Vehicle anomaly probability analysis method and system, computer equipment and storage medium

Abstract

The invention discloses a vehicle abnormal probability analysis method and system, computer equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a part configuration list of a target vehicle; on the basis of the part configuration list, generating a part abnormal probability value of each part in the part configuration list through a preset part abnormal probability prediction model; and on the basis of the abnormal probability values of all the parts in the part configuration list, the whole vehicle abnormal probability value of the target vehicle is obtained through integration. Through the method of the invention, the vehicle abnormity probability value of the target vehicle can be obtained through timely and accurate analysis, and then an insurance company can be helped to carry out insurance pricing of the target vehicle based on the vehicle abnormity probability value.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a vehicle abnormality probability analysis method, system, computer equipment and non-volatile computer-readable storage medium. Background Art

[0002] At present, with the rapid development of new energy vehicles in recent years, the average annual growth rate of new energy vehicles has increased year by year. New energy vehicle technology is also constantly innovating, and breakthroughs have been made in core technologies such as batteries, motors, and electronic controls. At present, with the continuous influx of new energy vehicle emerging technologies and brands into the market, insurance companies are also facing new challenges in cost pricing based on vehicle risks. On the one hand, due to the barriers of emerging technologies, it is difficult for insurance company professionals to respond to emerging technologies in a timely manner in the short term. On the other hand, the existing vehicle risk prediction model is a risk prediction based on historical vehicle data. Due to the lack of historical data for newly launched models, the difficulty for insurance companies to timely and accurately estimate vehicle risks for newly launched models has also increased significantly.

[0003] Based on this situation, how to provide a vehicle abnormality probability analysis method, system, computer equipment and non-volatile computer-readable storage medium that can timely and accurately analyze the vehicle abnormality probability value of the target vehicle is a problem that urgently needs to be solved by technical personnel in this field. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a vehicle abnormality probability analysis method, system, computer equipment and non-volatile computer-readable storage medium that can be used in financial technology or other related fields, aiming to solve the problem of how to timely and accurately analyze and obtain the vehicle abnormality probability value of the target vehicle.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A vehicle abnormality probability analysis method, comprising:

[0007] Obtain the component configuration list of the target vehicle;

[0008] Based on the component configuration list, a component abnormality probability value of each component in the component configuration list is generated by a preset component abnormality probability prediction model;

[0009] Based on the abnormal probability values ​​of each component in the component configuration list, the abnormal probability value of the entire vehicle of the target vehicle is integrated to obtain.

[0010] In a further technical solution, the vehicle abnormality probability analysis method, wherein the component configuration list of the target vehicle is obtained,

[0011] The parts configuration list includes the name, model and specification of each part.

[0012] In a further technical solution, the vehicle abnormality probability analysis method, wherein the component abnormality probability value of each component in the component configuration list is generated based on the component configuration list by a preset component abnormality probability prediction model, comprises:

[0013] Pre-training the constructed artificial intelligence model based on the historical experience data of each of the components to generate a corresponding component abnormality probability prediction model;

[0014] The acquired component configuration list is input into the component abnormality probability prediction model to generate a component abnormality probability value for each component in the component configuration list.

[0015] In a further technical solution, the vehicle abnormality probability analysis method, wherein the abnormality probability value of each component in the component configuration list is integrated to obtain the abnormality probability value of the entire vehicle of the target vehicle, includes:

[0016] Determining additional influencing factors of the target vehicle;

[0017] Based on the additional influencing factors, generating an additional factor abnormal probability value of the target vehicle through a preset additional factor abnormal probability prediction model;

[0018] The abnormal probability value of the additional factor is integrated with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle.

[0019] In a further technical solution, the vehicle abnormality probability analysis method, wherein the abnormal probability value of the additional factor is integrated with the abnormal probability value of each component in the component configuration list to generate the whole vehicle abnormality probability value of the target vehicle, includes:

[0020] Determining a vehicle human factor of the target vehicle;

[0021] Based on the vehicle-to-personnel factor, a vehicle-to-personnel abnormality probability value of the target vehicle is generated by a preset vehicle-to-personnel abnormality probability prediction model;

[0022] The abnormal probability value of the person in the vehicle and the abnormal probability value of the additional factor are integrated with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle.

[0023] In a further technical solution, the vehicle abnormality probability analysis method, wherein the additional influencing factors of the target vehicle are determined,

[0024] Among them, the additional influencing factors include the vehicle's driving area, brand and maintenance cost.

[0025] In a further technical solution, the vehicle abnormality probability analysis method, wherein the vehicle abnormality probability analysis method determines the target vehicle from the human factor,

[0026] Among them, the human factors of the vehicle include the age, gender, driving experience and violation record of the vehicle driver.

[0027] A vehicle abnormality probability analysis system, comprising:

[0028] An acquisition module is used to obtain a component configuration list of a target vehicle;

[0029] A generating module, configured to generate a component abnormality probability value of each component in the component configuration list based on the component configuration list by using a preset component abnormality probability prediction model;

[0030] The integration module is used to integrate the abnormal probability value of each component in the component configuration list to obtain the abnormal probability value of the entire vehicle of the target vehicle.

[0031] A computer device, wherein the computer device comprises at least one processor; and

[0032] a memory communicatively connected to the at least one processor; wherein,

[0033] The memory stores a computer program that can be executed by the at least one processor. When the computer program is executed by the at least one processor, the vehicle abnormality probability analysis method as described in any one of the above items can be implemented.

[0034] A non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the vehicle abnormality probability analysis method as described in any one of the above items can be implemented.

[0035] Compared with the prior art, the present invention provides a vehicle abnormality probability analysis method, system, computer device and non-volatile computer-readable storage medium, wherein the method includes: obtaining a component configuration list of a target vehicle; based on the component configuration list, generating a component abnormality probability value of each component in the component configuration list through a preset component abnormality probability prediction model; based on the component abnormality probability values ​​of each component in the component configuration list, integrating to obtain the whole vehicle abnormality probability value of the target vehicle. In this way, the whole vehicle abnormality probability value of the target vehicle can be analyzed and obtained in a timely and accurate manner through the method of the present invention, which can then help insurance companies to perform insurance pricing for the target vehicle based on the whole vehicle abnormality probability value. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic flow chart of a vehicle abnormality probability analysis method provided by an embodiment of the present invention.

[0038] Figure 2 A schematic diagram of functional modules of a vehicle abnormality probability analysis system provided in an embodiment of the present invention.

[0039] Figure 3 A schematic diagram of the hardware structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] In the description of the present invention, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps therein is not limited and can be appropriately adjusted as needed.

[0042] Various non-limiting embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0043] At present, with the rapid development of new energy vehicles in recent years, the average annual growth rate of new energy vehicles has increased year by year. New energy vehicle technology is also constantly innovating, and breakthroughs have been made in core technologies such as batteries, motors, and electronic controls. At present, with the continuous influx of new energy vehicle emerging technologies and brands into the market, insurance companies are also facing new challenges in cost pricing based on vehicle risks. On the one hand, due to the barriers of emerging technologies, it is difficult for insurance company professionals to respond to emerging technologies in a timely manner in the short term. On the other hand, the existing vehicle risk prediction model is a risk prediction based on historical vehicle data. Due to the lack of historical data for newly launched models, the difficulty for insurance companies to timely and accurately estimate vehicle risks for newly launched models has also increased significantly.

[0044] Based on this situation, how to provide a vehicle abnormality probability analysis method, system, computer equipment and non-volatile computer-readable storage medium that can timely and accurately analyze the vehicle abnormality probability value of the target vehicle is a problem that urgently needs to be solved by technical personnel in this field.

[0045] To solve the above problems, see Figure 1 The embodiment of the present invention provides a vehicle abnormality probability analysis method, wherein the method comprises the steps of:

[0046] S100, obtaining a component configuration list of a target vehicle;

[0047] S200, based on the component configuration list, generating a component abnormality probability value of each component in the component configuration list by using a preset component abnormality probability prediction model;

[0048] S300: Based on the abnormal probability values ​​of the components in the component configuration list, an abnormal probability value of the entire vehicle of the target vehicle is obtained by integration.

[0049] Specifically, the vehicle abnormality probability analysis described in the present invention can be understood as an analysis of the risks (such as damage risks, etc.) of the target vehicle, that is, the method of the present invention can timely and accurately analyze the vehicle abnormality probability value (vehicle risk value) of the target vehicle, thereby helping insurance companies to price the insurance of the target vehicle based on the vehicle abnormality probability value.

[0050] Furthermore, in the vehicle abnormality probability analysis method, the step S100, obtaining a component configuration list of the target vehicle,

[0051] The parts configuration list includes the name, model and specification of each part.

[0052] In the specific implementation, in this embodiment, a component configuration list of the target vehicle is obtained, wherein the component configuration list includes the name, model and specification of each component;

[0053] The specific implementation process is as follows:

[0054] 1) Determine parts information requirements

[0055] Purpose: To clarify the types of parts information that need to be collected, including the name, model, specifications, etc. of the parts;

[0056] Action: Develop a template or checklist for collecting parts information to ensure completeness and consistency of information;

[0057] 2) Access vehicle database

[0058] Purpose: Retrieve detailed information of the target vehicle from data provided by the vehicle manufacturer or vehicle registration information;

[0059] Action: The original configuration data of the vehicle can be retrieved from the vehicle manufacturer database or vehicle management system using the vehicle identification number (VIN) as a query keyword;

[0060] 3) Organize and verify parts information

[0061] Purpose: To ensure that the collected parts information is accurate and consistent with the actual configuration of the vehicle;

[0062] Action: Verify the collected parts information against the information provided by the vehicle manufacturer and correct any inconsistencies;

[0063] 4) Generate parts configuration list report

[0064] Purpose: To organize the collected and verified parts information into a list report to provide basic data for subsequent abnormal probability analysis;

[0065] Action: Use professional software or manually compile a parts configuration list report, which should include detailed information such as the name, model, specification, and installation location of the parts;

[0066] Through the above process, it is possible to ensure that an accurate and complete list of component configurations of the target vehicle is obtained, providing reliable basic data for subsequent vehicle abnormality probability analysis.

[0067] Furthermore, the vehicle abnormality probability analysis method, wherein the step S200, based on the component configuration list, generates the component abnormality probability value of each component in the component configuration list by using a preset component abnormality probability prediction model, comprises:

[0068] Pre-training the constructed artificial intelligence model based on the historical experience data of each of the components to generate a corresponding component abnormality probability prediction model;

[0069] The acquired component configuration list is input into the component abnormality probability prediction model to generate a component abnormality probability value for each component in the component configuration list.

[0070] In the specific implementation, in this embodiment, the constructed artificial intelligence model is trained in advance based on the historical experience data of each of the components to generate a corresponding component abnormality probability prediction model; the obtained component configuration list is input into the component abnormality probability prediction model to generate a component abnormality probability value for each component in the component configuration list;

[0071] The specific implementation process is as follows:

[0072] It is understandable that for newly launched models, although there is a lack of historical experience data for the entire vehicle, its components may already have certain historical experience data; therefore, this embodiment can construct a component abnormality probability prediction model based on the historical experience data of each component (such as battery, motor, electronic control, etc.);

[0073] 1) Collect and preprocess historical experience data

[0074] Purpose: To train the AI ​​model, it is necessary to collect historical experience data of each component, including the component's failure record, usage, and maintenance history;

[0075] Action: Collect historical experience data from multiple sources such as maintenance databases, vehicle recall records, and customer feedback; clean the collected data, including pre-processing operations such as removing outliers, filling missing values, and unifying the format;

[0076] 2) Feature selection and model training

[0077] Purpose: To select features that have a significant impact on the probability of component abnormality from historical experience data and use these features to train an artificial intelligence model;

[0078] Action: Apply statistical analysis and feature engineering methods to identify key features; select an appropriate machine learning algorithm (such as random forest, gradient boosting machine, neural network, etc.) and use a training dataset to train the model;

[0079] 3) Model validation and optimization

[0080] Purpose: Evaluate the predictive accuracy of the model and optimize model performance by adjusting model parameters or selecting different algorithms;

[0081] Action: Use cross-validation, A / B testing and other methods to validate the model; adjust the model structure or parameters based on the validation results to improve the model's generalization ability and prediction accuracy;

[0082] 4) Deployment of component abnormality probability prediction model

[0083] Purpose: Deploy the trained and optimized component abnormality probability prediction model to the production environment so that it can receive new input data and output prediction results;

[0084] Action: Integrate the component abnormality probability prediction model into the system to ensure that the system can process the input component configuration list and call the component abnormality probability prediction model for prediction analysis;

[0085] 5) Input the parts configuration list and generate the parts abnormality probability value

[0086] Purpose: Use the deployed component anomaly probability prediction model to analyze the component configuration list of the target vehicle and generate the component anomaly probability value of each component;

[0087] Action: Input the acquired component configuration list data into the component abnormality probability prediction model; the component abnormality probability prediction model analyzes the characteristic data of each component, calculates and outputs the component abnormality probability value of each component;

[0088] Through the above process, an artificial intelligence model that can predict the probability of component abnormalities can be constructed, and the model can be applied to the actual vehicle component configuration list to provide data support for risk management and decision-making.

[0089] Furthermore, the vehicle abnormality probability analysis method, wherein the step S300, based on the abnormality probability values ​​of each component in the component configuration list, integrates and obtains the vehicle abnormality probability value of the target vehicle, comprises:

[0090] Determining additional influencing factors of the target vehicle;

[0091] Based on the additional influencing factors, generating an additional factor abnormal probability value of the target vehicle through a preset additional factor abnormal probability prediction model;

[0092] The abnormal probability value of the additional factor is integrated with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle.

[0093] Furthermore, in the vehicle abnormality probability analysis method, the additional influencing factors of the target vehicle are determined.

[0094] Among them, the additional influencing factors include the vehicle's driving area, brand and maintenance cost.

[0095] In the specific implementation, in this embodiment, the additional influencing factors of the target vehicle are determined; wherein the additional influencing factors include the driving area, brand and maintenance cost of the vehicle; based on the additional influencing factors, the additional factor abnormal probability value of the target vehicle is generated by a preset additional factor abnormal probability prediction model; the additional factor abnormal probability value is integrated with the abnormal probability value of each component in the component configuration list to generate the whole vehicle abnormal probability value of the target vehicle;

[0096] The specific implementation process is as follows:

[0097] 1) Identify additional influencing factors

[0098] Purpose: To identify additional factors that affect the abnormal probability of the target vehicle, which in this embodiment include the driving area, brand and maintenance cost (parts cost, labor cost) of the vehicle;

[0099] Action: Based on vehicle registration information and historical data, collect the target vehicle’s driving area characteristics (such as climate, terrain), brand characteristics (such as historical failure rates, user reviews) and maintenance cost data (such as parts cost, labor cost);

[0100] 2) Collect additional influencing factor data

[0101] Purpose: To obtain the target vehicle's driving area environment data, brand historical performance data, and maintenance cost data;

[0102] Action: Collect data from sources such as vehicle management systems, brand history records, and maintenance databases to ensure data accuracy and completeness;

[0103] 3) Construct an additional factor abnormal probability prediction model

[0104] Objective: Build and train a model to predict vehicle abnormality probability based on additional influencing factors;

[0105] Action: Use historical data and collected additional influencing factor data to select an appropriate machine learning algorithm to build a model, and perform training and parameter tuning using a training data set;

[0106] 4) Predict the abnormal probability value of additional factors

[0107] Purpose: Use the trained model to predict the abnormal probability value of the target vehicle based on additional influencing factors;

[0108] Action: Input the target vehicle’s driving region, brand, and repair cost data into the trained and optimized additional factor abnormal probability prediction model to generate the additional factor abnormal probability prediction value of the target vehicle;

[0109] 5) Integrate the abnormal probability values ​​of components and additional factors

[0110] Purpose: To integrate the abnormal probability values ​​of each component with the abnormal probability values ​​of additional factors to generate the abnormal probability value of the entire target vehicle;

[0111] Action: Use mathematical methods (such as weighted average, Bayesian update, etc.) to combine the abnormal probability values ​​of each component with the abnormal probability values ​​of additional factors to generate the abnormal probability value of the target vehicle; determine appropriate weights or probability update rules to ensure the accuracy of the integrated results;

[0112] Through the above process, the abnormal risk of the target vehicle can be comprehensively assessed, and a scientific basis can be provided for applications such as vehicle maintenance and insurance pricing.

[0113] Furthermore, the vehicle abnormality probability analysis method, wherein the abnormality probability value of the additional factor is integrated with the abnormality probability value of each component in the component configuration list to generate the abnormality probability value of the entire vehicle of the target vehicle, comprises:

[0114] Determining a vehicle human factor of the target vehicle;

[0115] Based on the vehicle-to-personnel factor, a vehicle-to-personnel abnormality probability value of the target vehicle is generated by a preset vehicle-to-personnel abnormality probability prediction model;

[0116] The abnormal probability value of the person in the vehicle and the abnormal probability value of the additional factor are integrated with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle.

[0117] Furthermore, in the vehicle abnormality probability analysis method, the vehicle abnormality probability of the target vehicle is determined from human factors.

[0118] Among them, the human factors of the vehicle include the age, gender, driving experience and violation record of the vehicle driver.

[0119] In specific implementation, in this embodiment, the vehicle human factors of the target vehicle are determined; wherein the vehicle human factors include the age, gender, driving experience and violation record of the vehicle driver; based on the vehicle human factors, the vehicle human abnormality probability value of the target vehicle is generated through a preset vehicle human abnormality probability prediction model; the vehicle human abnormality probability value and the additional factor abnormality probability value are integrated with the abnormal probability values ​​of each component in the component configuration list to generate the whole vehicle abnormality probability value of the target vehicle;

[0120] The specific implementation process is as follows:

[0121] It can be understood that, in addition to the influence of the vehicle itself, factors such as the driver's behavior and driving habits (i.e., vehicle-human factors) will also affect the probability of vehicle abnormality and the degree of loss. Therefore, in this embodiment, the influence of vehicle-human factors is combined;

[0122] 1) Determine the human factor of the vehicle

[0123] Purpose: To identify and determine the driver's personal factors that affect the abnormal probability of the target vehicle, including age, gender, driving experience and violation record;

[0124] Action: Develop a data collection plan that identifies the sources and methods of obtaining the required driver information;

[0125] 2) Collect data from human factors of vehicles

[0126] Purpose: To obtain data such as age, gender, driving experience and violation record of the target vehicle driver;

[0127] Action: Collect relevant information through driver management systems, traffic violation databases or directly from drivers, and ensure data accuracy and privacy protection;

[0128] 3) Construct a prediction model for the probability of vehicle-to-person abnormality

[0129] Objective: To build and train a model to predict the probability of vehicle abnormality based on the driver's personal factors;

[0130] Action: Use the driver's historical data and vehicle abnormality records to select an appropriate machine learning algorithm to build a model, and train and optimize it using the training data set;

[0131] 4) Predict the probability of abnormality of vehicles and people

[0132] Purpose: To use the trained vehicle-to-personnel abnormal probability prediction model to predict the vehicle-to-personnel abnormal probability value of the target vehicle based on the driver's personal factors;

[0133] Action: Input the target vehicle driver’s age, gender, driving experience, and violation record data into the vehicle-to-personnel abnormal probability prediction model to generate the target vehicle’s vehicle-to-personnel abnormal probability value;

[0134] 5) Integrate the abnormal probability values ​​of each component, the abnormal probability values ​​of additional factors, and the abnormal probability values ​​of the vehicle and the person

[0135] Purpose: To effectively integrate the abnormal probability values ​​of each component, the abnormal probability value of additional factors and the abnormal probability value of the vehicle, and generate the abnormal probability value of the target vehicle;

[0136] Action: Use appropriate statistical methods or algorithms, such as the Bayesian method, risk matrix, or machine learning integration method, to comprehensively consider the probability values ​​of the three and calculate the abnormal probability value of the target vehicle;

[0137] Through the above process, the component conditions of the target vehicle, additional influencing factors and human factors of the vehicle can be comprehensively considered to generate a more accurate abnormal probability value of the whole vehicle, providing a scientific basis for vehicle risk assessment and management, as well as insurance pricing and other applications.

[0138] It can be seen from the above method embodiments that the vehicle abnormality probability analysis method provided by the present invention at least includes: obtaining a component configuration list of a target vehicle; based on the component configuration list, generating a component abnormality probability value of each component in the component configuration list through a preset component abnormality probability prediction model; based on the component abnormality probability values ​​of each component in the component configuration list, integrating to obtain a whole vehicle abnormality probability value of the target vehicle; etc. In this way, the whole vehicle abnormality probability value of the target vehicle can be analyzed and obtained in a timely and accurate manner through the method of the present invention, thereby helping insurance companies to perform insurance pricing for the target vehicle based on the whole vehicle abnormality probability value.

[0139] It should be understood that, although the present application provides method operation steps as described in the embodiments or flowcharts, more or less operation steps may be included based on conventional or non-creative labor, and these operation steps are not necessarily performed in sequence according to the embodiment or flowchart. The order of steps listed in the embodiment or flowchart is only one way of executing the order of many steps, and does not represent the only execution order. It should be noted that there is not necessarily a certain order between the above steps. A person of ordinary skill in the art can understand from the description of the embodiment of the present invention that in different embodiments, the above steps may have different execution orders, that is, they may be executed in parallel, or they may be executed in exchange, etc. Moreover, at least a part of the steps in the embodiment or flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but may be executed in turn, alternately or synchronously with other steps or at least a part of the sub-steps or stages of other steps.

[0140] Based on the above method embodiment, please refer to Figure 2 Another embodiment of the present invention further provides a vehicle abnormality probability analysis system, wherein the system comprises:

[0141] The acquisition module 11 is used to acquire the component configuration list of the target vehicle;

[0142] A generating module 12, configured to generate a component abnormality probability value of each component in the component configuration list based on the component configuration list by using a preset component abnormality probability prediction model;

[0143] The integration module 13 is used to integrate the abnormal probability value of each component in the component configuration list to obtain the abnormal probability value of the entire vehicle of the target vehicle.

[0144] Specifically, the vehicle abnormality probability analysis described in the present invention can be understood as an analysis of the risks (such as damage risks, etc.) of the target vehicle, that is, the system of the present invention can timely and accurately analyze and obtain the vehicle abnormality probability value (vehicle risk value) of the target vehicle, thereby helping insurance companies to price the insurance of the target vehicle based on the vehicle abnormality probability value.

[0145] Furthermore, in the vehicle abnormality probability analysis system, the component configuration list of the target vehicle is obtained.

[0146] The parts configuration list includes the name, model and specification of each part.

[0147] In the specific implementation, in this embodiment, a component configuration list of the target vehicle is obtained, wherein the component configuration list includes the name, model and specification of each component;

[0148] The specific implementation process is as follows:

[0149] 1) Determine parts information requirements

[0150] Purpose: To clarify the types of parts information that need to be collected, including the name, model, specifications, etc. of the parts;

[0151] Action: Develop a template or checklist for collecting parts information to ensure completeness and consistency of information;

[0152] 2) Access vehicle database

[0153] Purpose: Retrieve detailed information of the target vehicle from data provided by the vehicle manufacturer or vehicle registration information;

[0154] Action: The original configuration data of the vehicle can be retrieved from the vehicle manufacturer database or vehicle management system using the vehicle identification number (VIN) as a query keyword;

[0155] 3) Organize and verify parts information

[0156] Purpose: To ensure that the collected parts information is accurate and consistent with the actual configuration of the vehicle;

[0157] Action: Verify the collected parts information against the information provided by the vehicle manufacturer and correct any inconsistencies;

[0158] 4) Generate parts configuration list report

[0159] Purpose: To organize the collected and verified parts information into a list report to provide basic data for subsequent abnormal probability analysis;

[0160] Action: Use professional software or manually compile a parts configuration list report, which should include detailed information such as the name, model, specification, and installation location of the parts;

[0161] Through the above process, it is possible to ensure that an accurate and complete list of component configurations of the target vehicle is obtained, providing reliable basic data for subsequent vehicle abnormality probability analysis.

[0162] Furthermore, the vehicle abnormality probability analysis system, wherein the generating of the component abnormality probability value of each component in the component configuration list by a preset component abnormality probability prediction model based on the component configuration list, comprises:

[0163] Pre-training the constructed artificial intelligence model based on the historical experience data of each of the components to generate a corresponding component abnormality probability prediction model;

[0164] The acquired component configuration list is input into the component abnormality probability prediction model to generate a component abnormality probability value for each component in the component configuration list.

[0165] In the specific implementation, in this embodiment, the constructed artificial intelligence model is trained in advance based on the historical experience data of each of the components to generate a corresponding component abnormality probability prediction model; the obtained component configuration list is input into the component abnormality probability prediction model to generate a component abnormality probability value for each component in the component configuration list;

[0166] The specific implementation process is as follows:

[0167] It is understandable that for newly launched models, although there is a lack of historical experience data for the entire vehicle, its components may already have certain historical experience data; therefore, this embodiment can construct a component abnormality probability prediction model based on the historical experience data of each component (such as battery, motor, electronic control, etc.);

[0168] 1) Collect and preprocess historical experience data

[0169] Purpose: To train the AI ​​model, it is necessary to collect historical experience data of each component, including the component's failure record, usage, and maintenance history;

[0170] Action: Collect historical experience data from multiple sources such as maintenance databases, vehicle recall records, and customer feedback; clean the collected data, including pre-processing operations such as removing outliers, filling missing values, and unifying the format;

[0171] 2) Feature selection and model training

[0172] Purpose: To select features that have a significant impact on the probability of component abnormality from historical experience data and use these features to train an artificial intelligence model;

[0173] Action: Apply statistical analysis and feature engineering methods to identify key features; select an appropriate machine learning algorithm (such as random forest, gradient boosting machine, neural network, etc.) and use a training dataset to train the model;

[0174] 3) Model validation and optimization

[0175] Purpose: Evaluate the predictive accuracy of the model and optimize model performance by adjusting model parameters or selecting different algorithms;

[0176] Action: Use cross-validation, A / B testing and other methods to validate the model; adjust the model structure or parameters based on the validation results to improve the model's generalization ability and prediction accuracy;

[0177] 4) Deployment of component abnormality probability prediction model

[0178] Purpose: Deploy the trained and optimized component abnormality probability prediction model to the production environment so that it can receive new input data and output prediction results;

[0179] Action: Integrate the component abnormality probability prediction model into the system to ensure that the system can process the input component configuration list and call the component abnormality probability prediction model for prediction analysis;

[0180] 5) Input the parts configuration list and generate the parts abnormality probability value

[0181] Purpose: Use the deployed component anomaly probability prediction model to analyze the component configuration list of the target vehicle and generate the component anomaly probability value of each component;

[0182] Action: Input the acquired component configuration list data into the component abnormality probability prediction model; the component abnormality probability prediction model analyzes the characteristic data of each component, calculates and outputs the component abnormality probability value of each component;

[0183] Through the above process, an artificial intelligence model that can predict the probability of component abnormalities can be constructed, and the model can be applied to the actual vehicle component configuration list to provide data support for risk management and decision-making.

[0184] Furthermore, the vehicle abnormality probability analysis system, wherein the abnormality probability value of each component in the component configuration list is integrated to obtain the abnormality probability value of the entire vehicle of the target vehicle, comprises:

[0185] Determining additional influencing factors of the target vehicle;

[0186] Based on the additional influencing factors, generating an additional factor abnormal probability value of the target vehicle through a preset additional factor abnormal probability prediction model;

[0187] The abnormal probability value of the additional factor is integrated with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle.

[0188] Furthermore, in the vehicle abnormality probability analysis system, the additional influencing factors of determining the target vehicle are:

[0189] Among them, the additional influencing factors include the vehicle's driving area, brand and maintenance cost.

[0190] In the specific implementation, in this embodiment, the additional influencing factors of the target vehicle are determined; wherein the additional influencing factors include the driving area, brand and maintenance cost of the vehicle; based on the additional influencing factors, the additional factor abnormal probability value of the target vehicle is generated by a preset additional factor abnormal probability prediction model; the additional factor abnormal probability value is integrated with the abnormal probability value of each component in the component configuration list to generate the whole vehicle abnormal probability value of the target vehicle;

[0191] The specific implementation process is as follows:

[0192] 1) Identify additional influencing factors

[0193] Purpose: To identify additional factors that affect the abnormal probability of the target vehicle, which in this embodiment include the driving area, brand and maintenance cost (parts cost, labor cost) of the vehicle;

[0194] Action: Based on vehicle registration information and historical data, collect the target vehicle’s driving area characteristics (such as climate, terrain), brand characteristics (such as historical failure rates, user reviews) and maintenance cost data (such as parts cost, labor cost);

[0195] 2) Collect additional influencing factor data

[0196] Purpose: To obtain the target vehicle's driving area environment data, brand historical performance data, and maintenance cost data;

[0197] Action: Collect data from sources such as vehicle management systems, brand history records, and maintenance databases to ensure data accuracy and completeness;

[0198] 3) Construct an additional factor abnormal probability prediction model

[0199] Objective: Build and train a model to predict vehicle abnormality probability based on additional influencing factors;

[0200] Action: Use historical data and collected additional influencing factor data to select an appropriate machine learning algorithm to build a model, and perform training and parameter tuning using a training data set;

[0201] 4) Predict the abnormal probability value of additional factors

[0202] Purpose: Use the trained model to predict the abnormal probability value of the target vehicle based on additional influencing factors;

[0203] Action: Input the target vehicle’s driving region, brand, and repair cost data into the trained and optimized additional factor abnormal probability prediction model to generate the additional factor abnormal probability prediction value of the target vehicle;

[0204] 5) Integrate the abnormal probability values ​​of components and additional factors

[0205] Purpose: To integrate the abnormal probability values ​​of each component with the abnormal probability values ​​of additional factors to generate the abnormal probability value of the entire target vehicle;

[0206] Action: Use mathematical methods (such as weighted average, Bayesian update, etc.) to combine the abnormal probability values ​​of each component with the abnormal probability values ​​of additional factors to generate the abnormal probability value of the target vehicle; determine appropriate weights or probability update rules to ensure the accuracy of the integrated results;

[0207] Through the above process, the abnormal risk of the target vehicle can be comprehensively assessed, and a scientific basis can be provided for applications such as vehicle maintenance and insurance pricing.

[0208] Furthermore, the vehicle abnormality probability analysis system, wherein the abnormality probability value of the additional factor is integrated with the abnormality probability value of each component in the component configuration list to generate the abnormality probability value of the entire vehicle of the target vehicle, comprises:

[0209] Determining a vehicle human factor of the target vehicle;

[0210] Based on the vehicle-to-personnel factor, a vehicle-to-personnel abnormality probability value of the target vehicle is generated by a preset vehicle-to-personnel abnormality probability prediction model;

[0211] The abnormal probability value of the person in the vehicle and the abnormal probability value of the additional factor are integrated with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle.

[0212] Furthermore, in the vehicle abnormality probability analysis system, the vehicle abnormality probability analysis system determines the target vehicle from the human factor,

[0213] Among them, the human factors of the vehicle include the age, gender, driving experience and violation record of the vehicle driver.

[0214] In specific implementation, in this embodiment, the vehicle human factors of the target vehicle are determined; wherein the vehicle human factors include the age, gender, driving experience and violation record of the vehicle driver; based on the vehicle human factors, the vehicle human abnormality probability value of the target vehicle is generated through a preset vehicle human abnormality probability prediction model; the vehicle human abnormality probability value and the additional factor abnormality probability value are integrated with the abnormal probability values ​​of each component in the component configuration list to generate the whole vehicle abnormality probability value of the target vehicle;

[0215] The specific implementation process is as follows:

[0216] It can be understood that, in addition to the influence of the vehicle itself, factors such as the driver's behavior and driving habits (i.e., vehicle-human factors) will also affect the probability of vehicle abnormality and the degree of loss. Therefore, in this embodiment, the influence of vehicle-human factors is combined;

[0217] 1) Determine the human factor of the vehicle

[0218] Purpose: To identify and determine the driver's personal factors that affect the abnormal probability of the target vehicle, including age, gender, driving experience and violation record;

[0219] Action: Develop a data collection plan that identifies the sources and methods of obtaining the required driver information;

[0220] 2) Collect data on human factors in vehicles

[0221] Purpose: To obtain data such as age, gender, driving experience and violation record of the target vehicle driver;

[0222] Action: Collect relevant information through driver management systems, traffic violation databases or directly from drivers, and ensure data accuracy and privacy protection;

[0223] 3) Construct a prediction model for the probability of vehicle-to-person abnormality

[0224] Objective: To build and train a model to predict the probability of vehicle abnormality based on the driver's personal factors;

[0225] Action: Use the driver's historical data and vehicle abnormality records to select an appropriate machine learning algorithm to build a model, and train and optimize it using the training data set;

[0226] 4) Predict the probability of abnormality of vehicles and people

[0227] Purpose: To use the trained vehicle-to-personnel abnormal probability prediction model to predict the vehicle-to-personnel abnormal probability value of the target vehicle based on the driver's personal factors;

[0228] Action: Input the target vehicle driver’s age, gender, driving experience, and violation record data into the vehicle-to-personnel abnormal probability prediction model to generate the target vehicle’s vehicle-to-personnel abnormal probability value;

[0229] 5) Integrate the abnormal probability values ​​of each component, the abnormal probability values ​​of additional factors, and the abnormal probability values ​​of the vehicle and the person

[0230] Purpose: To effectively integrate the abnormal probability values ​​of each component, the abnormal probability value of additional factors and the abnormal probability value of the vehicle, and generate the abnormal probability value of the target vehicle;

[0231] Action: Use appropriate statistical methods or algorithms, such as the Bayesian method, risk matrix, or machine learning integration method, to comprehensively consider the probability values ​​of the three and calculate the abnormal probability value of the target vehicle;

[0232] Through the above process, the component conditions of the target vehicle, additional influencing factors and human factors of the vehicle can be comprehensively considered to generate a more accurate abnormal probability value of the whole vehicle, providing a scientific basis for vehicle risk assessment and management, as well as insurance pricing and other applications.

[0233] As can be seen from the above system embodiments, the vehicle abnormality probability analysis system provided by the present invention at least includes: obtaining the component configuration list of the target vehicle; based on the component configuration list, generating the component abnormality probability value of each component in the component configuration list through a preset component abnormality probability prediction model; based on the abnormal probability values ​​of each component in the component configuration list, integrating to obtain the whole vehicle abnormality probability value of the target vehicle; etc. In this way, the whole vehicle abnormality probability value of the target vehicle can be analyzed and obtained in a timely and accurate manner through the system of the present invention, which can then help insurance companies to perform insurance pricing for the target vehicle based on the whole vehicle abnormality probability value.

[0234] Based on the above method embodiment, please refer to Figure 3 Another embodiment of the present invention further provides a computer device, wherein the computer device 10 includes:

[0235] memory 120 and one or more processors 110, Figure 3 A processor 110 is used as an example for description. The processor 110 and the memory 120 may be connected via a communication bus or other means. Figure 3 The example of connecting via a communication bus is taken as an example.

[0236] The processor 110 is used to complete various control logics of the computer device 10. It can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or any combination of these components. In addition, the processor 110 can also be any traditional processor, microprocessor or state machine. The processor 110 can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP, or any other such configuration.

[0237] The memory 120 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the computer program corresponding to the vehicle abnormality probability analysis method in the embodiment of the present invention. The processor 110 executes various functional applications and data processing of the computer device 10 by running the non-volatile software programs, instructions and units stored in the memory 120, that is, the vehicle abnormality probability analysis method in the above method embodiment is implemented.

[0238] The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store an application required for operating the device or at least one function; the data storage area may store data created according to the use of the computer device 10, etc. In addition, the memory 120 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 120 may optionally include a memory remotely arranged relative to the processor 110, and these remote memories may be connected to the computer device 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0239] One or more units are stored in the memory 120, and when executed by one or more processors 110, the vehicle abnormality probability analysis method in any of the above method embodiments can be implemented, for example, the above described Figure 1 The method comprises steps S100 to S300.

[0240] Those skilled in the art will understand that Figure 3 The hardware structure diagram shown in the figure is only a schematic diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0241] Based on the above method embodiments, another embodiment of the present invention further provides a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the vehicle abnormality probability analysis method in any of the above method embodiments can be implemented, for example, the above described Figure 1 The method comprises steps S100 to S300.

[0242] As an example, the non-volatile storage medium can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable ROM (EEPROM), or a flash memory. Volatile memory can include a random access memory (RAM) as an external cache memory. By way of illustration and not limitation, RAM can be obtained in many forms such as synchronous RAM (SRAM), dynamic RAM, (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synch link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The disclosed memory components or memories of the operating environment described herein are intended to include one or more of these and / or any other suitable types of memory.

[0243] Based on the above method embodiments, another embodiment of the present invention further provides a computer program product, the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, when the program instructions are executed by a processor, the vehicle abnormality probability analysis method in any of the above method embodiments can be implemented, for example, the above described Figure 1 The method comprises steps S100 to S300.

[0244] The embodiments described above are merely illustrative, in which the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0245] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can exist in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0246] Conditional language such as "can," "can," "might," or "may," unless specifically stated otherwise or otherwise understood within the context as used, is generally intended to convey, among other things, that a particular embodiment can include (while other embodiments do not) a particular feature, element, and / or operation. Thus, such conditional language is also generally intended to imply that a feature, element, and / or operation is required for one or more embodiments anyway or that one or more embodiments must include logic for determining, with or without input or prompting, whether such features, elements, and / or operations are included or will be performed in any particular embodiment.

[0247] What has been described herein, in this specification and in the accompanying drawings includes examples that can provide a vehicle abnormal probability analysis method, system, computer equipment, and non-volatile computer-readable storage medium. Of course, it is not possible to describe every conceivable combination of elements and / or methods for the purpose of describing the various features of the present disclosure, but it can be recognized that many other combinations and permutations of the disclosed features are possible. Therefore, it is obvious that various modifications can be made to the present disclosure without departing from the scope or spirit of the present disclosure, but all these various modifications should belong to the scope of protection of the claims attached to the present invention. In addition, or in an alternative, other embodiments of the present disclosure may be obvious from consideration of the present specification and the accompanying drawings and from the practice of the present disclosure as presented herein. It is intended that the examples proposed in the present specification and the accompanying drawings are considered to be illustrative and non-restrictive in all respects. Although specific terms are used in this article, they are used in a general and descriptive sense and are not used for the purpose of limitation.

Claims

1. A vehicle abnormality probability analysis method, characterized in that: include: Obtain the component configuration list of the target vehicle; Based on the component configuration list, a component abnormality probability value of each component in the component configuration list is generated by a preset component abnormality probability prediction model; Based on the abnormal probability values ​​of each component in the component configuration list, the abnormal probability value of the entire vehicle of the target vehicle is integrated to obtain.

2. The vehicle abnormality probability analysis method according to claim 1, characterized in that: The component configuration list of the target vehicle is obtained. The parts configuration list includes the name, model and specification of each part.

3. The vehicle abnormality probability analysis method according to claim 1 or 2, characterized in that: The generating of the component abnormality probability value of each component in the component configuration list by using a preset component abnormality probability prediction model based on the component configuration list includes: Pre-training the constructed artificial intelligence model based on the historical experience data of each of the components to generate a corresponding component abnormality probability prediction model; The acquired component configuration list is input into the component abnormality probability prediction model to generate a component abnormality probability value for each component in the component configuration list.

4. The vehicle abnormality probability analysis method according to claim 3 is characterized in that: The step of integrating the abnormal probability values ​​of the components in the component configuration list to obtain the abnormal probability value of the entire target vehicle includes: Determining additional influencing factors of the target vehicle; Based on the additional influencing factors, generating an additional factor abnormal probability value of the target vehicle through a preset additional factor abnormal probability prediction model; The abnormal probability value of the additional factor is integrated with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle.

5. The vehicle abnormality probability analysis method according to claim 4, characterized in that: The step of integrating the abnormal probability value of the additional factor with the abnormal probability value of each component in the component configuration list to generate the abnormal probability value of the entire vehicle of the target vehicle includes: Determining a vehicle human factor of the target vehicle; Based on the vehicle-to-personnel factor, a vehicle-to-personnel abnormality probability value of the target vehicle is generated by a preset vehicle-to-personnel abnormality probability prediction model; The abnormal probability value of the vehicle's human and the abnormal probability value of the additional factors are integrated with the abnormal probability values ​​of each component in the component configuration list to generate a whole vehicle abnormal probability value of the target vehicle.

6. The vehicle abnormality probability analysis method according to claim 5, characterized in that: The additional influencing factors for determining the target vehicle, Among them, the additional influencing factors include the vehicle's driving area, brand and maintenance cost.

7. The vehicle abnormality probability analysis method according to claim 6, characterized in that: The determination of the target vehicle is based on human factors, Among them, the human factors of the vehicle include the age, gender, driving experience and violation record of the vehicle driver.

8. A vehicle abnormality probability analysis system, characterized in that: include: An acquisition module is used to obtain a component configuration list of a target vehicle; A generating module, configured to generate a component abnormality probability value of each component in the component configuration list based on the component configuration list by using a preset component abnormality probability prediction model; The integration module is used to integrate the abnormal probability value of each component in the component configuration list to obtain the abnormal probability value of the entire vehicle of the target vehicle.

9. A computer device, characterized in that: The computer device comprises at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. When the computer program is executed by the at least one processor, the vehicle abnormality probability analysis method as described in any one of claims 1 to 7 can be implemented.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the vehicle abnormality probability analysis method as described in any one of claims 1 to 7 can be implemented.

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