Vehicle pricing method and device, terminal equipment and storage medium

Abstract

The embodiment of the invention provides a vehicle pricing method and device, terminal equipment and a storage medium, and belongs to the technical field of data processing. The method comprises the following steps: determining a target vehicle and an initial price corresponding to the target vehicle, and obtaining accident case information corresponding to the target vehicle; performing risk assessment on the target vehicle according to the accident case information to obtain an initial risk portrait factor; obtaining a target accessory corresponding to the target vehicle, and obtaining price change information and maintenance cost information corresponding to the target accessory; performing information supplement on the initial risk portrait factor according to the maintenance cost information to obtain a target risk portrait factor corresponding to the target vehicle; and updating the initial price according to the price change information and the target risk portrait factor to obtain a target price corresponding to the target vehicle. The problem that a traditional pure data pricing model is difficult to adapt to the existing market, so that the market share is lost due to too high pricing, or loss is brought to an insurance company due to too low pricing is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, terminal equipment and storage medium for determining vehicle prices. Background Art

[0002] As a rapidly developing sunrise industry, various manufacturers have emerged, and the market ownership continues to grow. However, the accompanying high costs are difficult to control. How to reasonably price cars to protect the benefits of insurance companies and expand market share has become a key issue that insurance companies urgently need to solve. Car pricing faces multiple challenges: first, the data accumulation of new models is insufficient, the technology iteration is rapid, there are many models and the technology differences between manufacturers are obvious; second, the risks of old models change rapidly, the historical maintenance cost information is outdated, the price of accessories fluctuates frequently, and there is a lack of effective information verification mechanism. There are also large fluctuations in historical claims data. These personalized factors make it difficult for traditional pure data pricing models to adapt, which may lead to overpricing and loss of market share, or underpricing and losses to insurance companies. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide a method, apparatus, terminal device and storage medium for vehicle price pricing, aiming to solve the problem that the traditional pure data pricing model used in related technologies is difficult to adapt to the existing market, which may result in over-pricing and loss of market share, or over-pricing and cause losses to insurance companies.

[0004] In a first aspect, an embodiment of the present invention provides a method for determining a vehicle price, comprising:

[0005] Determine a target vehicle and an initial price corresponding to the target vehicle, and obtain accident case information corresponding to the target vehicle;

[0006] Performing risk assessment on the target vehicle according to the accident case information to obtain an initial risk profile factor corresponding to the target vehicle;

[0007] Obtaining a target accessory corresponding to the target vehicle, and obtaining price change information and maintenance cost information corresponding to the target accessory;

[0008] Supplementing the initial risk profile factor according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle;

[0009] The initial price is updated according to the price change information and the target risk profile factor to obtain a target price corresponding to the target vehicle.

[0010] In a second aspect, an embodiment of the present invention provides a vehicle price determination device, comprising:

[0011] A data acquisition module, used to determine a target vehicle and an initial price corresponding to the target vehicle, and obtain accident case information corresponding to the target vehicle;

[0012] A risk assessment module, used to perform risk assessment on the target vehicle according to the accident case information, and obtain an initial risk profile factor corresponding to the target vehicle;

[0013] An information acquisition module, used to obtain target parts corresponding to the target vehicle, and obtain price change information and maintenance cost information corresponding to the target parts;

[0014] An information supplementation module, used to supplement the initial risk profile factor according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle;

[0015] The price determination module is used to update the initial price according to the price change information and the target risk profile factor to obtain the target price corresponding to the target vehicle.

[0016] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any vehicle price pricing method provided in the specification of the present invention are implemented.

[0017] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any vehicle price pricing method provided in the specification of the present invention.

[0018] The embodiment of the present invention provides a method, device, terminal device and storage medium for vehicle pricing. The method performs risk assessment on a target vehicle by introducing accident case information and obtains an initial risk profile factor, which can provide a comprehensive risk overview for the target vehicle. This step uses historical data and case information to establish a preliminary risk profile of the target vehicle to help identify potential risk factors. Subsequently, by analyzing the maintenance cost information, the initial risk profile factor is supplemented and adjusted in depth to generate a target risk profile factor for the target vehicle. This supplement not only includes changes in maintenance costs, but also takes into account possible additional risks, so that the risk profile factor is more accurate and comprehensive. Next, the initial price is updated according to the price change information and the target risk profile factor to obtain the target price of the target vehicle. This update process will reflect the actual market situation and risk assessment results, making the price adjustment more reasonable and targeted. This method enables vehicle pricing to better adapt to market dynamics and risk changes, thereby improving pricing accuracy and market adaptability. Finally, the pricing method enables claims professionals to effectively empower the vehicle pricing process. The target vehicle is adjusted based on price change information and target risk profile factors, so that the risk benchmark can be quickly located and reflect the actual market demand. This not only improves the accuracy of pricing, but also avoids the problems that may be encountered in traditional pure data pricing models, such as the risk of losing market share due to overpricing, or the risk of insurance companies losing money due to underpricing. By comprehensively considering risk factors and market changes, this method achieves dynamic pricing that is more in line with market demand, thereby optimizing pricing strategies and improving the competitiveness and market performance of insurance companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0020] Figure 1 A schematic diagram of a flow chart of a vehicle price pricing method provided by an embodiment of the present invention;

[0021] Figure 2 for Figure 1 A schematic flow chart of sub-step S104 of the method for pricing a vehicle price;

[0022] Figure 3 A schematic diagram of a model structure of a risk assessment model provided by an embodiment of the present invention;

[0023] Figure 4A schematic diagram of the module structure of a vehicle price determination device provided by an embodiment of the present invention;

[0024] Figure 5 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0027] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0028] The embodiment of the present invention provides a method, apparatus, terminal device and storage medium for determining vehicle prices. The method for determining vehicle prices can be applied to a terminal device, which can be an electronic device such as a tablet computer, a laptop computer, a desktop computer, a personal digital assistant and a wearable device. The terminal device can be a server or a server cluster.

[0029] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0030] Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for determining vehicle prices provided in an embodiment of the present invention.

[0031] like Figure 1 As shown, the vehicle price pricing method includes steps S101 to S105.

[0032] Step S101, determine a target vehicle and an initial price corresponding to the target vehicle, and obtain accident case information corresponding to the target vehicle.

[0033] Exemplarily, the target vehicle is a new car that is ready to be launched on the market, such as a new energy vehicle or a new oil vehicle. Then, the selling price of similar models on the market is queried based on the positioning of the target vehicle, and the initial price of the target vehicle is determined based on the R&D cost of the target vehicle.

[0034] For example, a small amount of case information and the professional knowledge of surveyors and adjusters can be used to guide the pricing of new car models. After the new model is launched on the market, a claims risk survey can be quickly carried out. Through questionnaires, surveyors and adjusters who handle new model cases can be found, and their evaluations of the risks of the model can be collected, thereby determining the corresponding claim case information for the target vehicle.

[0035] For example, by contacting the insurance company to obtain the insurance record and accident history of the target vehicle. This information usually includes past accident records, claims, loss types, etc., and then checking the insurance file and claim records of the target vehicle. Usually, this data can be obtained through the online platform or customer service provided by the insurance company to obtain the accident case information of the target vehicle.

[0036] Step S102: Perform risk assessment on the target vehicle according to the accident case information to obtain an initial risk profile factor corresponding to the target vehicle.

[0037] For example, the accident case information is analyzed to identify key factors that affect the risk of the target vehicle. These factors may include accident frequency, loss scale, accident type, fault location, etc. For example, accident frequency refers to the number of accidents that the target vehicle has in a specific time period, loss scale refers to the financial loss caused by each accident, and accident type may include collision, fire, etc., and fault location indicates which parts of the vehicle are more likely to have problems.

[0038] Exemplarily, quantitative indicators are set for each key factor to facilitate measurement and evaluation. For example, the accident frequency can be expressed by the number of accidents per year, the loss scale can be quantified by the average loss amount per accident, and the fault location can be quantified based on the frequency of occurrence and the repair cost. Based on the collected information on accident cases, the quantitative value of each key factor is calculated. For example, the number of accidents in the past year is counted to obtain the accident frequency value, and the loss amount of each accident is calculated to obtain the loss scale value. Set corresponding risk thresholds for each key factor. These thresholds are based on historical data, industry standards or expert opinions, and are used to determine whether the key factor indicates a higher risk level. For example, a threshold for accident frequency is set. If the number of accidents of the target vehicle exceeds the threshold, the risk of the vehicle is considered to be high.

[0039] Exemplarily, the quantitative value of each key factor is compared with the corresponding risk threshold. When the quantitative value of the key factor is greater than or equal to the risk threshold, it is considered that the key factor poses a significant risk to the target vehicle. Based on the above analysis, determine which key factors serve as the initial risk profile factors of the target vehicle. These factors can reflect the actual risk status of the vehicle.

[0040] In some embodiments, the risk assessment of the target vehicle based on the accident case information to obtain an initial risk profile factor corresponding to the target vehicle includes: extracting information from the accident case information to obtain vehicle damage information and claim information, wherein the vehicle damage information includes damaged parts, damage degree and repair cost, and the claim information includes claim amount and claim frequency; performing a risk assessment on the damaged parts based on the damage degree, the repair cost, the claim amount and the claim frequency to obtain a first risk value corresponding to the damaged parts; and calculating the risk level of the target vehicle based on the first risk value to obtain the initial risk profile factor.

[0041] Exemplarily, the keyword extraction algorithm is used to extract information from accident case information to obtain vehicle loss information and claim information, wherein the vehicle damage information includes damaged parts, damage degree and repair cost, and the claim information includes claim amount and claim frequency. For example, damaged parts include but are not limited to engine, body, suspension system, brake; damage degree is to determine the damage degree of each damaged part such as mild, moderate, and severe; repair cost is to extract the repair cost of each damaged part, including but not limited to part replacement, repair man-hours and material costs. For example, the claim amount is the claim amount for each accident extracted from the claim record provided by the insurance company; the claim frequency is the number of claims calculated within a certain time range (such as one year). Among them, the more severe the damage, the higher the risk value. The higher the repair cost, the greater the risk, because the high repair cost may indicate that the damage is more serious. The larger the claim amount, the higher the severity of the damage, and the risk value should also increase accordingly. The more frequent the number of claims, the higher the risk level of the target vehicle.

[0042] For example, a weight is set for each risk assessment indicator (damage degree, repair cost, claim amount, claim frequency) to reflect its impact on the overall risk. The quantitative results of each indicator are combined with its weight to calculate the first risk value of each damaged component. The weight is set based on actual conditions and professional judgment.

[0043] Exemplarily, a risk threshold for each damaged component is defined based on industry standards, historical data or expert opinions to distinguish different levels of risk. The risk threshold can be set to different levels, such as low, medium and high risk levels. The threshold corresponding to each level can be adjusted according to actual conditions. The first risk value of each damaged component is matched with its corresponding risk threshold. If the first risk value of a damaged component is greater than or equal to its risk threshold, the component is deemed to pose a significant risk to the target vehicle. For each damaged component, it is marked according to the comparison results to determine which damaged components will be included in the initial risk profile factor. Each initial risk profile factor represents the risk status of the damaged component in the target vehicle.

[0044] Specifically, by analyzing the specific damaged parts and the extent of damage, the weak links of the vehicle can be effectively identified, which helps to predict possible future failures or accidents. At the same time, the claim amount and claim frequency reflect the severity of damage and repair needs in the history of the target vehicle, which helps to discover potential high-risk factors. A comprehensive assessment of the extent of damage, repair costs, claim amount and claim frequency can provide a more detailed and accurate risk analysis. This comprehensive assessment can not only accurately determine the risk level of the target vehicle, but also provide support for optimizing insurance pricing and services, and provide a good basis for subsequent pricing.

[0045] Step S103: Obtain a target component corresponding to the target vehicle, and obtain price change information and maintenance cost information corresponding to the target component.

[0046] Exemplarily, the target vehicle is disassembled to obtain target parts corresponding to the target vehicle, and then the price changes of the target parts are monitored to obtain the price change information corresponding to the target parts.

[0047] Exemplarily, the maintenance frequency and maintenance price corresponding to the target part during maintenance are obtained, and then the maintenance cost information corresponding to the target part is determined according to the maintenance frequency and maintenance price.

[0048] Step S104: supplement the initial risk profile factor according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle.

[0049] Exemplarily, the maintenance cost information is matched with a preset risk level standard. If the maintenance cost exceeds the set high risk threshold, the target accessory is marked as high risk. If the maintenance cost is in the medium range, the target accessory is marked as medium risk. If the maintenance cost is below the low risk threshold, the target accessory is marked as low risk. Check whether the risk level of the target accessory reaches a preset level. If the risk level of the accessory reaches or exceeds the set threshold (for example, high risk or medium risk), the risk profile factor needs to be updated. The risk level and other relevant information of the target accessory are integrated into the initial risk profile factor. The initial risk profile factor is thereby updated according to the newly added target accessory to reflect the target risk profile factor of the target vehicle.

[0050] In one embodiment, the initial risk profile factor is supplemented with information according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle. Specifically, referring to Figure 2 , step S104 includes: sub-step S1041 to sub-step S1043.

[0051] Sub-step S1041, obtaining the manufacturing technology, manufacturing materials and supply chain information corresponding to the target component according to the maintenance cost information.

[0052] Exemplarily, the production and design documents of the target accessory are searched, including product specifications, technical manuals, and bills of materials. These documents usually contain the manufacturing technology and material information of the accessory. Based on the design and production documents of the target accessory, identify the technical process used to manufacture the target accessory. For example, injection molding, casting, machining, etc. Based on the production documents or bills of materials of the target accessory, obtain the manufacturing materials used for the accessory. For example, steel, aluminum alloy, plastic, etc. Obtain the supply chain information of the target accessory, including major suppliers, procurement channels, and logistics arrangements.

[0053] Sub-step S1042: predicting the risk of the target component by using the manufacturing technology, the manufacturing materials and the supply chain information according to the risk assessment model to obtain a second risk value corresponding to the target component.

[0054] Exemplarily, the risk assessment model can be a neural network model or a machine learning model, and the manufacturing technology, manufacturing materials and supply chain information are then input into the input layer of the risk assessment model to obtain a first vector corresponding to the manufacturing technology, a second vector corresponding to the manufacturing materials and a third vector corresponding to the supply chain information, and then the first vector, the second vector and the third vector are input into the fusion layer to obtain the target vector. The fusion layer can use a variety of methods, such as splicing, weighted average or more complex fusion mechanisms, and then use the target vector to perform risk prediction according to the risk prediction layer of the risk assessment model. The risk prediction layer usually uses activation functions (such as ReLU, Sigmoid, etc.) and loss functions (such as mean square error, cross entropy loss, etc.) to calculate the prediction results, thereby obtaining the second risk value corresponding to the target accessory.

[0055] In some embodiments, the risk assessment model includes a first information extraction layer, a second information extraction layer, a first information representation layer, a second information representation layer, a third information representation layer, a data fusion layer, and a data prediction layer, and the risk assessment model is used to predict the risk of the target accessory using the manufacturing technology, the manufacturing material, and the supply chain information to obtain a second risk value corresponding to the target accessory, including: using the first information extraction layer to decompose the information of the manufacturing technology to obtain multiple sub-technologies corresponding to the manufacturing technology and the technical relationship between any two of the sub-technologies; using the second information extraction layer to decompose the information of the manufacturing material to obtain multiple sub-materials corresponding to the manufacturing material and the material relationship between any two of the sub-materials; according to The first information representation layer performs information representation on the sub-technology to obtain a first feature vector, and uses the technology relationship to perform feature fusion on the first feature vector to obtain a second feature vector; the second information representation layer performs information representation on the sub-material to obtain a third feature vector, and uses the material relationship to perform feature fusion on the third feature vector to obtain a fourth feature vector; the third information representation layer performs information representation on the supply chain information to obtain a fifth feature vector; the data fusion layer performs feature fusion on the second feature vector, the fourth feature vector and the fifth feature vector to obtain a target feature vector; the data prediction layer performs risk prediction on the target feature vector to obtain the second risk value corresponding to the target accessory.

[0056] For example, Figure 3 As shown, the risk assessment model includes a first information extraction layer, a second information extraction layer, a first information representation layer, a second information representation layer, a third information representation layer, a data fusion layer, and a data prediction layer.

[0057] For example, a manufacturing technology is analyzed and disassembled in detail to obtain multiple sub-technologies. These sub-technologies can be specific processes, technical modules or key operations. The relationship between any two sub-technologies is identified through expert knowledge, literature research or data analysis, including their dependencies, mutual influences or collaborative relationships.

[0058] Exemplarily, a similar disassembly is performed on manufacturing materials to analyze the composition, properties and uses of the materials, and identify and define multiple sub-materials. This may include raw materials, auxiliary materials and processed materials. The material relationship between any two sub-materials is determined, including their compatibility, mutual influence and possible substitution.

[0059] Exemplarily, the first information extraction layer is used to encode and extract features from the sub-techniques to obtain a first feature vector, and then the first feature vector is fused using the technical relationship between the sub-techniques. The fusion method may include weighted averaging, feature concatenation or matrix operation to obtain a second feature vector.

[0060] Exemplarily, the second information extraction layer is used to extract features from the sub-materials to obtain a third feature vector. This can be achieved through similar feature engineering techniques. And according to the material relationship between the sub-materials, the third feature vector is fused to obtain a fourth feature vector. The fusion method may include calculating the material relationship of the sub-materials for weighted combination or other forms of feature integration.

[0061] Exemplarily, the supply chain information is processed through the third information representation layer, and the relevant features of the supply chain are extracted and encoded to generate the fifth feature vector, which may involve factors such as the stability of the supply chain, supplier performance, and delivery time.

[0062] Exemplarily, at the data fusion layer, the second eigenvector, the fourth eigenvector and the fifth eigenvector are fused to obtain the target eigenvector. The purpose of fusion is to integrate the features from different sources into a unified representation for comprehensive analysis. Fusion methods may include feature concatenation, weighted averaging, principal component analysis (PCA), etc.

[0063] Exemplarily, the target feature vector is processed using a data prediction layer to predict the risk. According to the target feature vector, a suitable algorithm (such as a regression model, a classification model or a neural network) is used to calculate the final risk value, that is, the second risk value of the target accessory.

[0064] Specifically, through detailed sub-technique and sub-material characterization, the characteristics of each sub-component can be captured more accurately. This refined feature extraction can help the model better understand and handle complex risk factors, which in turn enables the risk assessment model to more comprehensively consider all relevant characteristics, improve prediction accuracy, and provide more reliable support for subsequent vehicle pricing.

[0065] Sub-step S1043: supplementing the initial risk profile factor according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle.

[0066] For example, if the second risk value is lower than the risk threshold, the target component is marked as low risk. If the second risk value is higher than the risk threshold, the target component is marked as high risk, and when the risk level of the target component reaches high risk, the risk profile factor needs to be updated. The risk level of the target component and other relevant information are integrated into the initial risk profile factor. Thus, the initial risk profile factor is updated according to the newly added target component to reflect the target risk profile factor of the target vehicle.

[0067] In some embodiments, the information of the initial risk profile factor is supplemented according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle, including: performing intersection calculation on the target accessory and the damaged part to obtain the same accessories between the target accessory and the damaged part; obtaining the first risk label corresponding to the same accessory from the initial risk profile factor, and updating the first risk label according to the second risk value to obtain the second risk label; using the second risk value to determine the third risk label corresponding to the remaining accessories in the target accessory; supplementing the initial risk profile factor according to the second risk label and the third risk label to obtain the target risk profile factor corresponding to the target vehicle.

[0068] Exemplarily, find the common accessories between the target accessories and the damaged parts, that is, those accessories that appear in both lists. Compare the lists of the target accessories and the damaged parts to find the intersection between them. Set operations (such as set intersection) can be used to identify identical accessories. Extract the first risk labels corresponding to the identical accessories from the initial risk portrait factors. These first risk labels reflect the risk judgment of these accessories during the initial assessment. Recalculate the current risk label corresponding to the identical accessory based on the second risk value. If the current risk label is higher than the risk severity corresponding to the first risk label, update the first risk label to obtain the second risk label.

[0069] Exemplarily, the remaining accessories are determined, and the remaining accessories refer to the other accessories in the target accessories except the "same accessories". The risk of the remaining accessories in the target accessories is estimated according to the second risk value to obtain the third risk label of the remaining accessories. Thus, the updated second risk label and the third risk label are incorporated into the initial risk profile factor to obtain the target risk profile factor.

[0070] Specifically, by updating the risk labels of the same accessories according to the second risk value, it is ensured that the risk labels reflect the latest risk information. This helps to adjust the risk assessment in a timely manner and avoid outdated data affecting decision-making. Estimating the risk labels of the remaining accessories based on the second risk value can ensure that the risk situation of all target accessories is fully understood. This helps to discover potential risks in accessories that are not directly damaged and improve the comprehensiveness of the overall assessment. The updated second risk label and the third risk label are supplemented with the initial risk profile factor to generate the target risk profile factor of the target vehicle. This ensures that the risk assessment covers all relevant factors, making risk management more comprehensive and accurate.

[0071] Step S105: Update the initial price according to the price change information and the target risk profile factor to obtain a target price corresponding to the target vehicle.

[0072] Exemplarily, the price change information is analyzed to determine its impact on the initial price. This typically involves calculating a ratio or absolute value of the price change information. For example, a 10% price increase may require the initial price to also be increased by 10%. The price change information is applied to the initial price to obtain an adjustment that reflects the direct impact of the price change information on the initial price. The calculated adjustment is called the first weight, which represents the degree of impact of the price change information on the price adjustment.

[0073] Exemplarily, the potential impact of the target risk profile factor on the initial price is analyzed. The target risk profile factor may lead to a price discount (such as a high failure rate) or a premium (such as a good maintenance record and a low failure rate). Based on the evaluation results of the target risk profile factor, an adjustment range is calculated, which reflects the impact of the target risk profile factor on the initial price. This may be a discount or a premium. The calculated risk adjustment range is called the second weight, which indicates the degree of influence of the target risk profile factor on the price adjustment.

[0074] Exemplarily, the first weight and the second weight are used to adjust the initial price. A common practice is to apply the two weights to the initial price, and obtain the final target price by weighted average, addition or other suitable methods.

[0075] In some embodiments, the initial price is updated according to the price change information and the target risk profile factor to obtain the target price corresponding to the target vehicle, including: obtaining the price trend, fluctuation range, and periodic change corresponding to the target accessory from the price change information; determining the first impact factor of the target accessory on the initial price according to the price trend, the fluctuation range, and the periodic change; determining the second impact factor of the accident case information on the initial price according to the target risk profile factor; and adjusting the initial price using the first impact factor and the second impact factor to obtain the target price corresponding to the target vehicle.

[0076] For example, the long-term trend of price change information is analyzed, such as whether the price is gradually rising, falling, or stable. The price trend can be determined by calculating the price difference. The volatility of price change information is analyzed, such as the difference between the maximum and minimum values ​​of the price. Statistical methods (such as standard deviation) can be used to quantify price fluctuations to obtain the fluctuation amplitude. Determine the periodic change pattern in the price change information, such as seasonal fluctuations. Periodic changes can be identified by observing the repetitive pattern of data or periodograms.

[0077] For example, price trends, fluctuations, and periodic changes are integrated to evaluate the combined impact of these factors on the price of the target accessory. For example, an upward price trend and a larger fluctuation may result in a greater impact on the price of the target accessory.

[0078] Exemplarily, the analysis results of price trends, fluctuations, and cyclical changes are considered together to evaluate their overall impact on the target accessory. These factors can be converted into weights or adjustments. The first impact factor is calculated based on the results of the comprehensive analysis. For example, if the price trend is rising, the fluctuation is large, and there are seasonal changes, the first impact factor may be high, indicating that the adjustment to the initial price will also be large. The determined first impact factor is applied to the pricing strategy of the target accessory. This can be an adjustment coefficient that directly affects the calculation of the initial price.

[0079] Exemplarily, the target risk profile factors are classified, such as being divided into external risks (such as environmental factors, technical impacts) and internal risks (such as vehicle performance, historical maintenance conditions), and then weights are assigned to external risks and internal risks according to the degree of impact of each risk factor on the overall risk. These weights represent the relative importance of each type of risk to the overall impact. The risk value of the external risk factor is multiplied by the corresponding weight, and the sum is obtained to obtain the comprehensive risk value of the external risk. The risk value of the internal risk factor is multiplied by the corresponding weight, and the sum is obtained to obtain the comprehensive risk value of the internal risk. The comprehensive risk value of the external risk and the comprehensive risk value of the internal risk are weighted and fused. The second influencing factor can be calculated by simple addition, weighted average, and the like.

[0080] Exemplarily, based on the type of the first impact factor (e.g., a percentage), the amount of adjustment required is calculated. If the first impact factor is a percentage adjustment, it is applied to the initial price. Based on the type of the second impact factor, an additional adjustment amount is calculated. If the second impact factor is a fixed amount or another percentage, it is applied to the price adjusted by the first impact factor.

[0081] Exemplarily, the first influencing factor is applied to the initial price to calculate the adjusted middle price, and the second influencing factor is applied to the middle price to obtain the final target price.

[0082] Specifically, by analyzing price change information in detail, including price trends, fluctuations, and cyclical changes, the market dynamics of the target accessories can be more accurately understood. This analysis helps determine a more accurate first influencing factor, making the pricing of the target vehicle more in line with the actual market situation, thereby improving the accuracy of pricing. The impact of the accident case information on the initial price is determined based on the target risk profile factor, so that the pricing can reflect potential risk factors. By analyzing the accident case information, the second influencing factor obtained can effectively capture the actual impact of risk on price, ensuring that price adjustments not only take into account market trends, but also reflect the actual risk situation.

[0083] In some embodiments, after obtaining the target price, the method further includes: updating the accident case information according to a preset time to obtain the updated accident case information; updating the price change information and the repair cost information according to the preset time to obtain the updated price change information and the repair cost information; updating the target price according to the updated accident case information, the updated price change information and the updated repair cost information to obtain the updated target price.

[0084] For example, according to business needs, a suitable update frequency is set, such as monthly or semi-annual updates. When the preset time arrives, the latest accident case information is obtained from the relevant system or database, and the price change information and maintenance cost information are updated according to the preset time to obtain updated price change information and maintenance cost information.

[0085] For example, based on the updated price change information, the price trend, fluctuation range and periodic changes are recalculated to determine a new first influencing factor. And based on the updated accident case information and repair cost information, the target risk profile factor is re-evaluated to determine a new second influencing factor.

[0086] Exemplarily, the target price is readjusted to obtain the latest target price using the new first influencing factor and the new second influencing factor.

[0087] Specifically, by setting a preset time, regularly updating accident case information, price change information, and repair cost information, and recalculating the impact factors and adjusting the target price accordingly, it can ensure that the target price is always based on the latest market data and business conditions. This regular update mechanism helps to improve the accuracy and timeliness of pricing, thereby better adapting to market changes and business needs.

[0088] The embodiment of the present invention provides a method, device, terminal device and storage medium for vehicle pricing. The method performs risk assessment on a target vehicle by introducing accident case information and obtains an initial risk profile factor, which can provide a comprehensive risk overview for the target vehicle. This step uses historical data and case information to establish a preliminary risk profile of the target vehicle to help identify potential risk factors. Subsequently, by analyzing the maintenance cost information, the initial risk profile factor is supplemented and adjusted in depth to generate a target risk profile factor for the target vehicle. This supplement not only includes changes in maintenance costs, but also takes into account possible additional risks, so that the risk profile factor is more accurate and comprehensive. Next, the initial price is updated according to the price change information and the target risk profile factor to obtain the target price of the target vehicle. This update process will reflect the actual market situation and risk assessment results, making the price adjustment more reasonable and targeted. This method enables vehicle pricing to better adapt to market dynamics and risk changes, thereby improving pricing accuracy and market adaptability. Finally, the pricing method enables claims professionals to effectively empower the vehicle pricing process. The target vehicle is adjusted based on price change information and target risk profile factors, so that the risk benchmark can be quickly located and reflect the actual market demand. This not only improves the accuracy of pricing, but also avoids the problems that may be encountered in traditional pure data pricing models, such as the risk of losing market share due to overpricing, or the risk of insurance companies losing money due to underpricing. By comprehensively considering risk factors and market changes, this method achieves dynamic pricing that is more in line with market demand, thereby optimizing pricing strategies and improving the competitiveness and market performance of insurance companies.

[0089] See also Figure 4 , Figure 4 A vehicle pricing device 200 is provided in an embodiment of the present application, and the vehicle pricing device 200 includes a data acquisition module 201, a risk assessment module 202, an information acquisition module 203, an information supplementation module 204, and a price determination module 205, wherein the data acquisition module 201 is used to determine a target vehicle and an initial price corresponding to the target vehicle, and obtain accident case information corresponding to the target vehicle; the risk assessment module 202 is used to perform a risk assessment on the target vehicle according to the accident case information, and obtain an initial risk profile factor corresponding to the target vehicle; the information acquisition module 203 is used to obtain target accessories corresponding to the target vehicle, and obtain price change information and maintenance cost information corresponding to the target accessories; the information supplementation module 204 is used to supplement the initial risk profile factor according to the maintenance cost information, and obtain a target risk profile factor corresponding to the target vehicle; the price determination module 205 is used to update the initial price according to the price change information and the target risk profile factor, and obtain a target price corresponding to the target vehicle.

[0090] In some implementations, the risk assessment module 202, in the process of performing risk assessment on the target vehicle according to the accident case information and obtaining the initial risk profile factor corresponding to the target vehicle, executes:

[0091] Extracting information from the accident case information to obtain vehicle damage information and claim information, wherein the vehicle damage information includes damaged parts, damage degree and repair cost, and the claim information includes claim amount and claim frequency;

[0092] Performing a risk assessment on the damaged component according to the damage degree, the repair cost, the claim amount and the claim frequency to obtain a first risk value corresponding to the damaged component;

[0093] The risk level of the target vehicle is calculated according to the first risk value to obtain the initial risk profile factor.

[0094] In some implementations, the information supplementation module 204 performs, during the process of supplementing the initial risk profile factor according to the maintenance cost information to obtain the target risk profile factor corresponding to the target vehicle:

[0095] Obtaining manufacturing technology, manufacturing materials and supply chain information corresponding to the target component according to the maintenance cost information;

[0096] Performing risk prediction on the target component by using the manufacturing technology, the manufacturing materials and the supply chain information according to the risk assessment model to obtain a second risk value corresponding to the target component;

[0097] The initial risk profile factor is supplemented with information according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle.

[0098] In some embodiments, the risk assessment model includes a first information extraction layer, a second information extraction layer, a first information representation layer, a second information representation layer, a third information representation layer, a data fusion layer, and a data prediction layer. In the process of performing risk prediction on the target component using the manufacturing technology, the manufacturing materials, and the supply chain information according to the risk assessment model to obtain the second risk value corresponding to the target component, the information supplement module 204 executes:

[0099] Using the first information extraction layer to decompose the manufacturing technology to obtain multiple sub-technologies corresponding to the manufacturing technology and a technical relationship between any two of the sub-technologies;

[0100] Using the second information extraction layer to decompose the manufacturing material to obtain a plurality of sub-materials corresponding to the manufacturing material and a material relationship between any two of the sub-materials;

[0101] Performing information representation on the sub-technology according to the first information representation layer to obtain a first feature vector, and performing feature fusion on the first feature vector using the technical relationship to obtain a second feature vector;

[0102] Performing information representation on the sub-material according to the second information representation layer to obtain a third feature vector, and performing feature fusion on the third feature vector using the material relationship to obtain a fourth feature vector;

[0103] Characterizing the supply chain information according to the third information characterization layer to obtain a fifth feature vector;

[0104] According to the data fusion layer, the second feature vector, the fourth feature vector and the fifth feature vector are subjected to feature fusion to obtain a target feature vector;

[0105] Perform risk prediction on the target feature vector according to the data prediction layer to obtain the second risk value corresponding to the target accessory.

[0106] In some implementations, the information supplementing module 204 performs, during the process of supplementing the initial risk profile factor according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle:

[0107] Performing intersection calculation on the target component and the damaged component to obtain the same components between the target component and the damaged component;

[0108] Obtaining a first risk label corresponding to the same accessory from the initial risk profile factor, and updating the first risk label according to the second risk value to obtain a second risk label;

[0109] Determine a third risk label corresponding to the remaining accessories in the target accessory by using the second risk value;

[0110] The initial risk profile factor is supplemented with information according to the second risk label and the third risk label to obtain the target risk profile factor corresponding to the target vehicle.

[0111] In some implementations, the price determination module 205, in the process of updating the initial price according to the price change information and the target risk profile factor to obtain the target price corresponding to the target vehicle, executes:

[0112] Obtaining the price trend, fluctuation range, and periodic change corresponding to the target accessory from the price change information;

[0113] Determining a first impact factor of the target accessory on the initial price according to the price trend, the fluctuation range and the periodic change;

[0114] Determine a second impact factor corresponding to the initial price of the risk case information according to the target risk profile factor;

[0115] The initial price is adjusted using the first influencing factor and the second influencing factor to obtain the target price corresponding to the target vehicle.

[0116] In some implementations, after obtaining the target price, the price determination module 205 further performs:

[0117] Update the accident case information according to a preset time to obtain the updated accident case information;

[0118] updating the price change information and the maintenance cost information according to the preset time to obtain the updated price change information and the maintenance cost information;

[0119] The target price is updated according to the updated accident case information, the updated price change information and the updated repair cost information to obtain the updated target price.

[0120] In some implementations, the vehicle price pricing apparatus 200 may be applied to a terminal device.

[0121] It should be noted that, those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working process of the vehicle price pricing device 200 described above can refer to the corresponding process in the aforementioned vehicle price pricing method embodiment, and will not be repeated here.

[0122] See also Figure 5 , Figure 5 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0123] like Figure 5 As shown, the terminal device 300 includes a processor 301 and a memory 302, and the processor 301 and the memory 302 are connected via a bus 303, such as an I2C (Inter-integrated Circuit) bus.

[0124] Specifically, the processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. The processor 301 can be a central processing unit (CPU), and the processor 301 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0125] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0126] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a partial structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] The processor is used to run a computer program stored in the memory, and implement any one of the vehicle price pricing methods provided by the embodiments of the present invention when executing the computer program.

[0128] In one embodiment, the processor is used to run a computer program stored in the memory, and implements the following steps when executing the computer program:

[0129] Determine a target vehicle and an initial price corresponding to the target vehicle, and obtain accident case information corresponding to the target vehicle;

[0130] Performing risk assessment on the target vehicle according to the accident case information to obtain an initial risk profile factor corresponding to the target vehicle;

[0131] Obtaining a target accessory corresponding to the target vehicle, and obtaining price change information and maintenance cost information corresponding to the target accessory;

[0132] Supplementing the initial risk profile factor according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle;

[0133] The initial price is updated according to the price change information and the target risk profile factor to obtain a target price corresponding to the target vehicle.

[0134] In some implementations, the processor 301, in the process of performing risk assessment on the target vehicle according to the accident case information and obtaining the initial risk profile factor corresponding to the target vehicle, executes:

[0135] Extracting information from the accident case information to obtain vehicle damage information and claim information, wherein the vehicle damage information includes damaged parts, damage degree and repair cost, and the claim information includes claim amount and claim frequency;

[0136] Performing a risk assessment on the damaged component according to the damage degree, the repair cost, the claim amount and the claim frequency to obtain a first risk value corresponding to the damaged component;

[0137] The risk level of the target vehicle is calculated according to the first risk value to obtain the initial risk profile factor.

[0138] In some implementations, the processor 301, in the process of supplementing the initial risk profile factor according to the maintenance cost information to obtain the target risk profile factor corresponding to the target vehicle, executes:

[0139] Obtaining manufacturing technology, manufacturing materials and supply chain information corresponding to the target component according to the maintenance cost information;

[0140] Performing risk prediction on the target component by using the manufacturing technology, the manufacturing materials and the supply chain information according to the risk assessment model to obtain a second risk value corresponding to the target component;

[0141] The initial risk profile factor is supplemented with information according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle.

[0142] In some embodiments, the risk assessment model includes a first information extraction layer, a second information extraction layer, a first information representation layer, a second information representation layer, a third information representation layer, a data fusion layer, and a data prediction layer. The processor 301 performs the following process in which the manufacturing technology, the manufacturing materials, and the supply chain information are used to predict the risk of the target component according to the risk assessment model to obtain the second risk value corresponding to the target component:

[0143] Using the first information extraction layer to decompose the manufacturing technology to obtain multiple sub-technologies corresponding to the manufacturing technology and a technical relationship between any two of the sub-technologies;

[0144] Using the second information extraction layer to decompose the manufacturing material to obtain a plurality of sub-materials corresponding to the manufacturing material and a material relationship between any two of the sub-materials;

[0145] Performing information representation on the sub-technology according to the first information representation layer to obtain a first feature vector, and performing feature fusion on the first feature vector using the technical relationship to obtain a second feature vector;

[0146] Performing information representation on the sub-material according to the second information representation layer to obtain a third feature vector, and performing feature fusion on the third feature vector using the material relationship to obtain a fourth feature vector;

[0147] Characterizing the supply chain information according to the third information characterization layer to obtain a fifth feature vector;

[0148] According to the data fusion layer, the second feature vector, the fourth feature vector and the fifth feature vector are subjected to feature fusion to obtain a target feature vector;

[0149] Perform risk prediction on the target feature vector according to the data prediction layer to obtain the second risk value corresponding to the target accessory.

[0150] In some implementations, the processor 301, in the process of supplementing the initial risk profile factor according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle, executes:

[0151] Performing intersection calculation on the target component and the damaged component to obtain the same components between the target component and the damaged component;

[0152] Obtaining a first risk label corresponding to the same accessory from the initial risk profile factor, and updating the first risk label according to the second risk value to obtain a second risk label;

[0153] Determine a third risk label corresponding to the remaining accessories in the target accessory by using the second risk value;

[0154] The initial risk profile factor is supplemented with information according to the second risk label and the third risk label to obtain the target risk profile factor corresponding to the target vehicle.

[0155] In some implementations, the processor 301, in the process of updating the initial price according to the price change information and the target risk profile factor to obtain the target price corresponding to the target vehicle, executes:

[0156] Obtaining the price trend, fluctuation range, and periodic change corresponding to the target accessory from the price change information;

[0157] Determining a first impact factor of the target accessory on the initial price according to the price trend, the fluctuation range and the periodic change;

[0158] Determine a second impact factor corresponding to the initial price of the risk case information according to the target risk profile factor;

[0159] The initial price is adjusted using the first influencing factor and the second influencing factor to obtain the target price corresponding to the target vehicle.

[0160] In some implementations, after obtaining the target price, the processor 301 further executes:

[0161] Update the accident case information according to a preset time to obtain the updated accident case information;

[0162] updating the price change information and the maintenance cost information according to the preset time to obtain the updated price change information and the maintenance cost information;

[0163] The target price is updated according to the updated accident case information, the updated price change information and the updated repair cost information to obtain the updated target price.

[0164] It should be noted that technicians in the relevant field can clearly understand that for the convenience and brevity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned vehicle price pricing method embodiment, and will not be repeated here.

[0165] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any vehicle price pricing method provided in the description of the embodiment of the present invention.

[0166] The storage medium may be an internal storage unit of the terminal device in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device.

[0167] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0168] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0169] The serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for determining vehicle prices, characterized in that: The method comprises: Determine a target vehicle and an initial price corresponding to the target vehicle, and obtain accident case information corresponding to the target vehicle; Performing risk assessment on the target vehicle according to the accident case information to obtain an initial risk profile factor corresponding to the target vehicle; Obtaining a target accessory corresponding to the target vehicle, and obtaining price change information and maintenance cost information corresponding to the target accessory; Supplementing the initial risk profile factor according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle; The initial price is updated according to the price change information and the target risk profile factor to obtain a target price corresponding to the target vehicle.

2. The method according to claim 1, characterized in that The step of performing risk assessment on the target vehicle according to the accident case information to obtain an initial risk profile factor corresponding to the target vehicle includes: Extracting information from the accident case information to obtain vehicle damage information and claim information, wherein the vehicle damage information includes damaged parts, damage degree and repair cost, and the claim information includes claim amount and claim frequency; Performing a risk assessment on the damaged component according to the damage degree, the repair cost, the claim amount and the claim frequency to obtain a first risk value corresponding to the damaged component; The risk level of the target vehicle is calculated according to the first risk value to obtain the initial risk profile factor.

3. The method according to claim 1, characterized in that The step of supplementing the initial risk profile factor according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle includes: Obtaining manufacturing technology, manufacturing materials and supply chain information corresponding to the target component according to the maintenance cost information; Performing risk prediction on the target component by using the manufacturing technology, the manufacturing materials and the supply chain information according to the risk assessment model to obtain a second risk value corresponding to the target component; The initial risk profile factor is supplemented with information according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle.

4. The method according to claim 3, characterized in that The risk assessment model includes a first information extraction layer, a second information extraction layer, a first information representation layer, a second information representation layer, a third information representation layer, a data fusion layer, and a data prediction layer. The risk assessment model is used to predict the risk of the target component using the manufacturing technology, the manufacturing materials, and the supply chain information to obtain a second risk value corresponding to the target component, including: Using the first information extraction layer to decompose the manufacturing technology to obtain multiple sub-technologies corresponding to the manufacturing technology and a technical relationship between any two of the sub-technologies; Using the second information extraction layer to decompose the manufacturing material to obtain a plurality of sub-materials corresponding to the manufacturing material and a material relationship between any two of the sub-materials; Performing information representation on the sub-technology according to the first information representation layer to obtain a first feature vector, and performing feature fusion on the first feature vector using the technical relationship to obtain a second feature vector; Performing information representation on the sub-material according to the second information representation layer to obtain a third feature vector, and performing feature fusion on the third feature vector using the material relationship to obtain a fourth feature vector; Characterizing the supply chain information according to the third information characterization layer to obtain a fifth feature vector; According to the data fusion layer, the second feature vector, the fourth feature vector and the fifth feature vector are subjected to feature fusion to obtain a target feature vector; Perform risk prediction on the target feature vector according to the data prediction layer to obtain the second risk value corresponding to the target accessory.

5. The method according to claim 3, characterized in that: The step of supplementing the initial risk profile factor according to the second risk value to obtain the target risk profile factor corresponding to the target vehicle includes: Performing intersection calculation on the target component and the damaged component to obtain the same components between the target component and the damaged component; Obtaining a first risk label corresponding to the same accessory from the initial risk profile factor, and updating the first risk label according to the second risk value to obtain a second risk label; Determine a third risk label corresponding to the remaining accessories in the target accessory by using the second risk value; The initial risk profile factor is supplemented with information according to the second risk label and the third risk label to obtain the target risk profile factor corresponding to the target vehicle.

6. The method according to claim 1, characterized in that The updating of the initial price according to the price change information and the target risk profile factor to obtain the target price corresponding to the target vehicle includes: Obtaining the price trend, fluctuation range, and periodic change corresponding to the target accessory from the price change information; Determining a first impact factor of the target accessory on the initial price according to the price trend, the fluctuation range and the periodic change; Determine a second impact factor corresponding to the initial price of the risk case information according to the target risk profile factor; The initial price is adjusted using the first influencing factor and the second influencing factor to obtain the target price corresponding to the target vehicle.

7. The method according to any one of claims 1 to 6, characterized in that After obtaining the target price, the method further includes: Update the accident case information according to a preset time to obtain the updated accident case information; updating the price change information and the maintenance cost information according to the preset time to obtain the updated price change information and the maintenance cost information; The target price is updated according to the updated accident case information, the updated price change information and the updated repair cost information to obtain the updated target price.

8. A device for determining vehicle prices, characterized in that: include: A data acquisition module, used to determine a target vehicle and an initial price corresponding to the target vehicle, and obtain accident case information corresponding to the target vehicle; A risk assessment module, used to perform risk assessment on the target vehicle according to the accident case information, and obtain an initial risk profile factor corresponding to the target vehicle; An information acquisition module, used to obtain target parts corresponding to the target vehicle, and obtain price change information and maintenance cost information corresponding to the target parts; An information supplementation module, used to supplement the initial risk profile factor according to the maintenance cost information to obtain a target risk profile factor corresponding to the target vehicle; The price determination module is used to update the initial price according to the price change information and the target risk profile factor to obtain the target price corresponding to the target vehicle.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the vehicle price pricing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the vehicle price pricing method according to any one of claims 1 to 7.

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