Vehicle accident loss assessment risk control method, device, electronic device and storage medium

Abstract

To provide a vehicle accident loss assessment risk control method and an apparatus.SOLUTION: The method can include: acquiring a vehicle accident loss assessment report of a current accident of a vehicle, in which the vehicle accident loss assessment report includes vehicle information of the vehicle and vehicle loss items related to the current accident of the vehicle; calculating the vehicle loss items in the vehicle accident loss assessment report based on a pre-established risk algorithm model in combination with the vehicle information, and identifying risk items with risks in the vehicle loss items; and intercepting the risk items or prompting the risk items to a user. According to the embodiment, a warning can be automatically made to a leakage that may occur in the vehicle accident loss assessment based on artificial intelligence, industry big data standardization, big data mining analysis and the like, to realize the risk control and ensure the accuracy of vehicle accident loss assessment.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to the fields of vehicle information technology and vehicle loss assessment, and particularly relates to a method, apparatus, electronic device, and storage medium for controlling vehicle accident loss assessment risks.

Background Art

[0002] With the rapid development of the Chinese economy, people's consumption levels have gradually improved, and vehicles have gradually changed from previous high-end consumer goods to daily means of transportation for families. Vehicle insurance services have developed rapidly as value-added services, and vehicle insurance has become the largest type of insurance industry with a market share of 60%. Vehicle insurance is a mandatory insurance business required by the state and an insurance business that all automobiles must purchase. This insurance mainly aims to protect property or personal losses caused by traffic accidents that may be encountered during driving.

[0003] In the processes of vehicle insurance claims and damage assessment, etc., accurately and reasonably evaluating vehicle accident losses is an important matter for insurance companies to fulfill their insurance obligations and is also the most crucial prerequisite key point in the insurance claim process. However, in the vehicle loss assessment process, the employees responsible for vehicle accident damage assessment in insurance companies often have untrue and unreasonable assessment contents due to their own skills, quality factors, and the complexity and professionalism factors of vehicle accident losses, which may cause insurance claim omissions for insurance companies and increase the compensation costs of insurance companies. How to control the risk of insurance claims from the source of vehicle accident loss assessment and reduce compensation costs has become particularly important.

[0004] In the process of the insurance company's vehicle accident loss assessment, in order to improve the timeliness of insurance claims and customer satisfaction, while it is being evaluated by the damage assessment personnel of the former insurance company and will also be evaluated by the repair companies that repaired the vehicles, for accidents with small losses, the vehicle owners will further conduct independent evaluations. However, driven by profit, it is inevitable that the evaluations of repair companies deviate in the evaluation process, and the independent damage assessment evaluations of vehicle owners are likely to deviate due to lack of professional capabilities. Customers' needs are constantly changing, and the business model needs to shift in the process of meeting the changing needs of customers. The traditional empirical evaluation and review method no longer adapts to the development of the industry, and in addition to lacking an efficient and intelligent risk control method, there is an inevitable trend to shift from manual experience review to equipment review.

[0005] The evaluation leakage of vehicle insurance accident losses is assumed to reach more than tens of billions of Chinese yuan in the current industry, which has a profound impact on the compensation costs of insurance companies, affects the compensation capabilities of insurance companies, causes huge waste of social wealth, and is an obstacle that has been difficult to solve in the insurance industry.

[0006] Therefore, in the process of vehicle accident loss assessment, how insurance companies can evaluate efficiently and accurately while improving their risk identification capabilities and preventing risk loss leakage in vehicle accident loss assessment is an issue that needs to be considered urgently.

Summary of the Invention

Problems to be Solved by the Invention

[0007] In view of this, embodiments of the present invention provide a vehicle accident loss assessment risk control method and device that can automatically give an alarm for possible leakage in vehicle accident loss assessment, realize risk control, and ensure the accuracy of vehicle accident loss assessment based on AI artificial intelligence, industry big data standardization, big data mining analysis, etc.

[0008] In order to achieve the above object, according to a first aspect of the present invention, a vehicle accident loss evaluation report including vehicle information of the vehicle in the current accident of the vehicle and vehicle loss items related to the current accident of the vehicle is obtained, Based on a pre-constructed risk algorithm model, the vehicle information is combined and calculated for the vehicle loss items in the vehicle accident loss evaluation report to identify risk items where risks exist in the vehicle loss items, Provided is a vehicle accident loss evaluation risk control method including blocking the risk items or presenting them to the user.

[0009] According to the vehicle accident loss evaluation risk control method according to the first aspect of the present invention, by using a pre-constructed risk algorithm model to perform intelligent judgment on the vehicle accident loss items to be input or reported in the initially determined vehicle accident loss evaluation information of the insurance claim vehicle, risk items having risks can be calculated, and direct forced blocking can be performed, or these risk items (items not corresponding to the current vehicle accident loss or determined to be not properly evaluated) can be presented to the user in the generated risk control report. Therefore, the user can perform corresponding operations based on the risk items presented by the system, reduce the vehicle accident loss evaluation risk, make the vehicle accident loss evaluation result more accurate and reasonable, realize efficient and intelligent vehicle accident loss evaluation risk control, and significantly reduce insurance leakage.

[0010] The vehicle accident loss evaluation risk control method according to the first aspect of the present invention performs various calculations and judgments by using a pre-constructed risk algorithm model, that is, performs risk evaluation by a computer using an artificial intelligence method to realize mechanical review of vehicle accident loss evaluation. Compared with the conventional manual review that depends on the professional technical level of employees, it greatly reduces labor costs, saves time, and improves accuracy and objectivity.

[0011] Preferably, in the vehicle accident loss assessment risk control method, the vehicle information may include a vehicle frame number VIN, vehicle characteristics (own vehicle / vehicle being collided with), accident cause (such as collision), loss assessment method (such as repair loss assessment).

[0012] According to the vehicle accident loss assessment risk control method, appropriate risk control can be performed based on vehicle information, and reliability is improved.

[0013] Preferably, in the vehicle accident loss assessment risk control method, the pre-constructed risk algorithm model includes steps of virtualizing the vehicle as a corresponding figure in a three-dimensional coordinate system based on the vehicle information of the vehicle, and assigning three-dimensional coordinates to vehicle parts based on the actual mounting positions of the vehicle parts on the vehicle; dividing the vehicle into different collision parts, and establishing the correspondence between the collision parts and the vehicle parts by creating three-dimensional coordinate data for each of the collision parts based on the vehicle information, and includes a vehicle collision part algorithm model constructed by these steps.

[0014] Preferably, in the vehicle accident loss assessment risk control method, the calculations performed on the vehicle loss items in the vehicle accident loss assessment report based on the vehicle collision part algorithm model include calculating the collision part of the current accident based on the vehicle loss items in the current vehicle accident loss assessment report of the acquired vehicle, comparing the current vehicle accident loss assessment report of the vehicle with the past vehicle accident loss assessment reports of the own vehicle, calculating whether there are the same collision parts or the same damaged parts, and if calculated to exist, identifying the same collision part or damaged part as a risk item; and when it is calculated that there are multiple collision parts in the current accident of the vehicle, determining whether there is a collision part that does not conform to the collision logic among the multiple collision parts, and if calculated to exist, identifying the collision part that does not conform to the collision logic as a risk item, and includes at least one of these calculations.

[0015] Preferably, in the vehicle accident loss assessment risk control method, the pre-constructed risk algorithm model Based on the past vehicle accident loss assessment reports of various types of vehicles, in combination with the loss photos of the past vehicle accident loss assessments, manually marking the collision location and damage degree for past vehicle accident cases, Based on the collision location and damage degree manually marked for the past vehicle accident cases, using a computer to perform machine learning on the past vehicle accident loss assessment reports of various types of vehicles and the images of the collision location and damage degree, to obtain the correspondence between the collision location and damage degree and the vehicle loss items. It includes a vehicle collision location damage degree algorithm model constructed by these steps.

[0016] Preferably, in the vehicle accident loss assessment risk control method, the calculation performed on the vehicle loss items in the vehicle accident loss assessment report based on the vehicle collision location damage degree algorithm model Based on the vehicle loss items in the current vehicle accident loss assessment report of the obtained vehicle and the collision location calculated by the vehicle collision location algorithm model included in the risk algorithm model, calculating the damage degree of the collision location of the current accident, identifying the part loss boundary for the case of the collision location and the damage degree, determining whether there are damaged parts outside the part loss boundary in the damaged parts, and if it is determined that there are, identifying the damaged parts outside the part loss boundary as risk items.

[0017] Preferably, in the vehicle accident loss assessment risk control method, the pre-constructed risk algorithm model Using the records in the past vehicle accident loss assessment reports of various types of vehicles as sample data, performing big data mining analysis, calculating and storing the damage-related density between each vehicle part during vehicle accident damage. It includes a vehicle part damage-related density model constructed by these steps. The damage-related density includes at least one of the probability that when one vehicle part is damaged and becomes a damaged part, the other vehicle part also becomes a damaged part accordingly, and the probability that when one vehicle part is damaged and becomes a damaged part, the other vehicle part does not become a damaged part.

[0018] Preferably, in the vehicle accident loss assessment risk control method, the calculation performed on the vehicle loss items in the vehicle accident loss assessment report based on the vehicle part damage-related density model is Based on the vehicle loss items in the current vehicle accident loss assessment report of the obtained vehicle, in combination with the damage-related density, it is identified whether another part having a strong correlation with one damaged part included in the vehicle loss items is also recorded as a damaged part in the vehicle loss items. If it is identified as not recorded, the calculation of identifying the one damaged part as a risk item is at least included. Having the strong correlation means that when one vehicle part is damaged and becomes a damaged part, the probability that the other vehicle part also becomes a damaged part accordingly is higher than a predetermined threshold.

[0019] Preferably, in the vehicle accident loss assessment risk control method, The pre-constructed risk algorithm model includes a vehicle original manufacturer standard equipment information model constructed by performing big data standardization on the vehicle original manufacturer equipment information. The vehicle original manufacturer equipment information includes at least one of the equipment data of the vehicle original manufacturer, part codes, single vehicle usage amount of parts, relationship between part assemblies and parts, guiding prices of parts, and usage amount of auxiliary materials.

[0020] Preferably, in the vehicle accident loss assessment risk control method, the calculation performed on the vehicle loss items in the vehicle accident loss assessment report based on the vehicle original manufacturer standard equipment information model is Based on the vehicle information, identify the vehicle original manufacturer's equipment information corresponding to the vehicle according to the vehicle original manufacturer's standard equipment information model, compare the information of the damaged parts in the vehicle loss items with the vehicle original manufacturer's equipment information, and when the comparison results of the two do not match, include the calculation of identifying the non-matching damaged parts as risk items.

[0021] Preferably, in the above vehicle accident loss assessment risk control method, the pre-constructed risk algorithm model includes a vehicle part standard repair process model constructed by standardizing vehicle part repair technical data and repair process standards into big data.

[0022] Preferably, in the above vehicle accident loss assessment risk control method, the calculation performed on the vehicle loss items in the vehicle accident loss assessment report based on the vehicle part standard repair process model Based on the vehicle information, compare the repair information of the damaged parts in the vehicle loss items with the repair technical data and repair process standards according to the vehicle part standard repair process model, and when the comparison results of the two do not match, include the calculation of identifying the non-matching damaged parts as risk items.

[0023] According to the vehicle accident loss assessment risk control method of the first aspect of the present invention, in the pre-constructed risk algorithm model that serves as the basis for risk control calculation, there are at least five models, namely, the vehicle collision location algorithm model, the vehicle collision location damage degree algorithm model, the vehicle part damage related density model, the vehicle original manufacturer standard equipment information model, and the vehicle part standard repair process model. By comprehensively considering the four aspects of the collision location and damage degree of the vehicle accident, the accident damage related density between parts, the original manufacturer's standard configuration, and the repair process, and performing machine learning based on the data of past real cases and based on the content of the vehicle loss assessment report, the collision location and damage degree of the vehicle can be identified. Through the data mining algorithm, probability calculations can be performed for different vehicle models of different brands, different collision locations, different collision damage degrees, and events where part damage does not occur. By performing big data standardization on vehicle original manufacturer equipment data, part data, repair technology data, repair process standards, etc., a set of big data rules for loss assessment risk identification and judgment can be formed. Therefore, comprehensive, reliable, accurate, and intelligent risk control can be performed, and the risk control ability can be improved.

[0024] In addition, by using the vehicle accident loss assessment risk control method of the first aspect of the present invention, an efficient, intelligent, and convenient vehicle accident loss risk control tool is provided. It breaks through the models and concepts of conventional vehicle accident loss risk control, innovates technology, innovates from conventional empirical rules to big data mining analysis, converts from manual experience review to equipment review, greatly improves the review efficiency, saves the compensation cost of the insurance industry, and avoids the waste of social wealth. Based on AAI artificial intelligence, industry big data standardization, and big data mining analysis, a complete risk control system is formed to solve the pain points of the industry.

[0025] Preferably, in the vehicle accident loss assessment risk control method, after presenting risks to the user, the user selects whether to perform risk modification based on the description of each risk item presented, and if it is selected to perform risk modification, risk recognition and judgment are performed again based on the risk algorithm model.

[0026] According to the vehicle accident loss assessment risk control method implemented above, calibration and circulation control can be realized for risk items based on the user's selection, and the operability and accuracy can be improved.

[0027] A second aspect of the present invention is an acquisition unit for acquiring a vehicle accident loss assessment report including the vehicle information of the vehicle in the current accident of the vehicle and the vehicle loss items related to the current accident of the vehicle, a risk identification and judgment unit for calculating, based on a pre-constructed risk algorithm model, in combination with the vehicle information, for the vehicle loss items in the vehicle accident loss assessment report, and identifying risk items that are items for which it is determined that there is a risk in the vehicle loss items, that do not correspond to the current vehicle accident loss, or that are determined to be inappropriate in evaluation, and a risk processing unit for blocking or presenting the risk items to the user, and provides a vehicle accident loss assessment risk control device including the same.

[0028] According to the vehicle accident injury assessment risk control device according to the second aspect of the present invention, various technical effects similar to those of the first aspect can be achieved.

[0029] A third aspect of the present invention provides an electronic device including one or more processors and a storage device storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the method according to the first aspect is realized by the one or more processors.

[0030] A fourth aspect of the present invention provides a computer-readable medium storing a computer program, which, when executed by a processor, realizes the method according to the first aspect.

[0031] The inventions according to the second to fourth aspects of the present invention have the same technical effects as those of the first aspect.

[0032] Regarding further effects of the above-described non-conventional selectable forms, specific embodiments will be described below with reference to specific embodiments.

Brief Description of the Drawings

[0033] The drawings are for better understanding of the present invention and do not unduly limit the present invention.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Embodiments for Carrying Out the Invention

[0034] Hereinafter, exemplary embodiments of the present invention including various details for easy understanding will be described with reference to the drawings, but they are merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for the sake of clarity and brevity, descriptions of known functions and structures are omitted in the following description.

[0035] The vehicle related to the vehicle accident loss assessment risk control method in this application is particularly applicable to four-wheel passenger cars, but is not limited thereto.

[0036] FIG. 1 is a schematic diagram of the main flow of the vehicle accident loss assessment risk control method according to an embodiment of the present invention. As shown in FIG. 1, the vehicle accident loss assessment risk control method according to an embodiment of the present invention mainly includes steps S101-S103.

[0037] In step S101, the system can obtain a vehicle accident loss assessment report including the vehicle information of the vehicle in the current accident of the vehicle and the vehicle loss items related to the current accident of the vehicle. The vehicle information mentioned here may include the vehicle brand and model, the vehicle frame number VIN, vehicle characteristics such as the vehicle itself / collision target vehicle, accident causes such as collision, and damage assessment methods such as repair damage assessment methods. The vehicle loss items may include damage part information related to the damaged parts and repair process information of the damaged parts.

[0038] In addition, the system obtaining the vehicle accident loss evaluation report for the current accident of the vehicle includes at least two scenarios. In Scenario 1, the user may obtain vehicle accident loss evaluation information by manually inputting or typing in the information required by the system on a keyboard. In Scenario 2, a third party (such as a repair shop) may already have completed the vehicle accident loss evaluation report and obtain the vehicle accident loss evaluation report by accessing the system. Therefore, the vehicle accident loss risk control method of this embodiment has high versatility and can be applied not only to the scenario of directly obtaining vehicle accident loss evaluation information by this system, but also to the scenario of obtaining the already established vehicle accident loss information provided by a third party and performing risk assessment calculations.

[0039] In step S102, based on a pre-constructed risk algorithm model, the system can calculate the vehicle loss items in the vehicle accident loss evaluation report in combination with the vehicle information, and identify the risk items where risks exist in the vehicle loss items. The risk items mentioned here refer to the items that are not applicable to the current vehicle accident loss or are judged to be not properly evaluated.

[0040] In step S103, the system can block the risk items or present them to the user. More specifically, the system can generate a risk control report for the user to preview. The risk control report may include risk item names of the presentation class, rule names (such as risk causes), rule classifications (such as fraud prevention events, loss logics), operation suggestions, deductible amounts, remarks, and so on.

[0041] According to the vehicle accident loss assessment risk control method of this embodiment in steps S101 - S103, by using a pre - constructed risk algorithm model to perform intelligent judgment on the input or the vehicle accident loss items to be reported in the initially determined accident loss assessment information of the insurance claim vehicle, risk items with risks are calculated. For example, direct blocking can be performed, or, for example, a risk control report can be generated to present these risk items to the user for risk presentation. The user can perform corresponding operations based on the risk items presented by the system, reducing the vehicle accident loss assessment risk, making the vehicle accident loss assessment result more accurate and reasonable, realizing efficient and intelligent vehicle accident loss assessment risk control, and significantly reducing insurance leakage. And various calculations and judgments are performed by the pre - constructed risk algorithm model, that is, an artificial intelligence method is adopted to perform risk assessment by machine, realizing machine review for vehicle accident loss assessment. Compared with the conventional manual review that depends on the professional technical level of employees, it greatly reduces labor costs, saves time, and improves accuracy and objectivity.

[0042] According to an embodiment of the present invention, the pre - constructed risk algorithm model in step S102 includes a vehicle collision location algorithm model for calculating the vehicle collision location related to the current accident of the vehicle and identifying the risk items based on the calculation result, a vehicle collision location damage degree algorithm model for calculating the damage degree of the vehicle collision location and identifying the risk items based on the calculation result, a vehicle component damage - related density model for identifying the risk items where risks exist in the vehicle loss items based on the damage - related density between each vehicle component, a vehicle original manufacturer standard equipment information model for identifying whether the damage part information of the damaged part matches the original equipment information of the vehicle component manufacturer and the original manufacturer standard information of the vehicle component to identify the risk items, and a vehicle component standard repair process model for calculating whether the repair information of the damaged part matches the repair technology data and the repair process standard to identify the risk items.

[0043] Note that although it was exemplified above that the risk algorithm model includes four models: the vehicle collision location algorithm model, the vehicle part damage related density model, the vehicle original manufacturer standard equipment information model, and the vehicle part standard repair process model, it is not limited thereto. For example, the risk algorithm model may include at least one of the above.

[0044] The vehicle collision location algorithm model of this embodiment can be preliminarily established by: based on the vehicle information of the vehicle, virtualizing the vehicle as a corresponding figure in a three-dimensional coordinate system, and assigning three-dimensional coordinates to vehicle parts based on the actual mounting positions of the vehicle parts in the vehicle (corresponding to S2011 in FIG. 3); and dividing the vehicle into different collision locations, and establishing the correspondence between the collision locations and the vehicle parts by creating three-dimensional coordinate data for each of the collision locations based on the vehicle information (corresponding to S2012 in FIG. 3).

[0045] More specifically, create three-dimensional coordinate gyro data of the vehicle part mounting positions. According to different vehicle body types of the vehicle (a total of 6 types such as 3-box 4-door, 3-box 2-door, 2-box 5-door, SUV, MPV, and wagon), virtualize the vehicle as a corresponding three-dimensional figure such as a rectangle, and establish a three-dimensional coordinate system for the part mounting positions. For example, if a passenger car belongs to a 3-box 4-door vehicle body, a three-dimensional coordinate system can be established such that the X-axis (from the left side to the right side of the vehicle) coordinates = (1, 2, 3, 4, 5, 6, 7, 8, 9), the Y-axis (from the front to the back of the vehicle) coordinates = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), and the Z-axis (from the bottom to the top of the vehicle) coordinates = (1, 2, 3). Determine the three-dimensional coordinates of the part mounting positions according to the actual mounting positions of the parts in the vehicle. For example, the three-dimensional coordinates of the "headlight assembly (left)" of a passenger car can be (1, 1, 2), (2, 1, 2).

[0046] Also, create a vehicle collision site and define a site three-dimensional coordinate area. The exterior appearance of the vehicle body is divided into 10 collision sites: "frontal collision, front left-side collision, front right-side collision, middle left-side collision, middle right-side collision, rear collision, rear left-side collision, rear right-side collision, roof part collision, floor part collision". Create three-dimensional coordinate data for each collision site area according to the vehicle body type. Thereby, based on the vehicle body type analyzed with different vehicle frame numbers and the actual mounting position coordinates of the parts, the part names included in each collision site can be calculated.

[0047] The vehicle collision damage degree algorithm model may be obtained by performing computer machine learning using past vehicle accident loss evaluation reports of various types of vehicles as sample data to establish the correspondence between the damage degree of the collision site and the accident loss items. More specifically, for example, it is established in the steps shown in FIG. 3. Referring to FIG. 3, in step S3011, based on the past vehicle accident loss evaluation report, in combination with the loss photos of the past vehicle accident loss evaluation report, manually mark the collision site and the damage degree for the past vehicle accident cases. In step S3012, based on the collision site and the damage degree manually marked for the past vehicle accident cases, the computer performs machine learning on the past vehicle accident loss evaluation reports, the collision sites, and the images of the damage degree of various types of vehicles to obtain the correspondence between the collision site, the damage degree, and the vehicle loss items.

[0048] In this embodiment, the degree of vehicle collision damage can be divided into five categories: minor collision damage, mild collision damage, moderate collision damage, severe collision damage, and extremely severe collision damage. For example, as described above, based on the loss photos of past vehicle accident loss evaluations, for the past vehicle accident loss evaluation reports, the collision location and the degree of collision damage are manually marked. The computer learns the correspondence between the collision location, the degree of damage, and the accident damage items by machine learning the loss evaluation reports of various types of vehicles and the images of various collision locations and degrees of collision damage, and can construct a vehicle collision damage degree algorithm model. For example, in one case shown in FIG. 4 below, based on the vehicle collision location algorithm model, if the loss evaluation report includes damaged parts such as the headlight assembly (right), the front bumper skin, and the front fender panel (right), the computer can accurately identify based on the correspondence table that the accident belongs to the form of "front right collision, mild collision damage" due to these damaged parts. Also, the probability of no loss items occurring can be calculated simultaneously according to different vehicle models (for example, the 2022 model A6L of FAW Audi), different vehicle bodies (for example, 3-box 4-door), different collision locations, and different degrees of collision damage.

[0049] Also, after calculating the degree of damage to the collision location of this accident using the vehicle collision location damage degree algorithm model, the parts loss boundary in the case of the collision location and the degree of damage can also be specified. For example, when it is determined to be in the state of a head-on collision and minor collision damage, the loss boundary in this case should be external parts and should not include internal parts, based on big data mining of past cases. If it is determined that there is damage to the engine assembly in the vehicle accident loss evaluation report, it does not conform to the loss boundary. This is because damage to the engine assembly should not be included in a minor head-on collision.

[0050] As above, using the vehicle collision location algorithm model for the vehicle loss items in the vehicle accident loss evaluation report Based on the vehicle loss items in the current vehicle accident loss assessment report of the obtained vehicle, calculate the collision location of the current accident, compare the current vehicle accident loss assessment report of the vehicle with the past vehicle accident loss assessment reports of the own vehicle, calculate whether there is the same collision location or the same damaged parts, and if it is calculated that there is, perform a calculation to identify the same collision location or damaged parts as risk items, If it is calculated that there are multiple collision locations in the current accident of the vehicle, determine whether there is a collision location that does not conform to the collision logic among the multiple collision locations, and if it is calculated that there is, perform a calculation to identify the collision location that does not conform to the collision logic as a risk item. At least one of the calculations can be performed.

[0051] Then, by using the vehicle collision location damage degree algorithm model, calculate the damage degree of the collision location of the current accident based on the vehicle loss items and the calculated collision location in the current vehicle accident loss assessment report of the obtained vehicle, thereby specifying the part loss boundary in the case of the collision location and the damage degree, determine whether there are damaged parts outside the part loss boundary in the damaged parts, and if it is determined that there are, the damaged parts outside the part loss boundary can be identified as risk items.

[0052] Note that the collision locations that do not conform to the collision logic among the multiple collision locations described above are, for example, locations where the collision locations are not adjacent or are far apart. For example, the collision locations calculated for a vehicle accident using the vehicle collision location algorithm model are a frontal collision, a left-frontal collision, and a rear collision. In this case, a rear collision that is not adjacent or is far apart is a collision location that does not conform to the collision logic.

[0053] Regarding the vehicle component damage-related density model shown in FIG. 2, by using the records in the past vehicle accident loss evaluation reports of various types of vehicles as sample data and performing big data mining analysis, calculating and storing the damage-related density between each vehicle component at the time of vehicle accident damage, the vehicle component damage-related density model can be constructed. Here, the damage-related density includes the probability that when one vehicle component is damaged and becomes a damaged component, the other vehicle component also becomes a damaged component accordingly.

[0054] Thereby, based on the past vehicle accident loss evaluation reports, big data mining calculations are performed on the related density in the accident damage of components to obtain the relevance of accident damage occurring in the AB2 components of the same brand vehicle model. Such relevance includes Confidence (reliability) indicating the probability that when an accident damage occurs in component A, an accident damage occurs in component B simultaneously, Lift (improvement degree) indicating the independence of the damage occurring between the AB2 components, Conviction (certainty degree) indicating the probability of prediction error and also indicating the independence of the damage occurring between the AB2 components. Therefore, based on this vehicle component damage-related density model, risk identification can be performed on the loss items in the vehicle accident loss evaluation report. For example, when it is calculated that there is a strong relevance between the AB2 components, if it is recognized that the report includes damage to component A but does not include damage to component B, then component A is determined as a risk item.

[0055] A strong relevance between vehicle components means that when one vehicle component is damaged and becomes a damaged component, the probability that the other vehicle component also becomes a damaged component accordingly is higher than a predetermined threshold value (for example, 90%).

[0056] According to the vehicle accident loss evaluation risk control method of this embodiment, based on the past real data, the vehicle collision site damage degree algorithm model and the vehicle component damage-related density model can be accurately, reliably and intelligently constructed and improved to perform risk identification and judgment.

[0057] The explanations for the vehicle original manufacturer standard equipment information model and the vehicle part standard repair process model shown in FIG. 2 are as follows.

[0058] The vehicle original manufacturer standard equipment information model is constructed, for example, by standardizing the vehicle original manufacturer equipment information, and is related to the vehicle part original manufacturer equipment information and the vehicle part original manufacturer standard information. The vehicle part original manufacturer equipment information and the vehicle part original manufacturer standard information include, for example, big data such as vehicle original manufacturer equipment data, part codes, part single vehicle usage amounts, the relationship between part assemblies and parts, part guiding prices, and the usage amounts of auxiliary materials such as engine oil.

[0059] At the time of vehicle shipment, each vehicle has the identity of a unique vehicle frame number. Each vehicle has an original manufacturer equipment standard based on the vehicle frame number, and has different functional equipment standards, for example, the headlight has different functional equipment standards such as halogen, xenon, and those with an adaptive adjustment function. When the vehicle type equipment is different, the part prices vary greatly. The system can accurately analyze the vehicle frame number, identify the functional equipment standard of the vehicle, and identify the corresponding equipment standard for the parts in the evaluation loss report, make a risk identification judgment for both, and make a risk cut-off by making an identification judgment for the risk of not conforming to the original manufacturer's equipment standard.

[0060] Based on the identity of the vehicle frame number, there are standards for the single-vehicle usage of parts in a vehicle, the relationship between assemblies and parts, etc. By comparing and identifying the damaged parts in the vehicle accident loss assessment report and determining whether there is a situation where the single-vehicle usage of a part exceeds the standard and the assembly and the part are damaged simultaneously, risk interception is carried out. For example, for the "Headlight Assembly (Left)", if the single-vehicle usage of the original manufacturer of the part is "1" and it is "2" in the assessment report, it exceeds the standard. Since the part of the "Headlight Assembly (Left)" is an assembly and already includes the part "Headlight Housing (Left)", if the losses of the "Headlight Assembly (Left)" and the "Headlight Housing (Left)" occur simultaneously in the assessment report, risk interception is carried out.

[0061] The standard repair process model for vehicle parts is constructed, for example, by standardizing the repair technical data and repair process standards of vehicle parts into big data. There are standard repair processes for vehicle parts. For example, for the "Front Bumper Skin", it cannot be repaired simultaneously by the "Replacement" method and the "Repair" method, and replacement and repair are two mutually exclusive repair processes. Therefore, based on the standard repair process model for vehicle parts, it is evaluated whether there are situations where the repair processes in the report are mutually exclusive, etc. If they exist, risk interception is carried out.

[0062] FIG. 4 is an exemplary diagram showing the operation rules of risk items calculated by the corresponding vehicle collision part algorithm model, vehicle collision part damage degree algorithm model, vehicle part damage related density model, vehicle original manufacturer standard equipment information model, and vehicle part standard repair process model. As shown in the figure, in this embodiment, the risk items are classified into a cutoff risk rule type and a presentation risk rule type whose risk level is lower than the cutoff risk rule type. Generally (non-limitingly), the risk items calculated by the vehicle part standard repair process model and the vehicle original manufacturer standard equipment information model are classified into the cutoff risk rule type and are forcibly cut off, and the risk items corresponding to the cutoff risk rule type are deleted or corrected from the vehicle accident loss evaluation information. The risk items calculated by the vehicle collision part algorithm model, the vehicle collision part damage degree algorithm model, and the vehicle part damage related density model are classified into the presentation risk rule type and are cut off or passed based on the subsequent operations of the user.

[0063] As shown in FIG. 4, the cutoff risk rule type is mainly in items 1-9, with a particularly high risk level and sufficient to be recognized as an evaluation risk. The presentation risk rule type is mainly in items 10-14, with a risk level smaller than the cutoff risk rule type, belonging to a highly suspected risk, and the evaluator needs to verify the authenticity and accuracy of the loss again and explain the sufficient reasons for the evaluation. Also, in the rightmost column in FIG. 5, the basis of the corresponding risk rule is also interpreted in association. These explanations in FIG. 4 may all be displayed in the risk control report in association, for example.

[0064] According to the vehicle accident damage evaluation risk control method implemented above, risk items are assigned risk levels and controlled in different ways. It can reduce the operation burden of the user and efficiently and directly filter high-risk items rather than having the user perform post-processing on all risk items collectively.

[0065] According to the vehicle accident loss assessment risk control method of the first aspect of the present invention, the pre-constructed risk algorithm model that serves as the basis for risk control calculation includes at least five models, namely, the vehicle collision location algorithm model, the vehicle collision location damage degree algorithm model, the vehicle part damage related density model, the vehicle original manufacturer standard equipment information model, and the vehicle part standard repair process model. By comprehensively considering the four aspects of the collision location and damage degree of a vehicle accident, the accident damage related density between parts, the original manufacturer's standard configuration, and the repair process, and based on the data of past real cases, machine learning is performed based on the content of the vehicle loss assessment report, and the vehicle collision location and damage degree can be identified. Through the data mining algorithm, the probability of an event where no part damage occurs can be calculated for different vehicle models of different brands, different collision locations, and different collision damage degrees. By standardizing big data for vehicle original manufacturer equipment data, part data, repair technology data, repair process standards, etc., a set of big data rules for loss assessment risk identification and judgment can be formed, so that comprehensive, reliable, accurate, and intelligent risk control can be carried out, and the risk control ability can be improved.

[0066] Moreover, by using the vehicle accident loss assessment risk control method of the first aspect of the present invention, an efficient, intelligent, and convenient vehicle accident loss risk control tool is provided for the industry. It breaks through the conventional vehicle accident loss risk control modes and concepts, innovates technologies, transforms from conventional empirical rules to big data mining analysis, and from manual experience review to equipment review, greatly improving the review efficiency, saving the insurance industry's claim costs, and avoiding the waste of social wealth. Based on AIA artificial intelligence, industry big data standardization, and big data mining analysis, a complete risk control system is formed to solve the pain points of the industry.

[0067] Also, as shown in FIG. 5, in the vehicle accident loss assessment risk control method of this embodiment, after step S103, the user selects whether to perform risk modification based on the description of each risk item in the risk control report. If it is selected to perform risk modification, risk identification and judgment are performed again based on the risk algorithm model, calibration and cyclic control can be realized for the risk items, and the operability and accuracy are improved.

[0068] The basic concept of the vehicle accident loss assessment risk control method according to the embodiment of the present invention has been described above. Hereinafter, for the sake of easy understanding, examples will be given and described more clearly with reference to FIGS. 6-7. However, those skilled in the art should understand that this example is not limiting and is merely an example.

[0069] First, the user can request the creation of a vehicle accident loss assessment report within the system. Creating a vehicle accident loss assessment report is, for example, a process of inputting vehicle basic information and generating a loss assessment task. In this embodiment, the user (assessor) uses a mobile device such as a mobile phone to access the APP top page, or a desktop computer accesses the web page top page, and inputs the license plate number and vehicle frame number by OCR (optical character recognition) or manually. Then, the system performs vehicle type analysis on the frame number, recognizes the vehicle identity, and obtains vehicle information. After that, the user selects a repair enterprise (such as a 4S store or a repair factory), selects and supplements the assessment price standard information, and creates an initial vehicle accident loss assessment report (S1).

[0070] After the system receives a request to create a vehicle accident loss evaluation report, it pops up an input page for the user. On this page, the user can input vehicle loss items (S2), such as the name of damaged parts, usage quantity, repair process (replacement, removal and installation, painting, repair, low carbon, etc.). For example, in this embodiment, when the right headlamp air cleaner, front bumper replacement, and right front fender painting are input into the input box, through the intelligent semantic analysis and identification of the system, it automatically clicks on the headlamp assembly (right) [part] [replacement], air cleaner assembly [part] [replacement], front bumper skin [part] [replacement], front fender (right) [painting], thereby determining the vehicle loss items. At the same time, the system automatically brings out the original part manufacturer code, single vehicle usage quantity, remarks, manufacturer's guiding price, and system reference price.

[0071] After that, the appraiser refers to the reference price of the loss items presented by the system, re-determines the loss amount one by one, and finally determines the vehicle loss items and the total loss amount to generate a complete vehicle accident loss evaluation report (S3).

[0072] At this time, the system obtains the vehicle accident loss evaluation report. Based on the vehicle information and damaged part information in it, it uses the vehicle collision site algorithm model, vehicle collision site damage degree algorithm model, vehicle part damage related density model, vehicle original manufacturer standard equipment information model, and vehicle part standard repair process model to perform corresponding loss evaluation risk identification calculations, makes risk identification judgments on the content of the vehicle accident loss evaluation report based on these risk algorithm models, and calculates the risk items, operation suggestions, and loss reduction amounts (S4).

[0073] The system automatically generates a risk report after calculation (S5). The report includes a risk item name, risk description, rule type, operation proposal, reducible amount, and remarks (see Figure 6). For risks of the cut-off risk rule type, the system can perform forced cut-off at the input stage of loss items. For risks of the presentation risk rule type, the appraiser should re-review and confirm, correct incorrect evaluations, or explain sufficient reasons for the evaluations based on the description in the risk assessment report (S6).

[0074] In the technical solution of the present invention, risk control can be automatically performed using artificial intelligence. One of the core functions of artificial intelligence is to model the damage forms of vehicle collisions, perform machine learning with a large amount of historical data, conduct big data mining analysis, and realize the relevant algorithm between the vehicle collision damage model and the loss details. Specifically, machine learning is performed based on the content of the vehicle accident loss assessment report to identify the vehicle collision location and damage degree. Through the data mining algorithm, the probability of an event without component damage is calculated for different brand vehicle models, different collision locations, and different collision damage degrees. Also, big data standardization is performed on vehicle original manufacturer equipment data, component data, repair technology data, repair process standards, etc. to form the big data rules for loss assessment risk identification and judgment.

[0075] Using the vehicle accident loss assessment risk control method of the present invention, an efficient, intelligent, and convenient vehicle accident loss risk control tool is provided. It breaks through the conventional modes and concepts of vehicle accident loss risk control, innovates from conventional empirical rules to big data mining analysis, converts from manual experience review to equipment review, greatly improves the review efficiency, saves the insurance industry's claim costs, and avoids the waste of social wealth. Based on AIA artificial intelligence, industry big data standardization, and big data mining analysis, a complete set of risk control systems is formed to solve the pain points of the industry.

[0076] Embodiments of the present invention further provide a vehicle accident loss assessment risk control device. Specifically, as shown in FIG. 8, the vehicle accident loss assessment risk control device 200 of this embodiment includes an acquisition unit 201 for acquiring a vehicle accident loss assessment report including vehicle information of the vehicle in this accident and vehicle loss items related to this accident of the vehicle, a risk identification and judgment unit 202 for calculating the vehicle loss items in the vehicle accident loss assessment report by combining the vehicle information based on a pre-constructed risk algorithm model, and identifying risk items that are items determined not to correspond to this vehicle accident loss or items determined to have inappropriate evaluations where there is a risk in the vehicle loss items, and a risk processing unit 203 for blocking or presenting the risk items to the user.

[0077] The vehicle accident loss assessment risk control device 200 of the embodiments of the present invention can achieve the same technical effects as the vehicle accident loss assessment risk control method in the first aspect. Although not described here, the vehicle accident injury assessment risk control device 200 can also execute various processes of the vehicle accident injury assessment risk control method in the first aspect.

[0078] FIG. 9 shows an exemplary system architecture 300 of a vehicle accident loss assessment risk control method or a vehicle accident loss assessment risk control device to which embodiments of the present invention can be applied.

[0079] As shown in FIG. 9, the system architecture 300 may include terminal devices 301, 302, 303, a network 304, and a server 305. The network 304 is used to provide a medium for communication links between the terminal devices 301, 302, 303 and the server 305. The network 304 may include various connection types such as wired, wireless communication links, or optical fiber cables.

[0080] Users can use terminal devices 301, 302, and 303 to access server 305 via network 304 and receive or send messages, etc. Various communication client applications such as, for example, vehicle loss assessment applications, web browser applications, search applications, instant messaging tools (merely examples) may be installed on terminal devices 301, 302, and 303.

[0081] Terminal devices 301, 302, and 303 may be various electronic devices having a display and capable of browsing web pages, including but not limited to smartphones, tablet computers, laptop computers, desktop computers, etc.

[0082] Server 305 may be a server that provides various services, for example, a background management server (merely an example) that provides data support for the frame numbers input by users using terminal devices 301, 302, and 303. The background management server can perform processes such as analysis on the received data such as product information query requests, and feedback the processing results (for example, vehicle brand, model information - merely an example) to the terminal devices.

[0083] Note that the vehicle accident loss assessment risk control method according to the embodiments of the present invention is generally executed by server 305, and accordingly, the vehicle accident loss assessment risk control device is generally provided in server 305.

[0084] It should be understood that the numbers of terminal devices, network, and server in FIG. 9 are merely examples. Any number of terminal devices, network, and server may be provided as needed.

[0085] Hereinafter, with reference to FIG. 10, a structural schematic diagram of a computer system 400 suitable for a terminal device for realizing an embodiment of the present invention will be described. The terminal device shown in FIG. 10 is merely an example and does not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0086] As shown in FIG. 10, the computer system 400 includes a central processing unit (CPU) 401 that can execute various appropriate operations and processes according to a program stored in a ROM (Read Only Memory) 402 or a program loaded from a storage unit 408 into a RAM (Random Access Memory) 403. The RAM 403 also stores various programs and data necessary for the operation of the system 400. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0087] Connected to the I / O interface 405 are an input unit 406 including a keyboard, a mouse, etc., an output unit 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc., a storage unit 408 including a hard disk, etc., and a communication unit 409 including a network interface card such as a LAN card, a modem, etc. The communication unit 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 410 as needed, and the computer program read therefrom is installed in the storage unit 408 as needed.

[0088] In particular, according to the embodiments of the disclosure of the present invention, the process described above with reference to the flowchart may be implemented as a computer software program. For example, the embodiments of the disclosure of the present invention include a computer program product including a computer program carried on a computer-readable medium, and the computer program product includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program is downloaded and installed from the network via the communication unit 409 and / or installed from the removable medium 411. When this computer program is executed by the central processing unit (CPU) 401, the above functions defined in the system of the present invention are executed.

[0089] Note that the computer-readable medium shown in the present invention may be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to, an electrical connection having one or more conductors, a portable computer magnetic disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that includes or records a program, and the program may be used in or in combination with a command execution system, apparatus, or device. In the present invention, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal may take various forms including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer-readable signal medium may be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium can transmit, propagate, or transmit a program used in or in combination with a command execution system, apparatus, or device. The program code included in the computer-readable medium can be transmitted by any suitable medium including, but not limited to, wireless, wired, optical fiber cable, RF, or any suitable combination of the above.

[0090] Flowcharts and block diagrams in the drawings illustrate the possible system architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. From this perspective, each block in a flowchart or block diagram can represent a module, block, or portion of code that includes one or more executable instructions for implementing a given logical function. Additionally, as an alternative, the functions described in a block may occur in an order different from that shown in the drawings. For example, two consecutively shown blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, as determined by the functions involved. Each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented by a system of dedicated hardware for performing a given function or operation, or may be implemented by a combination of dedicated hardware and computer instructions.

[0091] The units described in the embodiments of the present invention may be implemented by software or by hardware. The described units may be installed in a processor, and for example, may be described as a processor including an acquisition unit, a risk identification and determination unit, and a risk processing unit. Here, the names of these units do not necessarily limit the units themselves in some cases. For example, the risk identification and determination unit may be described as "a unit that requests a connected server to perform risk identification and determination on vehicle accident loss assessment information".

[0092] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiments, or may exist independently without being attached to the device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by one such device, the device obtains a vehicle accident loss evaluation report including the vehicle information of the vehicle and the vehicle loss item information regarding the current accident of the vehicle, combines the vehicle information based on a pre-constructed risk algorithm model, calculates it against the vehicle loss item information in the vehicle accident loss evaluation report, and identifies a risk item that is an item determined not to correspond to the current vehicle accident loss or an item determined to be inappropriate in the vehicle accident loss evaluation report, where there is a risk in the vehicle loss item of the vehicle accident loss evaluation report, and includes blocking the risk item or generating a risk control report for the user to preview.

[0093] The present invention breaks through the conventional modes and concepts of vehicle accident loss risk control, innovates big data mining analysis based on conventional rules of thumb, provides an efficient, intelligent and convenient vehicle accident loss risk control technology, saves the claim costs in the insurance industry, and avoids social property waste.

[0094] The above specific embodiments do not limit the protection scope of the present invention. Those skilled in the art should understand that various changes, combinations, sub-combinations, and substitutions are possible according to design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should all be included within the protection scope of the present invention.

Claims

1. 1. A method performed by a processor, comprising: Obtaining a vehicle accident loss assessment report for a current accident of a vehicle, the report including vehicle information of the vehicle and vehicle loss items related to the current accident of the vehicle; Combining the vehicle information based on a pre-constructed risk algorithm model to calculate the vehicle loss item in the vehicle accident loss assessment report, and identifying risk items that exist in the vehicle loss item; and blocking or presenting the risk item to a user. The pre-constructed risk algorithm model comprises: According to past vehicle accident damage assessment reports of various types of vehicles, by combining with damage photos of past vehicle accident damage assessment, manually marking the collision location and damage degree for past vehicle accident cases; A computer learns images of past vehicle accident loss assessment reports and collision locations and damage levels of various types of vehicles based on the manually marked collision locations and damage levels of the past vehicle accident cases, thereby obtaining a correspondence relationship between the collision locations and damage levels and vehicle loss items. Based on the vehicle collision part damage degree algorithm model, the calculation for the vehicle loss item in the vehicle accident loss evaluation report is calculating a damage level of the collision part of the current accident based on the vehicle loss items in the current vehicle accident loss assessment report of the acquired vehicle and the collision part calculated by the vehicle collision part algorithm model included in the risk algorithm model, thereby identifying a part loss boundary for the collision part and the damage level, determining whether or not the damaged parts include a damaged part outside the part loss boundary, and if it is determined that the damaged parts include a damaged part outside the part loss boundary, identifying the damaged part outside the part loss boundary as a risk item; A vehicle accident loss assessment risk control method comprising:

2. The vehicle impact site algorithm model included in the risk algorithm model constructed in advance is a step of imagining a vehicle as a corresponding figure in a three-dimensional coordinate system based on vehicle information of the vehicle, and assigning three-dimensional coordinates to the vehicle parts based on actual mounting positions of the vehicle parts on the vehicle; and establishing a correspondence between the collision locations and the vehicle parts by dividing the vehicle into different collision locations and generating three-dimensional coordinate data for each of the collision locations based on the vehicle information.

2. The vehicle accident loss assessment risk control method according to claim 1,

3. The calculation for the vehicle loss item in the vehicle accident loss assessment report based on the vehicle collision part algorithm model is Calculating the collision part of the current accident based on the vehicle loss items in the acquired current vehicle accident loss evaluation report of the vehicle, comparing the current vehicle accident loss evaluation report of the vehicle with the past vehicle accident loss evaluation report of the vehicle, calculating whether the same collision part or the same damaged part exists, and if it is determined that they exist, identifying the same collision part or the same damaged part as a risk item; When it is calculated that there are multiple collision parts in the current vehicle accident, it is determined whether there is a collision part that does not conform to the collision logic among the multiple collision parts, and when it is calculated that there is a collision part, it is determined that there is a collision part that does not conform to the collision logic as a risk item.

3. The vehicle accident loss assessment risk control method according to claim 2,

4. The pre-constructed risk algorithm model comprises: The vehicle part damage association density model is constructed by performing a big data mining analysis using past vehicle accident loss evaluation reports of various types of vehicles as sample data, and calculating and storing a damage association density between each vehicle part when the vehicle is damaged in an accident; The damage-associated density includes at least one of a probability that when one vehicle part is damaged and becomes a damaged part, the other vehicle part also becomes a damaged part accordingly, and a probability that when one vehicle part is damaged and becomes a damaged part, the other vehicle part does not become a damaged part.

2. The vehicle accident loss assessment risk control method according to claim 1,

5. The calculation for the vehicle loss item in the vehicle accident loss assessment report based on the vehicle part damage-related density model is Based on the vehicle loss item in the current vehicle accident loss assessment report of the acquired vehicle, in combination with the damage-related density, it is determined whether or not the other part having a strong correlation with one of the damaged parts included in the vehicle loss item is also recorded as a damaged part in the vehicle loss item, and if it is determined that the other part is not recorded, the calculation includes determining whether or not the one of the damaged parts is a risk item; The strong association means that when one vehicle part is damaged and becomes a damaged part, the probability that the other vehicle part also becomes a damaged part correspondingly is higher than a predetermined threshold value.

5. The vehicle accident loss assessment risk control method according to claim 4,

6. The pre-constructed risk algorithm model includes a vehicle original manufacturer standard equipment information model constructed by performing big data standardization on vehicle original manufacturer equipment information; The vehicle original manufacturer equipment information includes at least one of the following: vehicle original manufacturer equipment data, part code, single vehicle usage of parts, relationship between parts assemblies and parts, guide price of parts, and usage of auxiliary materials; 2. The vehicle accident loss assessment risk control method according to claim 1,

7. The calculation for the vehicle loss item in the vehicle accident loss evaluation report based on the vehicle original manufacturer standard equipment information model is as follows: Based on the vehicle information, the vehicle original manufacturer standard equipment information model is used to identify vehicle original manufacturer equipment information corresponding to the vehicle, and information on damaged parts in the vehicle loss item is compared with the vehicle original manufacturer equipment information. If the comparison results of the two are not consistent, the calculation is included in which the damaged parts that do not match are identified as risk items.

7. The vehicle accident loss assessment risk control method according to claim 6,

8. The pre-constructed risk algorithm model comprises: Including vehicle part repair technology data, vehicle part standard repair process model built by big data standardization of repair process standard; 2. The vehicle accident loss assessment risk control method according to claim 1,

9. The calculation for the vehicle loss item in the vehicle accident loss evaluation report based on the vehicle part standard repair process model is According to the vehicle information, the vehicle part standard repair process model is used to compare the repair information of the damaged parts in the vehicle loss item with the repair technology data and the repair process standard, and if the comparison results of the two are not consistent, the calculation of identifying the inconsistent damaged parts as a risk item is included. The vehicle accident loss assessment risk control method according to claim 8,

10. After presenting the risks to the user, the user selects whether or not to modify the risks based on the explanation of each risk item presented, and if the user selects to modify the risks, a risk recognition judgment is made again based on the risk algorithm model; 2. The vehicle accident loss assessment risk control method according to claim 1,

11. An acquisition unit for acquiring a vehicle accident loss assessment report of a current accident of a vehicle, the report including vehicle information of the vehicle and vehicle loss items related to the current accident of the vehicle; a risk identification and judgment unit for combining the vehicle information based on a pre-constructed risk algorithm model to calculate vehicle loss items in the vehicle accident loss evaluation report, and identifying risk items in the vehicle loss items that are determined to have a risk, not to be related to the current vehicle accident loss, or not to be evaluated; a risk processing unit for blocking or presenting the risk items to a user; The pre-constructed risk algorithm model comprises: Manually marking the collision location and damage degree for the past vehicle accident cases based on the past vehicle accident damage assessment reports of various types of vehicles, in combination with the damage photos of the past vehicle accident damage assessment; A computer learns images of past vehicle accident loss assessment reports and collision sites and damage levels of various types of vehicles based on the manually marked collision sites and damage levels of the past vehicle accident cases, thereby obtaining a correspondence relationship between the collision sites and damage levels and vehicle loss items. Based on the vehicle collision part damage degree algorithm model, the calculation for the vehicle loss item in the vehicle accident loss evaluation report is The method includes a step of calculating a damage level of the collision part of the current accident based on the vehicle loss items in the current vehicle accident loss assessment report of the acquired vehicle and the collision part calculated by the vehicle collision part algorithm model included in the risk algorithm model, thereby specifying a part loss boundary for the collision part and the damage level, determining whether or not the damaged parts include a damaged part outside the part loss boundary, and if it is determined that the damaged parts include a damaged part outside the part loss boundary, identifying the damaged part outside the part loss boundary as a risk item. A vehicle accident loss assessment risk control device comprising:

12. one or more processors; and a storage device storing one or more programs, The one or more programs are executed by the one or more processors, thereby causing the one or more processors to implement the method according to any one of claims 1 to 10. An electronic device characterized by:

Images