Rapid car insurance claim settlement method and device, computer equipment and storage medium

Abstract

The invention belongs to the field of intelligent decision making, and relates to a rapid vehicle insurance claim settlement method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the desensitization processing of vehicle driving data according to a data desensitization technology, and obtaining desensitized driving data; performing collaborative analysis processing on the desensitization driving data and the historical claim settlement records according to a collaborative analysis technology to obtain a high-risk driving behavior mode and accident associated factors; constructing a claim settlement prediction model according to the high-risk driving behavior mode and the accident associated factors; receiving a car insurance claim settlement request sent by a request terminal; acquiring user driving data corresponding to the request user information from a vehicle enterprise system database, and performing data desensitization processing on the user driving data to obtain desensitized user driving data; and inputting the desensitized user driving data and the accident scene image into a claim settlement prediction model for claim settlement prediction processing to obtain a vehicle insurance claim settlement result. The claim settlement period can be greatly shortened, and the customer satisfaction is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent decision-making technology, and in particular to a method, device, computer equipment and storage medium for rapid automobile insurance claims settlement. Background Art

[0002] Vehicles are the most commonly used means of transportation, which greatly expands people's range of activities. Modern people use vehicles more and more frequently. Most vehicles are insured with commercial insurance while participating in compulsory traffic insurance. When a traffic accident occurs, the owner goes to the insurance company to make a claim. The basic process of claims settlement includes: reporting, investigation and damage assessment, review of documents, review, approval, payment and other links. The process of the whole link is cumbersome and time-consuming. Especially the investigation and damage assessment link, which involves both the damage investigation of the vehicle and the allocation of responsibilities of the parties, often takes a long time to determine.

[0003] However, traditional auto insurance claims rely on manual review, which is cumbersome, time-consuming, and error-prone. Lack of real-time data analysis support makes it difficult to accurately assess accident liability and loss extent. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a rapid auto insurance claims method, apparatus, computer equipment and storage medium to solve the problem that traditional auto insurance claims rely on manual review, the process is cumbersome, time-consuming and prone to errors.

[0005] In order to solve the above technical problems, the present application embodiment provides a method for quick car insurance claims settlement, which adopts the following technical solutions:

[0006] Reading a vehicle manufacturer system database, and obtaining vehicle driving data in the vehicle manufacturer system database;

[0007] Desensitizing the vehicle driving data according to the data desensitization technology to obtain desensitized driving data;

[0008] Reading an insurance system database, and obtaining historical claims records in the insurance system database;

[0009] Perform collaborative analysis on the desensitized driving data and the historical claims records according to collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors;

[0010] Constructing a claims prediction model based on the high-risk driving behavior pattern and the accident-related factors;

[0011] Receiving a motor vehicle insurance claim request sent by a request terminal, wherein the motor vehicle insurance claim request includes requesting user information and an accident scene image;

[0012] Acquire user driving data corresponding to the requesting user information in the automobile enterprise system database, and perform data desensitization processing on the user driving data to obtain desensitized user driving data;

[0013] The desensitized user driving data and the accident scene image are input into the claim prediction model for claim prediction processing to obtain the car insurance claim result.

[0014] Furthermore, after the step of reading the vehicle manufacturer system database and obtaining the vehicle driving data in the vehicle manufacturer system database, the following steps are also included:

[0015] The vehicle driving data is subjected to noise removal and outlier removal processing.

[0016] Furthermore, after the step of constructing a claim prediction model according to the high-risk driving behavior pattern and the accident-related factors, the following steps are also included:

[0017] Performing a first optimization process on the claims prediction model according to feature engineering and model tuning technology to obtain a first optimized claims prediction model;

[0018] The step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result specifically includes the following steps:

[0019] The desensitized user driving data and the accident scene image are input into the first optimized claim prediction model for claim prediction processing to obtain a motor vehicle insurance claim result.

[0020] Furthermore, after the step of performing a first optimization process on the claim prediction model according to feature engineering and model tuning technology to obtain a first optimized claim prediction model, the following steps are also included:

[0021] Performing a second optimization process on the first optimized claims prediction model according to the federated learning technology to obtain a second optimized claims prediction model;

[0022] The step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result specifically includes the following steps:

[0023] The desensitized user driving data and the accident scene image are input into the second optimized claim prediction model for claim prediction processing to obtain a motor vehicle insurance claim result.

[0024] Furthermore, after the step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result, the following steps are also included:

[0025] Verify the auto insurance claim settlement result according to business rules and historical experience data to obtain a verification result;

[0026] If the verification result meets the preset verification condition, the motor insurance claim result is used as the final claim result;

[0027] If the verification result does not meet the preset verification conditions, the model retraining mechanism is triggered, the model is optimized and updated according to the new data, and the desensitized user driving data and the accident scene image are re-processed for claim prediction to obtain the final claim result.

[0028] Furthermore, after the step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result, the following steps are also included:

[0029] The desensitized user driving data, the accident scene image and the car insurance claim result are used as model training data to perform a third optimization process on the claim prediction model.

[0030] In order to solve the above technical problems, the embodiment of the present application also provides a fast car insurance claims settlement device, which adopts the following technical solution:

[0031] A driving data acquisition module, used to read a vehicle manufacturer system database and acquire vehicle driving data in the vehicle manufacturer system database;

[0032] A desensitization module, used for desensitizing the vehicle driving data according to a data desensitization technology to obtain desensitized driving data;

[0033] A claim settlement record acquisition module, used to read the insurance system database and obtain historical claim settlement records in the insurance system database;

[0034] A collaborative analysis module, used to collaboratively analyze and process the desensitized driving data and the historical claims records according to collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors;

[0035] A model building module, used to build a claim prediction model based on the high-risk driving behavior pattern and the accident-related factors;

[0036] A request acquisition module, used to receive a motor vehicle insurance claim request sent by a request terminal, wherein the motor vehicle insurance claim request includes requesting user information and an accident scene image;

[0037] A user driving data acquisition module, used to acquire user driving data corresponding to the requesting user information in the automobile enterprise system database, and perform data desensitization processing on the user driving data to obtain desensitized user driving data;

[0038] The claim prediction module is used to input the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the motor vehicle insurance claim result.

[0039] Furthermore, the device further comprises: a first optimization module, and the claim prediction module comprises: a first claim prediction submodule, wherein:

[0040] The first optimization module is used to perform a first optimization process on the claim prediction model according to feature engineering and model tuning technology to obtain a first optimized claim prediction model;

[0041] The first claim prediction submodule is used to input the desensitized user driving data and the accident scene image into the first optimized claim prediction model for claim prediction processing to obtain a motor insurance claim result.

[0042] Furthermore, the device further comprises: a second optimization module, and the claim prediction module comprises: a second claim prediction submodule, wherein:

[0043] The second optimization module is used to perform a second optimization process on the first optimized claims prediction model according to the federated learning technology to obtain a second optimized claims prediction model;

[0044] The second claim prediction submodule is used to input the desensitized user driving data and the accident scene image into the second optimized claim prediction model for claim prediction processing to obtain a motor insurance claim result.

[0045] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0046] It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned fast car insurance claims settlement method when executing the computer-readable instructions.

[0047] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0048] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the fast car insurance claims settlement method as described above are implemented.

[0049] The present application provides a method for fast motor vehicle insurance claims, comprising: reading a vehicle manufacturer system database, and obtaining vehicle driving data from the vehicle manufacturer system database; desensitizing the vehicle driving data according to a data desensitizing technique to obtain desensitized driving data; reading an insurance system database, and obtaining historical claims records from the insurance system database; collaboratively analyzing the desensitized driving data and the historical claims records according to a collaborative analysis technique to obtain high-risk driving behavior patterns and accident-related factors; constructing a claims prediction model based on the high-risk driving behavior patterns and the accident-related factors; receiving a motor vehicle insurance claim request sent by a request terminal, wherein the motor vehicle insurance claim request includes requesting user information and an accident scene image; obtaining user driving data corresponding to the requesting user information from the vehicle manufacturer system database, and performing data desensitizing on the user driving data to obtain desensitized user driving data; inputting the desensitized user driving data and the accident scene image into the claims prediction model for claims prediction processing to obtain a motor vehicle insurance claim result. Compared with the existing technology, this application uses driving data processed by privacy computing to enable insurance companies to more accurately assess the risk level of each vehicle, customize insurance products, reduce moral risks, and improve the fairness of compensation. Combining AI algorithms with privacy computing, it automatically analyzes accident scene pictures, videos and other information, quickly determines the type of accident and the attribution of responsibility, greatly shortens the claim settlement cycle, and improves customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0052] Figure 2 This is a flowchart of the implementation of the rapid car insurance claims method provided by the embodiment of the present application;

[0053] Figure 3 It is a schematic diagram of the structure of a rapid automobile insurance claims settlement device provided in an embodiment of the present application;

[0054] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0056] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0058] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0059] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0060] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0061] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0062] It should be noted that the fast car insurance claims settlement method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the fast car insurance claims settlement device is generally set in the server / terminal device.

[0063] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0064] Continue to refer Figure 2 , showing a flow chart of an embodiment of a fast car insurance claim settlement method according to the present application. The fast car insurance claim settlement method comprises: step S201, step S202, step S203, step S204, step S205, step S206, step S207 and step S208.

[0065] In step S201, the vehicle manufacturer's system database is read to obtain vehicle driving data from the vehicle manufacturer's system database.

[0066] In the embodiment of the present application, vehicle driving data is extracted from the system database of the automobile enterprise. Such data may include the vehicle's driving speed, driving time, driving mileage, braking frequency, number of sudden accelerations, etc., for subsequent analysis.

[0067] In step S202, the vehicle driving data is desensitized according to the data desensitization technology to obtain desensitized driving data.

[0068] In the embodiments of the present application, data desensitization is a technical means to protect personal privacy and data security. By desensitizing the vehicle driving data, sensitive information in the data, such as the owner's identity, vehicle identification number, etc., can be removed or replaced, thereby protecting user privacy while retaining sufficient information for analysis.

[0069] In the embodiment of this application, the car company continuously collects driving behavior data through vehicle sensors, including mileage, frequency of sudden acceleration / braking, collision events, etc., to ensure the comprehensiveness and timeliness of the data. Before uploading the data to the privacy computing platform, the data is de-identified and sensitive information directly related to personal identity is encrypted.

[0070] In step S203, the insurance system database is read to obtain historical claims records from the insurance system database.

[0071] In the embodiment of the present application, the system deploys a privacy computing environment such as multi-party computing (MPC) or federated learning. In this environment, data can be jointly analyzed without leaving their respective servers. As participants, automobile companies and insurance companies convert data into ciphertext form through encryption algorithms to ensure that the data is unreadable during transmission and calculation.

[0072] In an embodiment of the present application, historical claims records are extracted from the insurance company's system database. These records include information such as past insurance accidents, claim amounts, accident types, etc., which are used for comparative analysis with vehicle driving data.

[0073] In step S204, the desensitized driving data and historical claims records are collaboratively analyzed and processed according to collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors.

[0074] In an embodiment of the present application, collaborative analysis technology is used to conduct a comprehensive analysis of the desensitized vehicle driving data and historical claims records, with the aim of identifying high-risk driving behavior patterns (such as frequent speeding, sudden braking, etc.) and accident-related factors (such as specific time periods, weather conditions, etc.).

[0075] In step S205, a claim prediction model is constructed based on high-risk driving behavior patterns and accident-related factors.

[0076] In an embodiment of the present application, an encryption algorithm is used to collaboratively analyze driving data and historical claims records to identify high-risk driving behavior patterns and accident-related factors.

[0077] In the embodiment of the present application, the federated learning technology is used to jointly train the claims prediction model without exposing personal data to improve the accuracy and generalization ability of the model.

[0078] In the embodiment of the present application, a car insurance claim prediction model is constructed based on the high-risk driving behavior patterns and accident-related factors obtained by the above analysis. This model can use new driving data and accident scene information to predict the possibility of future car insurance claims.

[0079] In step S206, a motor vehicle insurance claim settlement request sent by a requesting terminal is received, wherein the motor vehicle insurance claim settlement request includes requesting user information and accident scene images.

[0080] In step S207, the user driving data corresponding to the requested user information is obtained from the automobile manufacturer system database, and the user driving data is desensitized to obtain the desensitized user driving data.

[0081] In step S208, the desensitized user driving data and accident scene images are input into the claim prediction model for claim prediction processing to obtain the car insurance claim result.

[0082] In an embodiment of the present application, when an accident occurs, the car owner uploads on-site photos, videos and other information to the platform through a mobile phone application; the platform uses a trained model to instantly analyze the accident situation, combined with the previous driving behavior analysis results, to quickly determine the accident responsibility and the extent of the loss; based on the analysis results, the insurance company can quickly make a claims decision, reduce the manual review process, and improve claims efficiency.

[0083] In an embodiment of the present application, a fast auto insurance claim method is provided, including: reading a car enterprise system database, obtaining vehicle driving data in the car enterprise system database; desensitizing the vehicle driving data according to data desensitization technology to obtain desensitized driving data; reading an insurance system database, obtaining historical claims records in the insurance system database; performing collaborative analysis on desensitized driving data and historical claims records according to collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors; constructing a claims prediction model based on high-risk driving behavior patterns and accident-related factors; receiving an auto insurance claim request sent by a request terminal, wherein the auto insurance claim request includes requesting user information and an accident scene image; obtaining user driving data corresponding to the requesting user information in the car enterprise system database, and performing data desensitization on the user driving data to obtain desensitized user driving data; inputting the desensitized user driving data and the accident scene image into the claims prediction model for claim prediction processing to obtain an auto insurance claim result. Compared with the prior art, the present application uses driving data processed by privacy computing, so that insurance companies can more accurately assess the risk level of each vehicle, personalize insurance products, reduce moral risks, and improve the fairness of compensation. Combining AI algorithms with privacy computing, it automatically analyzes accident scene pictures, videos and other information, quickly determines the type of accident and liability, significantly shortens the claims settlement cycle, and improves customer satisfaction.

[0084] In some optional implementations of the embodiments of the present application, after the step of reading the vehicle manufacturer system database and obtaining the vehicle driving data in the vehicle manufacturer system database, the following steps are also included:

[0085] The vehicle driving data is processed to remove noise and outliers.

[0086] In an embodiment of the present application, driving data such as the vehicle's speed, acceleration, and steering angle can be collected through on-board sensors, and the Kalman filter algorithm can be used to pre-process the data to remove high-frequency noise and outliers.

[0087] In some optional implementations of the embodiments of the present application, after the step of constructing a claim prediction model according to high-risk driving behavior patterns and accident-related factors, the following steps are also included:

[0088] Performing a first optimization process on the claims prediction model according to feature engineering and model tuning technology to obtain a first optimized claims prediction model;

[0089] The steps of inputting the desensitized user driving data and accident scene images into the claim prediction model for claim prediction processing to obtain the auto insurance claim result specifically include the following steps:

[0090] The desensitized user driving data and accident scene images are input into the first optimized claim prediction model for claim prediction processing to obtain the car insurance claim result.

[0091] In the embodiment of the present application, feature engineering is mainly used to preprocess, transform and select user driving data (such as driving speed, acceleration, braking frequency, etc.), and to extract useful information (such as vehicle damage, road conditions, etc.) from accident scene images. The goal of feature engineering is to create a set of distinguishing features that can accurately reflect the possibility of auto insurance claims in different situations.

[0092] In the present application embodiment, model tuning improves its performance by adjusting the parameters and architecture of the model. It is mainly used to adjust the hyperparameters of the classifier (such as the depth of the decision tree, the kernel function of the support vector machine, etc.), or try different machine learning algorithms (such as logistic regression, random forest, neural network, etc.). The goal of model tuning is to find a model configuration that performs best on the validation set, thereby ensuring that the model can be generalized to unseen data.

[0093] In the embodiment of the present application, after feature engineering and model tuning, we obtained an optimized claim prediction model, namely the first optimized claim prediction model. This model showed good performance on both the training set and the validation set, and is believed to be able to better predict the results of auto insurance claims.

[0094] In some optional implementations of the embodiments of the present application, after performing a first optimization process on the claims prediction model according to feature engineering and model tuning technology to obtain a first optimized claims prediction model, the following steps are also included:

[0095] Performing a second optimization process on the first optimized claims prediction model according to the federated learning technology to obtain a second optimized claims prediction model;

[0096] The steps of inputting the desensitized user driving data and accident scene images into the claim prediction model for claim prediction processing to obtain the auto insurance claim result specifically include the following steps:

[0097] The desensitized user driving data and accident scene images are input into the second optimized claim prediction model for claim prediction processing to obtain the car insurance claim result.

[0098] In the embodiments of the present application, federated learning technology is a distributed machine learning framework that allows multiple parties (such as different companies or organizations) to jointly train models without sharing original data. Each party trains the model on its own data and sends the model parameters (not the original data) to a central server for aggregation to generate a global model. This method protects data privacy and can improve model performance by using multi-party data.

[0099] In the embodiment of the present application, through federated learning, these institutions can jointly train a more powerful claims prediction model without leaking their respective sensitive data.

[0100] In an embodiment of the present application, based on the first optimized claims prediction model, we use federated learning technology to perform the second stage of optimization. This usually involves multiple parties (such as multiple insurance companies or car companies) independently training models on their respective data sets, and regularly sending model parameters to a central server for aggregation. The central server collects the model parameters of all participants, performs weighted averaging or other forms of aggregation, and then generates a new global model. This global model is returned to all participants for the next round of training. Through multiple rounds of iterations, federated learning can gradually optimize the global model so that it can perform well on different data sets.

[0101] In the embodiment of the present application, after multiple rounds of federated learning training, we obtained a more robust and accurate claims prediction model, namely the second optimized claims prediction model. This model not only utilizes data from multiple institutions, but also protects data privacy through the mechanism of federated learning.

[0102] In some optional implementations of the embodiments of the present application, after inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result, the following steps are also included:

[0103] Verify the auto insurance claim results according to business rules and historical experience data to obtain verification results;

[0104] If the verification result meets the preset verification conditions, the car insurance claim result will be used as the final claim result;

[0105] If the verification result does not meet the preset verification conditions, the model retraining mechanism will be triggered, the model will be optimized and updated according to the new data, and the desensitized user driving data and accident scene images will be re-processed for claim prediction to obtain the final claim result.

[0106] In the embodiment of the present application, business rules refer to specific regulations in the auto insurance claims business, which are formulated according to industry norms, company policies or laws and regulations. These rules may involve the upper limit of the claim amount, the claim processing method in specific circumstances, the claim time limit, etc.

[0107] In the present application embodiment, historical experience data refers to past auto insurance claims cases and their processing results. These data can provide references for current claims processing, help identify abnormal claims situations or discover potential claims risks.

[0108] In the embodiment of the present application, verification processing refers to verifying the auto insurance claim results according to business rules and historical experience data. The purpose of this step is to ensure that the claim results comply with business specifications and are consistent with historical experience data, thereby avoiding erroneous or non-compliant claim decisions.

[0109] In the embodiment of the present application, the auto insurance claim results are verified and processed through business rules and historical experience data to ensure the rationality and compliance of the claim results. If the verification results do not meet the preset conditions, the model retraining mechanism is triggered, the model is optimized and updated with new data, and the claim prediction process is re-performed to obtain the final claim result. This method not only improves the accuracy and compliance of claim processing, but also improves the claim service level of auto insurance companies through continuous model optimization.

[0110] In some optional implementations of the embodiments of the present application, after inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result, the following steps are also included:

[0111] The desensitized user driving data, accident scene images and car insurance claim results are used as model training data to perform the third optimization processing on the claim prediction model.

[0112] In the embodiment of the present application, the third optimization process of the claims prediction model is performed by using the desensitized user driving data, accident scene images, and auto insurance claims results as model training data, which is a key step in the optimization process of the auto insurance claims prediction model. Through this step, we can further improve the prediction accuracy of the model, make it better adapt to actual business scenarios, and thus improve the claims service level of auto insurance companies.

[0113] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0114] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0115] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0116] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0117] Further references Figure 3 , as a response to the above Figure 2The present application provides an embodiment of a fast car insurance claims settlement device, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0118] like Figure 3 As shown, the fast car insurance claims settlement device 200 of the embodiment of the present application includes:

[0119] The driving data acquisition module 210 is used to read the vehicle manufacturer system database and obtain the vehicle driving data in the vehicle manufacturer system database;

[0120] The desensitization module 220 is used to perform desensitization processing on the vehicle driving data according to the data desensitization technology to obtain desensitized driving data;

[0121] The claim record acquisition module 230 is used to read the insurance system database and obtain historical claim records in the insurance system database;

[0122] A collaborative analysis module 240 is used to collaboratively analyze and process the desensitized driving data and historical claims records according to collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors;

[0123] A model building module 250, for building a claim prediction model based on high-risk driving behavior patterns and accident-related factors;

[0124] The request acquisition module 260 is used to receive the automobile insurance claim settlement request sent by the request terminal, wherein the automobile insurance claim settlement request includes the requesting user information and the accident scene image;

[0125] The user driving data acquisition module 270 is used to acquire the user driving data corresponding to the requesting user information in the automobile enterprise system database, and perform data desensitization processing on the user driving data to obtain the desensitized user driving data;

[0126] The claim prediction module 280 is used to input the desensitized user driving data and accident scene images into the claim prediction model for claim prediction processing to obtain the car insurance claim result.

[0127] In the embodiment of the present application, vehicle driving data is extracted from the system database of the automobile enterprise. Such data may include the vehicle's driving speed, driving time, driving mileage, braking frequency, number of sudden accelerations, etc., for subsequent analysis.

[0128] In the embodiments of the present application, data desensitization is a technical means to protect personal privacy and data security. By desensitizing the vehicle driving data, sensitive information in the data, such as the owner's identity, vehicle identification number, etc., can be removed or replaced, thereby protecting user privacy while retaining sufficient information for analysis.

[0129] In the embodiment of this application, the car company continuously collects driving behavior data through vehicle sensors, including mileage, frequency of sudden acceleration / braking, collision events, etc., to ensure the comprehensiveness and timeliness of the data. Before uploading the data to the privacy computing platform, the data is de-identified and sensitive information directly related to personal identity is encrypted.

[0130] In the embodiment of the present application, the system deploys a privacy computing environment such as multi-party computing (MPC) or federated learning. In this environment, data can be jointly analyzed without leaving their respective servers. As participants, automobile companies and insurance companies convert data into ciphertext form through encryption algorithms to ensure that the data is unreadable during transmission and calculation.

[0131] In an embodiment of the present application, historical claims records are extracted from the insurance company's system database. These records include information such as past insurance accidents, claim amounts, accident types, etc., which are used for comparative analysis with vehicle driving data.

[0132] In an embodiment of the present application, collaborative analysis technology is used to conduct a comprehensive analysis of the desensitized vehicle driving data and historical claims records, with the aim of identifying high-risk driving behavior patterns (such as frequent speeding, sudden braking, etc.) and accident-related factors (such as specific time periods, weather conditions, etc.).

[0133] In an embodiment of the present application, an encryption algorithm is used to collaboratively analyze driving data and historical claims records to identify high-risk driving behavior patterns and accident-related factors.

[0134] In the embodiment of the present application, the federated learning technology is used to jointly train the claims prediction model without exposing personal data to improve the accuracy and generalization ability of the model.

[0135] In the embodiment of the present application, a car insurance claim prediction model is constructed based on the high-risk driving behavior patterns and accident-related factors obtained by the above analysis. This model can use new driving data and accident scene information to predict the possibility of future car insurance claims.

[0136] In an embodiment of the present application, when an accident occurs, the car owner uploads on-site photos, videos and other information to the platform through a mobile phone application; the platform uses a trained model to instantly analyze the accident situation, combined with the previous driving behavior analysis results, to quickly determine the accident responsibility and the extent of the loss; based on the analysis results, the insurance company can quickly make a claims decision, reduce the manual review process, and improve claims efficiency.

[0137] In an embodiment of the present application, a fast car insurance claims settlement device 200 is provided, including: a driving data acquisition module 210, which is used to read the car enterprise system database and obtain vehicle driving data in the car enterprise system database; a desensitization module 220, which is used to desensitize the vehicle driving data according to the data desensitization technology to obtain desensitized driving data; a claim record acquisition module 230, which is used to read the insurance system database and obtain historical claim records in the insurance system database; a collaborative analysis module 240, which is used to collaboratively analyze and process the desensitized driving data and historical claim records according to the collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors; model construction Module 250 is used to build a claim prediction model based on high-risk driving behavior patterns and accident-related factors; request acquisition module 260 is used to receive a motor insurance claim request sent by a request terminal, wherein the motor insurance claim request includes requesting user information and accident scene images; user driving data acquisition module 270 is used to obtain user driving data corresponding to the requesting user information in the car company system database, and perform data desensitization on the user driving data to obtain desensitized user driving data; claim prediction module 280 is used to input the desensitized user driving data and accident scene images into the claim prediction model for claim prediction processing to obtain the motor insurance claim result. Compared with the prior art, the driving data processed by privacy computing in this application enables insurance companies to more accurately assess the risk level of each vehicle, personalize insurance products, reduce moral risks, and improve the fairness of compensation. Combining AI algorithms with privacy computing, it automatically analyzes accident scene pictures, videos and other information, quickly determines the type of accident and the attribution of responsibility, greatly shortens the claim cycle, and improves customer satisfaction.

[0138] In some optional implementations of the embodiments of the present application, the above-mentioned fast car insurance claims settlement device 200 further includes: a first optimization module, and the claims settlement prediction module includes: a first claims settlement prediction submodule, wherein:

[0139] A first optimization module, used to perform a first optimization process on the claims prediction model according to feature engineering and model tuning technology to obtain a first optimized claims prediction model;

[0140] The first claim prediction submodule is used to input the desensitized user driving data and accident scene images into the first optimized claim prediction model for claim prediction processing to obtain the motor vehicle insurance claim result.

[0141] In some optional implementations of the embodiments of the present application, the above-mentioned fast car insurance claims settlement device 200 further includes: a second optimization module, and the claims settlement prediction module includes: a second claims settlement prediction submodule, wherein:

[0142] A second optimization module, used for performing a second optimization process on the first optimized claims prediction model according to the federated learning technology to obtain a second optimized claims prediction model;

[0143] The second claim prediction submodule is used to input the desensitized user driving data and accident scene images into the second optimized claim prediction model for claim prediction processing to obtain the motor insurance claim result.

[0144] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of a computer device according to an embodiment of the present application.

[0145] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 300 having components 310-330, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0146] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0147] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 310 can be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 310 can also be an external storage device of the computer device 300, such as a plug-in hard disk equipped on the computer device 300, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Of course, the memory 310 can also include both the internal storage unit of the computer device 300 and its external storage device. In the embodiment of the present application, the memory 310 is generally used to store the operating system and various application software installed on the computer device 300, such as computer-readable instructions of the fast car insurance claim settlement method, etc. In addition, the memory 310 can also be used to temporarily store various data that have been output or are to be output.

[0148] The processor 320 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiment of the present application, the processor 320 is used to run the computer-readable instructions stored in the memory 310 or process data, such as running the computer-readable instructions of the fast car insurance claim settlement method.

[0149] The network interface 330 may include a wireless network interface or a wired network interface. The network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0150] The computer equipment provided by this application uses driving data processed by privacy computing to enable insurance companies to more accurately assess the risk level of each vehicle, customize insurance products, reduce moral risks, and improve the fairness of compensation. Combining AI algorithms with privacy computing, it automatically analyzes accident scene pictures, videos and other information, quickly determines the type of accident and the attribution of responsibility, significantly shortens the claims cycle, and improves customer satisfaction.

[0151] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the fast car insurance claims method as described above.

[0152] The computer-readable storage medium provided by this application uses driving data processed by privacy computing to enable insurance companies to more accurately assess the risk level of each vehicle, customize insurance products, reduce moral risks, and improve the fairness of compensation. Combining AI algorithms with privacy computing, it automatically analyzes accident scene pictures, videos and other information, quickly determines the type of accident and the attribution of responsibility, significantly shortens the claim settlement cycle, and improves customer satisfaction.

[0153] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0154] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A fast car insurance claim settlement method, characterized in that: The steps include: Reading a vehicle manufacturer system database, and obtaining vehicle driving data in the vehicle manufacturer system database; Desensitizing the vehicle driving data according to the data desensitization technology to obtain desensitized driving data; Reading an insurance system database, and obtaining historical claims records in the insurance system database; Perform collaborative analysis on the desensitized driving data and the historical claims records according to collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors; Constructing a claims prediction model based on the high-risk driving behavior pattern and the accident-related factors; Receiving a motor vehicle insurance claim request sent by a request terminal, wherein the motor vehicle insurance claim request includes requesting user information and an accident scene image; Acquire user driving data corresponding to the requesting user information in the automobile enterprise system database, and perform data desensitization processing on the user driving data to obtain desensitized user driving data; The desensitized user driving data and the accident scene image are input into the claim prediction model for claim prediction processing to obtain the car insurance claim result.

2. The rapid car insurance claims method according to claim 1, characterized in that: After the step of reading the vehicle manufacturer system database and obtaining the vehicle driving data in the vehicle manufacturer system database, the following steps are also included: The vehicle driving data is subjected to noise removal and outlier removal processing.

3. The rapid car insurance claims settlement method according to claim 1, characterized in that: After the step of constructing a claim prediction model according to the high-risk driving behavior pattern and the accident-related factors, the following steps are also included: Performing a first optimization process on the claims prediction model according to feature engineering and model tuning technology to obtain a first optimized claims prediction model; The step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result specifically includes the following steps: The desensitized user driving data and the accident scene image are input into the first optimized claim prediction model for claim prediction processing to obtain a motor vehicle insurance claim result.

4. The rapid car insurance claims method according to claim 3, characterized in that: After the step of performing a first optimization process on the claim prediction model according to feature engineering and model tuning technology to obtain a first optimized claim prediction model, the following steps are also included: Performing a second optimization process on the first optimized claims prediction model according to the federated learning technology to obtain a second optimized claims prediction model; The step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result specifically includes the following steps: The desensitized user driving data and the accident scene image are input into the second optimized claim prediction model for claim prediction processing to obtain a motor vehicle insurance claim result.

5. The rapid car insurance claims settlement method according to claim 1, characterized in that: After the step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result, the following steps are also included: Verify the auto insurance claim settlement result according to business rules and historical experience data to obtain a verification result; If the verification result meets the preset verification condition, the motor insurance claim result is used as the final claim result; If the verification result does not meet the preset verification conditions, the model retraining mechanism is triggered, the model is optimized and updated according to the new data, and the desensitized user driving data and the accident scene image are re-processed for claim prediction to obtain the final claim result.

6. The rapid car insurance claims method according to claim 1, characterized in that: After the step of inputting the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the auto insurance claim result, the following steps are also included: The desensitized user driving data, the accident scene image and the car insurance claim result are used as model training data to perform a third optimization process on the claim prediction model.

7. A fast car insurance claims settlement device, characterized in that: include: A driving data acquisition module, used to read a vehicle manufacturer system database and acquire vehicle driving data in the vehicle manufacturer system database; A desensitization module, used for desensitizing the vehicle driving data according to a data desensitization technology to obtain desensitized driving data; A claim settlement record acquisition module, used to read the insurance system database and obtain historical claim settlement records in the insurance system database; A collaborative analysis module, used to collaboratively analyze and process the desensitized driving data and the historical claims records according to collaborative analysis technology to obtain high-risk driving behavior patterns and accident-related factors; A model building module, used to build a claim prediction model based on the high-risk driving behavior pattern and the accident-related factors; A request acquisition module, used to receive a motor vehicle insurance claim request sent by a request terminal, wherein the motor vehicle insurance claim request includes requesting user information and an accident scene image; A user driving data acquisition module, used to acquire user driving data corresponding to the requesting user information in the automobile enterprise system database, and perform data desensitization processing on the user driving data to obtain desensitized user driving data; The claim prediction module is used to input the desensitized user driving data and the accident scene image into the claim prediction model for claim prediction processing to obtain the motor vehicle insurance claim result.

8. The rapid car insurance claims settlement device according to claim 7, characterized in that: The device further comprises: a first optimization module, and the claim prediction module comprises: a first claim prediction submodule, wherein: The first optimization module is used to perform a first optimization process on the claim prediction model according to feature engineering and model tuning technology to obtain a first optimized claim prediction model; The first claim prediction submodule is used to input the desensitized user driving data and the accident scene image into the first optimized claim prediction model for claim prediction processing to obtain a motor insurance claim result.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the fast car insurance claims settlement method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the fast car insurance claims settlement method as described in any one of claims 1 to 6.

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