至行业服务器，以使得所述行业服务器对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；
    接收所述行业服务器返回的高风险车牌号、高风险出险地点；及
    当接收到携带所述高风险车牌号和/或高风险出险地点的车险理赔请求时，生成核实是否骗保的提示信息。
  15. 根据权利要求14所述的服务器，其特征在于，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器还执行以下步骤：
    接收客户终端上传的车险报案信息；所述车险报案信息中包括出险时间和出险车辆的车牌号；
    根据所述出险时间和所述出险车辆的车牌号生成事故责任查询请求，将所述事故责任查询请求发送至行业服务器，以使得所述行业服务器在已保存的事故责任认定数据中进行查询；及
    接收所述行业服务器返回的所述车牌号对应的事故责任，根据所述事故责任对所述出险车辆进行定损计算。
  16. 一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    接收多个理赔服务器上传的车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
    对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；及
    将所述高风险车牌号和高风险出险地点分别返回至多个理赔服务器，以使得所述理赔服务器在接收到携带所述高风险车牌号和/或高风险出险地点 的车险报案信息时，生成核实是否骗保的提示信息，并将所述提示信息发送至理赔终端。
  17. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器还执行以下步骤：获取所述车险理赔数据中的车牌号、出险地点和出险原因；
    对多个车牌号对应的出险地点和出险原因进行大数据分析；所述出险原因包括故意制造现场；
    获取出险原因为故意制造现场的车险案件所对应的出险地点的特征；及
    若具有相同特征的出险地点且出险原因为故意制造现场的车险案件数量大于预设案件量时，则记录所述出险地点的特征，并将具有所述特征的出险地点记录为高风险出险地点。
  18. 根据权利要求16所述的存储介质，其特征在于，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器还执行以下步骤：获取所述车险理赔数据中的车牌号、出险地点和出险原因；
    获取所述车险理赔数据中的出险地点；
    对多个出险地点进行大数据分析；及
    若在同一个出险地点的出险次数超过第二预设出险次数，则将所述出险地点记录为事故高发地点。
  19. 一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    获取车险理赔数据，所述车险理赔数据至少包括出险车辆的车牌号和出险地点；
    将所述车险理赔数据上传至行业服务器，以使得所述行业服务器对所述车险理赔数据进行大数据风险分析，得到车险对应的高风险车牌号、高风险出险地点；
    接收所述行业服务器返回的高风险车牌号、高风险出险地点；及
    当接收到携带所述高风险车牌号和/或高风险出险地点的车险理赔请求时，生成核实是否骗保的提示信息。
  20. 根据权利要求19所述的存储介质，其特征在于，所述计算机可读指 令被一个或多个处理器执行时，使得所述一个或多个处理器还执行以下步骤：接收客户终端上传的车险报案信息；所述车险报案信息中包括出险时间和出险车辆的车牌号；
    根据所述出险时间和所述出险车辆的车牌号生成事故责任查询请求，将所述事故责任查询请求发送至行业服务器，以使得所述行业服务器在已保存的事故责任认定数据中进行查询；及
    接收所述行业服务器返回的所述车牌号对应的事故责任，根据所述事故责任对所述出险车辆进行定损计算。
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