CN115545958A

CN115545958A - Intelligent vehicle insurance evaluation method and device, computer equipment and storage medium

Info

Publication number: CN115545958A
Application number: CN202211351933.6A
Authority: CN
Inventors: 江恒星
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2022-12-30

Abstract

The embodiment of the application belongs to the technical field of artificial intelligence, is applied to the field of intelligent transportation, and relates to an intelligent vehicle insurance assessment method, an intelligent vehicle insurance assessment device, computer equipment and a storage medium, wherein the intelligent vehicle insurance assessment method comprises the steps of receiving client intention protocol information, calling a preset network interface, and acquiring corresponding vehicle configuration information through the network interface according to the client intention protocol information; cleaning vehicle configuration information, obtaining target vehicle information, and storing the target vehicle information into a database; when a data analysis notice is received, the target vehicle information is called from a database and is input into a pre-trained safety analysis model to obtain the risk probability; acquiring all pre-configured agreement types, generating a claim amount based on the agreement types and the risk probability, inputting the risk probability and the claim amount into a pre-trained vehicle risk assessment model, and acquiring an output assessment result. Among other things, the security analysis model may be stored in a blockchain. The method and the device can be used for accurately evaluating the vehicle insurance of different users.

Description

Intelligent vehicle insurance evaluation method and device, computer equipment and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to an intelligent vehicle insurance assessment method and device, computer equipment and a storage medium.

Background

At present, a traditional vehicle insurance evaluation model is relatively simple, and only basic factors such as the purchase price, the purchase age, the historical insurance situation and the like of a vehicle are generally considered. The automobile insurance evaluation calculated by using the traditional automobile insurance evaluation model is not fine enough, for example, dangerous types such as responsibility insurance of a third party of the automobile, responsibility insurance of personnel (drivers and passengers) on the automobile of the automobile and the like are related to the insurance amount basically, factors such as actual insurance probability, insurance paying amount and the like are not considered, the most accurate and most suitable quotation cannot be intelligently provided for a user, and more accurate evaluation cannot be intelligently provided for the user.

Disclosure of Invention

The embodiment of the application aims to provide an intelligent vehicle insurance assessment method, an intelligent vehicle insurance assessment device, computer equipment and a storage medium, and vehicle insurance of different users is accurately assessed.

In order to solve the above technical problem, an embodiment of the present application provides an intelligent vehicle insurance assessment method, which adopts the following technical scheme:

an intelligent vehicle insurance assessment method comprises the following steps:

receiving client intention protocol information, calling a preset network interface, and acquiring corresponding vehicle configuration information through the network interface according to the client intention protocol information;

cleaning the vehicle configuration information, obtaining target vehicle information, and storing the target vehicle information into a database;

when a data analysis notice is received, the target vehicle information is called from the database and is input into a pre-trained safety analysis model to obtain the risk probability;

acquiring all pre-configured agreement types, generating a claim amount based on the agreement types and the risk probability, inputting the risk probability and the claim amount into a pre-trained vehicle risk assessment model, and acquiring an output vehicle risk assessment.

Further, the step of obtaining the corresponding vehicle configuration information through the network interface according to the client intention agreement information includes:

extracting a target number in the client intention protocol information;

and matching the target number in a preset vehicle database through the network interface to obtain the vehicle configuration information.

Further, the step of extracting the destination number in the customer intention agreement information comprises:

extracting a field corresponding to the position information in the client intention protocol information based on preset position information to serve as a target number; or alternatively

And identifying continuous numerical data which accord with a preset length in the client intention protocol information as the target number.

Further, the step of inputting the target vehicle information into a pre-trained safety analysis model to obtain the risk probability includes:

extracting a safety configuration table and an auxiliary configuration table of the target vehicle information, inputting the safety configuration table and the auxiliary configuration table into a pre-trained safety analysis model, and obtaining an output safety protection level;

acquiring the risk probability corresponding to the safety protection level based on a pre-configured level mapping table;

further, the step of extracting the safety configuration table and the auxiliary configuration table of the target vehicle information, inputting the safety configuration table and the auxiliary configuration table into a pre-trained safety analysis model, and obtaining an output safety protection level includes:

converting the safety configuration table and the auxiliary configuration table into word vectors through a pre-trained BERT model and splicing to obtain target vectors;

and inputting the target vector into the safety analysis model to obtain the safety protection level.

Further, the step of generating a reimbursement based on the agreement type and the probability of venture includes:

extracting a basic configuration table of the target vehicle information, inquiring the insurance ratio of the same type of vehicle in a historical record in a database based on the basic configuration table, and inquiring transaction price information of the same type of vehicle in real time through a browser;

inputting the insurance ratio and the transaction price information into a pre-trained value analysis model to obtain output value data;

and respectively calculating the benefits corresponding to the agreement types based on the calculation formulas corresponding to the agreement types, the risk probability and the value data.

Further, the step of cleaning the vehicle configuration information and obtaining the target vehicle information includes:

performing full storage operation on the vehicle configuration information;

judging whether key fields in the vehicle configuration information are missing or not, if so, deleting the corresponding vehicle configuration information to obtain the cleaned vehicle configuration information;

and splitting the cleaned vehicle configuration information based on fields corresponding to preset categories to obtain a plurality of storage tables with association relations, wherein the storage tables are used as the target vehicle information.

In order to solve the technical problem, an embodiment of the present application further provides an intelligent vehicle insurance evaluation apparatus, which adopts the following technical scheme:

an intelligent vehicle insurance assessment device, comprising:

the receiving module is used for receiving the client intention protocol information, calling a preset network interface and acquiring corresponding vehicle configuration information through the network interface according to the client intention protocol information;

the cleaning module is used for cleaning the vehicle configuration information, obtaining target vehicle information and storing the target vehicle information into a database;

the calling module is used for calling the target vehicle information from the database when a data analysis notice is received, and inputting the target vehicle information into a pre-trained safety analysis model to obtain the risk probability;

and the generating module is used for acquiring all pre-configured agreement types, generating a claim amount based on the agreement types and the claim probability, and inputting the claim probability and the claim amount into a pre-trained vehicle insurance evaluation model to obtain an output evaluation result.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

a computer device comprises a memory and a processor, wherein the memory stores computer readable instructions, and the processor realizes the steps of the intelligent vehicle insurance assessment method when executing the computer readable instructions.

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

a computer readable storage medium having computer readable instructions stored thereon, which when executed by a processor, implement the steps of the intelligent vehicle insurance assessment method described above.

Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:

the method and the device for acquiring the vehicle configuration information acquire complete vehicle configuration information through the network interface based on the client intention protocol information. The target vehicle information is obtained by cleaning the vehicle configuration information so as to obtain complete and structured data, and the subsequent data AI analysis processing process can be performed quickly and smoothly. When the data analysis notice is received, target vehicle information is called and input into the safety analysis model to obtain the output risk probability, accurate prediction of the risk probability is achieved through the AI model, then the corresponding claim amount of each agreement type is generated according to the risk probability and all the agreement types, and further the final evaluation result is obtained according to the claim amount, so that accurate evaluation of vehicle risks of different users is achieved.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram to which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of an intelligent vehicle insurance assessment method according to the present application;

FIG. 3 is a schematic structural diagram of one embodiment of an intelligent vehicle insurance assessment device according to the present application;

FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.

Reference numerals: 200. a computer device; 201. a memory; 202. a processor; 203. a network interface; 300. an intelligent vehicle insurance evaluation device; 301. a receiving module; 302. a cleaning module; 303. a calling module; 304. and generating a module.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein may be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

A user may use

terminal devices

101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III, motion Picture Experts Group Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion Picture Experts Group Audio Layer 4), a laptop portable computer, a desktop computer, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that, the intelligent vehicle insurance evaluation method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the intelligent vehicle insurance evaluation apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow diagram of one embodiment of an intelligent vehicle insurance assessment method according to the application is shown. The intelligent vehicle insurance evaluation method comprises the following steps:

s1: receiving client intention protocol information, calling a preset network interface, and acquiring corresponding vehicle configuration information through the network interface according to the client intention protocol information;

in this embodiment, the system of the present application includes two subsystems, a data warehouse and an AI brain. After the client fills in the client intention protocol information, namely the insurance information, the data warehouse subsystem acquires the vehicle configuration information by using a preset network interface, and can establish a database of the data warehouse subsystem in the actual operation without connecting an external vehicle database through the network interface for data query. In addition, in the process of calling the network interface, an authentication operation exists, and authentication is performed according to the requirement of an interface provider. The vehicle configuration information is cleaned and stored in a database in the data warehouse subsystem and the AI brain is notified that new data can be analyzed.

In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the intelligent vehicle insurance assessment method operates may receive the customer intention agreement information through a wired connection manner or a wireless connection manner. It is noted that the wireless connection may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a UWB (ultra wideband) connection, and other wireless connection now known or developed in the future.

Wherein the step of obtaining the corresponding vehicle configuration information through the network interface according to the client intention agreement information includes:

extracting a target number in the client intention protocol information;

In this embodiment, the destination number is a frame number or a license plate number. And inquiring the vehicle configuration information through the frame number or the license plate number.

Further, the step of extracting the destination number in the client intention agreement information comprises:

extracting a field corresponding to the position information in the client intention protocol information based on preset position information to serve as a target number; or

In this embodiment, a field of corresponding location information in the client intention protocol information is extracted by preset location information as a destination number. Or, identifying continuous numerical data in the client intention protocol information, which conforms to a preset length, as the target number.

S2: cleaning the vehicle configuration information, obtaining target vehicle information, and storing the target vehicle information into a database;

in this embodiment, the vehicle configuration information is cleaned, and the vehicle configuration information with invalid fields and missing key fields is removed to obtain the target vehicle information. And storing the target vehicle information into a database.

Wherein the step of cleaning the vehicle configuration information to obtain target vehicle information comprises:

performing full storage operation on the vehicle configuration information;

In this embodiment, on the basis of the full storage, three different types of database storage tables, namely a basic configuration table, a safety configuration table and an auxiliary configuration table, are established, and each configuration is stored according to the type, and the cleaned data table is obtained by using the vehicle VIN code as the main key.

The specific cleaning process comprises three steps: full storage, key field deletion and splitting classification, and the specific process is as follows:

1) And (3) full storage: after the vehicle detailed configuration information is obtained, the vehicle VIN number is used as a main key for full storage, namely all vehicle configuration information is stored.

2) Missing the key field: and judging whether the key fields in the vehicle configuration information are missing or not, if so, deleting the corresponding vehicle configuration information, and obtaining the cleaned vehicle configuration information. And if the key fields are not missing, directly taking the vehicle configuration information as the target vehicle information.

3) Splitting and classifying: the categories to which the vehicle configuration items belong are mainly classified into 3 types of tables: a base configuration table, a security configuration table, and an auxiliary configuration table. The basic configuration table comprises automobile general attribute fields such as manufacturer guide price, manufacturer, grade, energy type, environmental protection standard, time to market and the like. The safety configuration table includes vehicle-to-vehicle difference attribute fields such as a main driver seat airbag, a passenger seat airbag, a front-row side airbag, a rear-row side airbag, a knee airbag, a vehicle body stability control, and an active brake, and these configuration attributes generally have a direct influence on driving safety. The auxiliary configuration table comprises automobile matching configurations such as electronic engine anti-theft, internet of vehicles, front parking radar, rear parking radar, driving auxiliary images and uphill auxiliary, and the configuration attributes can generally improve driving safety.

S3: when a data analysis notice is received, the target vehicle information is called from the database and is input into a pre-trained safety analysis model to obtain the risk probability;

in the embodiment, the risk probability is obtained through the safety analysis model and the complete target vehicle information, and more accurate risk probability is obtained.

The step of inputting the target vehicle information into a pre-trained safety analysis model to obtain the risk probability comprises the following steps:

extracting a safety configuration table and an auxiliary configuration table of the target vehicle information, and inputting the safety configuration table and the auxiliary configuration table into a pre-trained safety analysis model to obtain an output safety protection level;

and acquiring the risk probability corresponding to the safety protection level based on a pre-configured level mapping table. In this embodiment, after acquiring the full configuration information of the vehicle from the data warehouse subsystem, the AI brain intelligently analyzes the configuration of the existing dangerous species and the relevant configuration of the vehicle to obtain the probability of occurrence and the amount of claims. The specific generation process of the risk probability comprises the following steps:

and (3) combining data of a safety configuration table and an auxiliary configuration table, such as information of the configuration condition of the whole vehicle air bag of the vehicle, whether a vehicle body stabilizing system is provided or not, whether the safety configuration of an active brake is provided or not and the like, and obtaining indexes such as safety protection levels of a driver and passengers (for example, very low-no air bag of the whole vehicle, lower-only air bag of a main driver, low-only air bag of a main driver and auxiliary driver, middle-main and auxiliary driver and rear-row side air bag are provided, high-main and auxiliary air bag, rear-row side air bag, knee air bag and even other air bag air curtains) and accident transmission potential probability (for example, low-no vehicle body stabilizing system and no active brake, general-vehicle body stabilizing system but no active brake, high-vehicle body stabilizing system and active brake) and the like through AI technical analysis and calculation.

Further, the step of extracting the safety configuration table and the auxiliary configuration table of the target vehicle information, inputting the safety configuration table and the auxiliary configuration table into a pre-trained safety analysis model, and obtaining an output safety protection level comprises:

In this embodiment, data in the security configuration table and the auxiliary configuration table are converted into word vectors, and then the word vectors are input into the security analysis model, so that an accurate security protection level is obtained. The safety analysis model is constructed based on a neural network and can fully extract data information.

S4: acquiring all pre-configured agreement types, generating a claim amount based on the agreement types and the risk probability, inputting the risk probability and the claim amount into a pre-trained vehicle risk evaluation model, and acquiring an output evaluation result.

In this embodiment, a refined car insurance evaluation result of the corresponding protocol type is further calculated by the car insurance evaluation model. And storing the evaluation result of the vehicle insurance into a vehicle evaluation result data table in a data warehouse so as to carry out backup of vehicle insurance evaluation. The automobile insurance evaluation model is based on the automobile portrait, and calculation rules and calculation formulas related to the automobile insurance evaluation model are visual and configurable. For example: for the liability insurance risk types of the third parties of the motor vehicles, the vehicle risk assessment model not only considers basic information such as vehicle quotation, vehicle service life and the like, but also obtains the safety configuration condition of the vehicle from the vehicle full configuration information, such as whether the vehicle is provided with an active brake safety system, whether the vehicle is provided with a pedestrian protection function, accessory price and the like, obtains intelligent and refined final quotation through comprehensive analysis of multi-dimensional information, and outputs corresponding assessment results through quotation, specifically: if the output quotation of a certain agreement type (namely, the car insurance risk type) exceeds the historical average quotation of the agreement type, the output evaluation result is that the insurance application is not suggested; if the output quotation of a certain agreement type is lower than the historical average quotation of the agreement type, the output evaluation result is a proposal application; if the output price quote of a certain agreement type is equal to the historical average price quote of the agreement type, the output evaluation result is that the insurance can be considered. And finally, displaying the evaluation results of all protocol types in a front-end page so as to be used for the user to select the vehicle insurance. By means of the AI technology, automobile data are deeply analyzed, an automobile danger assessment model based on automobile images is generated, assistance is given to accuracy of automobile danger assessment, and user experience and insurance application will be improved for intelligent and accurate assessment of all types of automobile dangers of users.

Wherein the step of generating a payout based on the agreement type and the probability of occurrence comprises:

In this embodiment, based on the basic configuration table information, an internal existing interface or an interface provided by a third party is called, or data provided by a big data platform is used to query the insurance ratio, insurance record details and transaction price information (for example, second-hand vehicle transaction price) of the same type of vehicle, and current value data (for example, indexes such as maintenance economy and the like) of the type of vehicle is obtained through AI technical analysis and calculation.

It is emphasized that, to further ensure the privacy and security of the security analysis model, the security analysis model may also be stored in a node of a blockchain.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

The application can be applied to the field of intelligent traffic, and therefore the construction of an intelligent city is promoted.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, can include processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an intelligent vehicle insurance assessment apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various electronic devices.

As shown in fig. 3, the intelligent vehicle risk assessment apparatus 300 according to the embodiment includes: a receiving module 301, a cleaning module 302, a retrieving module 303, and a generating module 304. Wherein: the receiving module 301 is configured to receive the customer intention protocol information, call a preset network interface, and obtain corresponding vehicle configuration information through the network interface according to the customer intention protocol information; a cleaning module 302, configured to clean the vehicle configuration information, obtain target vehicle information, and store the target vehicle information in a database; the calling module 303 is configured to, when receiving a data analysis notification, call the target vehicle information from the database, and input the target vehicle information into a pre-trained safety analysis model to obtain a risk probability; the generating module 304 is configured to obtain all preconfigured agreement types, generate a claim amount based on the agreement types and the claim probability, and input the claim probability and the claim amount into a pre-trained vehicle insurance evaluation model to obtain an output evaluation result.

In the embodiment, the complete vehicle configuration information is acquired through the network interface based on the client intention agreement information. The target vehicle information is obtained by cleaning the vehicle configuration information so as to obtain complete and structured data, and the subsequent data AI analysis processing process can be performed quickly and smoothly. When the data analysis notice is received, target vehicle information is called and input into the safety analysis model to obtain the output risk probability, accurate prediction of the risk probability is achieved through the AI model, then the corresponding claim amount of each agreement type is generated according to the risk probability and all the agreement types, and further the final evaluation result is obtained according to the claim amount, so that accurate evaluation of vehicle risks of different users is achieved.

The receiving module 301 includes an extracting sub-module and a matching sub-module, where the extracting sub-module is configured to extract a target number in the client intention protocol information; and the matching sub-module is used for matching the target number in a preset vehicle database through the network interface to obtain the vehicle configuration information.

The extraction submodule comprises a first extraction unit and/or a second extraction unit, wherein the first extraction unit is used for extracting a field corresponding to the position information in the client intention protocol information based on preset position information to serve as a target number; the second extraction unit is used for identifying continuous numerical data which accord with a preset length in the client intention protocol information as the target number.

The cleaning module 302 comprises a full storage submodule, a judgment submodule and a splitting submodule, wherein the full storage submodule is used for performing full storage operation on the vehicle configuration information; the judging submodule is used for judging whether key fields in the vehicle configuration information are missing or not, if so, deleting the corresponding vehicle configuration information, and obtaining the cleaned vehicle configuration information; the splitting submodule is used for splitting the cleaned vehicle configuration information based on fields corresponding to preset categories to obtain a plurality of storage tables with association relations, and the storage tables are used as the target vehicle information.

The calling module 303 comprises a calling submodule and an obtaining submodule, wherein the calling submodule is used for extracting a security configuration table and an auxiliary configuration table of the target vehicle information, inputting the security configuration table and the auxiliary configuration table into a pre-trained security analysis model, and obtaining an output security protection level; the obtaining submodule is used for obtaining the risk probability corresponding to the safety protection level based on a pre-configured level mapping table.

The input sub-module comprises a conversion unit and an input unit, wherein the conversion unit is used for converting the safety configuration table and the auxiliary configuration table into word vectors through a pre-trained BERT model and splicing the word vectors to obtain a target vector; the input unit is used for inputting the target vector into the safety analysis model to obtain the safety protection level.

The generation module 304 comprises a query submodule, an input submodule and a calculation submodule, wherein the query submodule is used for extracting a basic configuration table of the target vehicle information, querying the insurance proportion of the same type of vehicle in a historical record in a database based on the basic configuration table, and querying transaction price information of the same type of vehicle in real time through a browser; the input submodule is used for inputting the venture ratio and the transaction price information into a pre-trained value analysis model to obtain output value data; the calculation submodule is used for calculating the benefits corresponding to the protocol types respectively based on the calculation formulas corresponding to the protocol types, the risk probability and the value data.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 200 comprises a memory 201, a processor 202, a network interface 203 communicatively connected to each other via a system bus. It is noted that only computer device 200 having components 201-203 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 201 includes at least one type of readable storage medium including flash memory, hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disks, optical disks, etc. In some embodiments, the storage 201 may be an internal storage unit of the computer device 200, such as a hard disk or a memory of the computer device 200. In other embodiments, the memory 201 may also be an external storage device of the computer device 200, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 200. Of course, the memory 201 may also include both internal and external storage devices of the computer device 200. In this embodiment, the memory 201 is generally used for storing an operating system installed in the computer device 200 and various application software, such as computer readable instructions of an intelligent vehicle insurance assessment method. Further, the memory 201 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 202 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 202 is generally operative to control overall operation of the computer device 200. In this embodiment, the processor 202 is configured to execute computer-readable instructions stored in the memory 201 or process data, such as computer-readable instructions for executing the intelligent vehicle risk assessment method.

The network interface 203 may comprise a wireless network interface or a wired network interface, and the network interface 203 is generally used for establishing communication connection between the computer device 200 and other electronic devices.

In this embodiment, the accurate prediction of the risk probability is realized through the AI model, and then the corresponding claims of each agreement type are generated according to the risk probability and all the agreement types, and the final evaluation result is obtained according to the claims, so as to realize the accurate evaluation of the vehicle insurance of different users.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the intelligent vehicle insurance assessment method as described above.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), and include instructions for enabling a terminal device (such as a mobile phone, a computer, a server,

air conditioner, or network device, etc.) performs the methods described in the various embodiments of the present application.

It should be understood that the above-described embodiments are merely exemplary of some, and not all, embodiments of the present application, and that the drawings illustrate preferred embodiments of the present application without limiting the scope of the claims appended hereto. This application is capable of embodiments in many different forms and the embodiments are provided so that this disclosure will be thorough and complete. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. An intelligent vehicle insurance assessment method is characterized by comprising the following steps:

acquiring all pre-configured agreement types, generating a claim amount based on the agreement types and the risk probability, inputting the risk probability and the claim amount into a pre-trained vehicle risk evaluation model, and acquiring an output evaluation result.

2. The intelligent vehicle insurance assessment method according to claim 1, wherein the step of obtaining the corresponding vehicle configuration information via the network interface according to the customer intention agreement information comprises:

extracting a target number in the client intention protocol information;

3. The intelligent vehicle insurance assessment method according to claim 2, wherein the step of extracting the destination number in the customer intention agreement information comprises:

4. The intelligent vehicle insurance assessment method according to claim 1, wherein the step of inputting the target vehicle information into a pre-trained safety analysis model to obtain the risk probability comprises:

and acquiring the risk probability corresponding to the safety protection level based on a pre-configured level mapping table.

5. The intelligent vehicle insurance assessment method according to claim 4, wherein the steps of extracting the safety configuration table and the auxiliary configuration table of the target vehicle information, inputting the safety configuration table and the auxiliary configuration table into a pre-trained safety analysis model, and obtaining an output safety protection level comprise:

converting the security configuration table and the auxiliary configuration table into word vectors through a pre-trained BERT model and splicing to obtain target vectors;

6. The intelligent vehicle insurance assessment method according to claim 1, wherein the step of generating a payout amount based on the agreement type and the probability of occurrence comprises:

extracting a basic configuration table of the target vehicle information, inquiring the insurance proportion of the same type of vehicle in a historical record in a database based on the basic configuration table, and inquiring transaction price information of the same type of vehicle in real time through a browser;

7. The intelligent vehicle insurance assessment method according to claim 1, wherein the step of washing the vehicle configuration information to obtain target vehicle information comprises:

performing a full storage operation on the vehicle configuration information;

8. An intelligent vehicle insurance assessment apparatus, comprising:

the calling module is used for calling the target vehicle information from the database when receiving a data analysis notice, and inputting the target vehicle information into a pre-trained safety analysis model to obtain the risk probability;

and the generation module is used for acquiring all pre-configured agreement types, generating a claim amount based on the agreement types and the claim probability, and inputting the claim probability and the claim amount into a pre-trained vehicle insurance evaluation model to obtain an output evaluation result.

9. A computer device comprising a memory having computer readable instructions stored therein and a processor that when executed performs the steps of the intelligent vehicle insurance assessment method according to any one of claims 1 to 7.

10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the steps of the intelligent vehicle insurance assessment method according to any one of claims 1 to 7.