Intelligent loss assessment processing method, device, equipment and medium

Abstract

The invention relates to the field of artificial intelligence, and discloses an intelligent loss assessment processing method, device and equipment and a medium, and the method comprises the steps: receiving an accident vehicle image uploaded by a vehicle owner; preprocessing the accident vehicle image, and segmenting to obtain a damaged area image; vehicle damage assessment is carried out according to the damaged area image, and corresponding damage assessment information is generated according to an assessment result and a currently obtained damage assessment standard; and obtaining vehicle insurance policy information and historical behavior information of the vehicle owner, and generating corresponding claim settlement processing suggestion information according to the vehicle insurance policy information, the historical behavior information and the loss assessment information. Through receiving the accident vehicle image and performing automatic vehicle damage assessment, the claim settlement loss assessment information is obtained based on the assessment result and the newly obtained loss assessment standard, and a comprehensive suggestion is output for the current claim settlement decision in combination with the insurance policy of the vehicle owner, the claim settlement history and other information, so that the vehicle insurance loss assessment efficiency and flexibility are improved, and the user experience is improved. And the time cost is saved, and meanwhile, the requirements of different customers and vehicle damage conditions are better met.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology and financial technology, and in particular to an intelligent damage assessment processing method, device, equipment and medium. Background Art

[0002] In recent years, the role of cars in people's daily lives has become increasingly important. With the substantial increase in car ownership, driving accidents of varying degrees have occurred, resulting in vehicle damage. Therefore, the auto insurance industry came into being.

[0003] When making a claim for auto insurance, identifying the damage to the vehicle is an important part of the claim. Traditionally, after a vehicle accident is reported by phone or APP, the agency needs to send an external surveyor to conduct an on-site survey and make an appraisal of the vehicle accident. After that, the damage assessor will go to the repair shop to assess the damage and make a damage assessment based on the price of the repair shop's parts. Finally, the customer can get the final damage assessment report. This damage assessment process is not only long-term, inefficient, and labor-intensive, but also uses a unified process for damage assessment and compensation for all vehicle accidents, which is difficult to adapt to changes in different customers and different vehicle damage situations. As a result, the current auto insurance damage assessment still has problems of low efficiency and low flexibility. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide an intelligent damage assessment processing method, device, equipment and medium that can be applied to financial technology or other related fields. Its main purpose is to improve the efficiency and flexibility of motor vehicle insurance damage assessment, save time and cost, and better adapt to the needs of different customers and vehicle damage situations.

[0005] The technical solution of the present invention is as follows:

[0006] A first aspect of the present invention provides an intelligent damage assessment processing method, comprising:

[0007] Receive accident vehicle images uploaded by vehicle owners;

[0008] Preprocessing the accident vehicle image and segmenting it to obtain a damaged area image;

[0009] Performing a vehicle damage assessment based on the damaged area image, and generating corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard;

[0010] The vehicle owner's auto insurance policy information and historical behavior information are obtained, and corresponding claims processing suggestion information is generated based on the auto insurance policy information, historical behavior information and damage assessment information.

[0011] A second aspect of the present invention provides an intelligent damage assessment processing device, comprising:

[0012] An image receiving module is used to receive accident vehicle images uploaded by the vehicle owner;

[0013] A preprocessing module, used for preprocessing the accident vehicle image and segmenting it to obtain a damaged area image;

[0014] A damage assessment module, used to assess vehicle damage based on the damaged area image, and generate corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard;

[0015] The claim settlement suggestion output module is used to obtain the car owner's auto insurance policy information and historical behavior information, and generate corresponding claim settlement processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information.

[0016] A third aspect of the present invention provides a computer device, comprising at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned intelligent damage assessment processing method.

[0019] A fourth aspect of the present invention provides a non-volatile computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the one or more processors can execute the above-mentioned intelligent damage assessment processing method.

[0020] Beneficial effects: The present invention discloses an intelligent damage assessment processing method, device, equipment and medium. Compared with the prior art, the embodiment of the present invention receives the accident vehicle image uploaded by the car owner; pre-processes the accident vehicle image and segments it to obtain the damaged area image; performs vehicle damage assessment based on the damaged area image, and generates corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; obtains the car owner's auto insurance policy information and historical behavior information, and generates corresponding claims processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information. By receiving the accident vehicle image and performing an automated vehicle damage assessment, the claim damage assessment information is obtained based on the assessment result and the latest acquired damage assessment standard, and at the same time, the comprehensive suggestion output for the claim decision of this accident is combined with the car owner's insurance policy and claim history information, so that the efficiency and flexibility of auto insurance damage assessment can be improved, saving time and cost while being more adaptable to the needs of different customers and vehicle damage situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the scheme in the present invention, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A schematic diagram of an application environment of the intelligent damage assessment processing method provided by an embodiment of the present invention;

[0023] Figure 2 A flow chart of the intelligent damage assessment processing method provided by an embodiment of the present invention;

[0024] Figure 3 A flow chart of step S201 in the intelligent damage assessment processing method provided in an embodiment of the present invention;

[0025] Figure 4 A flow chart of step S202 in the intelligent damage assessment processing method provided in an embodiment of the present invention;

[0026] Figure 5 A flow chart of step S203 in the intelligent damage assessment processing method provided in an embodiment of the present invention;

[0027] Figure 6 A flow chart of step S204 in the intelligent damage assessment processing method provided in an embodiment of the present invention;

[0028] Figure 7 A schematic diagram of the functional modules of the intelligent damage assessment processing device provided by an embodiment of the present invention;

[0029] Figure 8 A schematic diagram of the hardware structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The embodiments of the present invention are described below in conjunction with the accompanying drawings.

[0031] The intelligent damage assessment processing method provided by the embodiment of the present invention can be applied in the following aspects: Figure 1 In the application environment of FIG. 1 , it includes a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0032] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only as examples).

[0033] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0034] The server 105 may be a server that provides various services, such as a background server that provides support for the content browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (for example only). The background server may analyze and process the received data such as the user request, and feed back the processing results (such as web pages, information, or data obtained or generated according to the user request) to the terminal device. The server 105 may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server 105 may also be a server for a distributed system, or a server combined with a blockchain.

[0035] It should be noted that the intelligent damage assessment processing method provided in the embodiment of the present application can generally be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103. Accordingly, the intelligent damage assessment processing device provided in the embodiment of the present invention can also be set in the first terminal device 101, the second terminal device 102 or the third terminal device 103. Alternatively, the intelligent damage assessment processing method provided in the embodiment of the present invention can also generally be executed by the server 105. Accordingly, the intelligent damage assessment processing device provided in the embodiment of the present invention can generally be set in the server 105.

[0036] It should be understood that the numbers of the above terminal devices, networks and servers are only illustrative. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0037] like Figure 2 As shown, the intelligent damage assessment processing method provided by the embodiment of the present invention specifically includes the following steps:

[0038] S201, receiving the accident vehicle image uploaded by the vehicle owner.

[0039] In this embodiment, after a traffic accident occurs, the car owner can log in to the mobile application or web application provided by the vehicle insurance company through smart devices such as mobile phones, tablets, laptops, etc., and upload the images of the accident vehicle collected by various devices such as smart phones, driving recorders, and vehicle cameras to the intelligent damage assessment platform. These devices can capture high-resolution images from multiple angles in real time to ensure full coverage of the damaged area. For example, after a traffic accident occurs, the car owner can directly take pictures of the damaged parts of the accident vehicle through a smart phone to ensure that the image is clear and can clearly show the damaged area, log in and upload it to the intelligent damage assessment platform, or export other auxiliary images such as environmental or road conditions from the driving recorder and vehicle camera and upload them to the platform to provide auxiliary information for damage assessment and claims.

[0040] After receiving the accident vehicle image, the intelligent damage assessment platform can also automatically record the upload time, owner information and vehicle information, providing basic data for subsequent damage assessment and claims processing. Directly uploading images through mobile devices simplifies the car owner's car insurance reporting process and improves processing efficiency.

[0041] S202: Preprocess the accident vehicle image and segment it to obtain a damaged area image.

[0042] In this embodiment, after receiving the accident vehicle image uploaded by the vehicle owner, the image is further preprocessed by an image processing algorithm. The specific preprocessing may include adjusting brightness, contrast, removing noise, and region segmentation, so as to improve the image quality while automatically identifying and segmenting the damaged region image from the overall image. Preferably, the segmented damaged region image may be further processed, such as edge detection, morphological operations, etc., to highlight the features of the damaged region. The image quality is improved by preprocessing, and clear image data is provided for subsequent damage assessment. At the same time, the damaged region is quickly and accurately located by automated image segmentation technology, which reduces manual intervention and improves the accuracy and efficiency of damage assessment.

[0043] S203: Perform vehicle damage assessment based on the damaged area image, and generate corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard.

[0044] In this embodiment, after the damaged area image is accurately segmented, the image features of the damaged area image are extracted through a machine learning algorithm, and then the vehicle damage assessment is performed on the accident vehicle. For example, a multi-task learning framework is used to integrate vehicle damage detection, classification and quantitative assessment in a unified architecture, so that the framework can simultaneously process multiple subtasks to achieve multi-dimensional vehicle damage assessment of the damaged area image, thereby obtaining detailed damage assessment results, such as damage categories (such as scratches, dents, cracks), damaged parts (such as body, door, front bumper, etc.) and damage severity (such as minor, moderate, severe, etc.). The current damage assessment standard database is queried to obtain the latest damage assessment standard, that is, the damage assessment standard in this embodiment is dynamically updated according to a certain update strategy, so that the damage assessment standard can be more in line with changes in the vehicle maintenance market. According to the assessment results and the currently acquired damage assessment standards, damage assessment information corresponding to the vehicle damage assessment results is generated. The specific damage assessment information may include, for example, maintenance items, component replacement, estimated maintenance time and cost, etc. The use of machine learning algorithms for damage assessment improves the accuracy and objectivity of the assessment, and ensures the reliability of the generated damage assessment information through dynamically updated damage assessment labels, reduces manual operations, and improves the efficiency and accuracy of damage assessment.

[0045] S204, obtaining the vehicle insurance policy information and historical behavior information of the vehicle owner, and generating corresponding claim settlement suggestion information according to the vehicle insurance policy information, historical behavior information and damage assessment information.

[0046] In this embodiment, after the damage assessment information is generated, in order to adapt to the behavioral characteristics of different car owners and the impact of different car damage situations on the auto insurance policy, etc., the car owner's auto insurance policy details are retrieved from the database through the policy number provided by the car owner, including insurance type, insured amount, insurance period, number of claims, etc., and the car owner's historical behavior information is also obtained, such as past claims records, violation records, etc. Combined with the auto insurance policy information, historical behavior information and damage assessment information, the current claim decision is intelligently and comprehensively evaluated to generate claim processing suggestion information. That is, after the damage assessment information is generated, the car owner's insurance terms, claims history and market data are further combined to evaluate the impact that the claim may have on the car owner's future premiums. For example, small claims may lead to increased premiums in the future, which is not the preferred approach for car owners in the long run. Therefore, the platform can use machine learning algorithms to comprehensively analyze the relationship between damage assessment information, auto insurance policies, and customer historical behavior patterns, and automatically generate current claims advice information, such as applying for claims, paying for repairs yourself, applying for no-claim discounts, etc., and compare suggested plans with plans, etc., to achieve intelligent decision-making optimization for auto insurance damage assessment and claims, which can not only help car owners reduce the risk of future premium increases, but also reduce insurance companies' management costs and claims expenses by reducing the occurrence of small claims.

[0047] For example, the car owner Zhang San had a minor traffic accident and uploaded photos of the damaged vehicle through the mobile phone APP. The intelligent damage assessment platform evaluated the degree of damage to the vehicle based on the damaged photos uploaded by Zhang San, and generated damage assessment information in combination with the damage assessment standards, including repair items and estimated costs. It also automatically extracted Zhang San's auto insurance policy information, including insurance type, insured amount, insurance period, etc., and queried Zhang San's historical behavior information, such as past claims records, violation records, etc. The intelligent damage assessment platform analyzed Zhang San's auto insurance policy information and historical behavior information and found that Zhang San had no claims record in the past year, and the degree of damage in this accident was relatively minor, with an estimated repair cost of 1,000 yuan. Based on this information, the platform generated corresponding claims processing advice information, prompting Zhang San: "Since the damage in this accident was relatively minor and the repair cost was less than 2,000 yuan, considering your good insurance record in the past, it is recommended that you choose to bear the repair costs yourself to avoid affecting the insurance premium rate for the next year. If you choose to claim, we will provide you with a quick claims service." By analyzing the car owner's historical behavior information and auto insurance policy information, we can quickly determine the damage and provide personalized claims advice to the car owner based on different car owners and vehicle damage situations, helping the car owner to decide whether to make a claim, thereby reducing the occurrence of small claims, greatly shortening the claims processing time, and improving claims efficiency.

[0048] In the above embodiment, the present invention discloses an intelligent damage assessment processing method, which receives the accident vehicle image uploaded by the car owner; pre-processes the accident vehicle image and segments it to obtain the damaged area image; performs vehicle damage assessment based on the damaged area image, and generates corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; obtains the car owner's auto insurance policy information and historical behavior information, and generates corresponding claims processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information. By receiving the accident vehicle image and performing an automated vehicle damage assessment, the claims damage assessment information is obtained based on the assessment result and the latest acquired damage assessment standard, and at the same time, the car owner's insurance policy and claims history information are combined to output a comprehensive suggestion for the claims decision of this accident, so that the efficiency and flexibility of auto insurance damage assessment can be improved, saving time and cost while also being more adaptable to the needs of different customers and vehicle damage situations.

[0049] In one embodiment, Figure 3 As shown, step S201 includes:

[0050] S301, in response to a vehicle accident request initiated by a vehicle owner, calling a camera of a device currently logged in by the vehicle owner to collect images of the accident vehicle from multiple angles;

[0051] S302, analyzing the real-time shooting picture of the camera, and superimposing corresponding shooting guidance information on the real-time shooting picture according to the picture analysis result, so as to guide the car owner to adjust the acquisition parameters of the camera;

[0052] S303: Receive images of the accident vehicle at different angles acquired by the camera under corresponding acquisition parameters.

[0053] In this embodiment, after a traffic accident occurs, the car owner can log in to the insurance company's mobile application through a device such as a smart phone to initiate a vehicle claim request. At this time, the application responds to the vehicle claim request and requests to call the camera of the car owner's currently logged-in device to collect images of the accident vehicle from multiple angles. For example, the car owner is prompted to walk around the accident vehicle and take pictures of the front, rear, left, right sides and collision point of the vehicle to ensure that the damage to the vehicle is captured from different angles. Preferably, automatic environmental perception and adjustment can also be achieved through environmental sensors, etc., and the shooting parameters can be adjusted by automatically identifying the current environmental data changes, such as automatically enhancing the exposure in low-light environments or adjusting the contrast in strong light, to ensure that the collected images are clear and usable under various conditions.

[0054] After the camera is called, the real-time shooting screen of the camera is analyzed to automatically identify whether the current shooting angle, lighting conditions, focal length and other parameters are appropriate, and provide corresponding prompts. The corresponding shooting guidance information is superimposed on the real-time shooting screen to guide the car owner to adjust the camera's acquisition parameters such as angle and distance. For example, if the current shooting angle is not enough to clearly show the damaged part, a guidance message will be displayed on the screen, such as "Please move the phone to the right side of the vehicle for a clearer view", prompting the car owner to adjust the shooting angle and distance according to the instructions of the superimposed shooting guidance information until it is confirmed that the image quality meets the requirements. Real-time analysis and guidance ensure that the car owner can take high-quality images as required. These images can clearly show the damaged area, so that the car owner can take photos that meet the requirements without professional knowledge, improving the efficiency of damage assessment and claims.

[0055] The images of the accident vehicle collected at different angles under the corresponding acquisition parameters are uploaded to the intelligent damage assessment platform for subsequent intelligent damage assessment processing, which provides the necessary visual data for damage assessment and enables the car owner to immediately initiate a claim through his or her mobile device without waiting for the insurance company's on-site response, thereby speeding up the accident handling process.

[0056] In one embodiment, Figure 4 As shown, step S202 includes:

[0057] S401, performing denoising processing on the accident vehicle image by using a pre-trained denoising model, identifying and eliminating a specified type of noise in the accident vehicle image, and obtaining a vehicle denoised image;

[0058] S402, performing histogram equalization processing and white balance adjustment processing on the denoised vehicle image to obtain a vehicle balanced image;

[0059] S403: input the vehicle balance image into a trained semantic segmentation model, identify and classify the intact area and the damaged area of ​​the vehicle balance image, and obtain a corresponding damaged area image according to the identification result.

[0060] In this embodiment, when the accident vehicle image is preprocessed and the region is segmented, the accident vehicle image is first denoised by a pre-trained denoising model, the specified type of noise in the accident vehicle image is identified and eliminated, and a vehicle denoised image is obtained, thereby restoring the original clarity of the image. For example, the accident vehicle image uploaded by the owner contains Gaussian noise and salt and pepper noise introduced by factors such as sensor noise and environmental interference. At this time, a pre-trained CNN denoising model, such as DnCNN, is used for denoising. The model is trained to identify and remove multiple types of noise. The uploaded image is input into the denoising model. The model automatically extracts features and identifies noise through its multi-layer convolutional network, and outputs a vehicle denoised image. The noise in these images is effectively reduced, while retaining the detailed information of the damaged area of ​​the vehicle, making the details of the damaged area more clearly visible, which helps to improve the accuracy of damage assessment.

[0061] At the same time, since the denoised image may be partially too dark or too bright due to uneven illumination or shooting under different illumination conditions, the denoised vehicle image is also processed by histogram equalization and white balance adjustment to obtain a balanced vehicle image. The histogram equalization process is specifically contrast-limited adaptive histogram equalization (CLAHE). CLAHE prevents overexposure of the image by limiting the enhancement of local contrast and improves the visibility of damaged parts. Compared with traditional histogram equalization, CLAHE can distribute image brightness more evenly, which is particularly suitable for processing vehicle damage pictures with uneven local illumination; the white balance adjustment process is specifically through the gray world assumption (Gray World Assumption) and the white balance algorithm based on color gain, which can automatically adjust the color temperature of the image, correct the color deviation caused by the ambient light source, and make the image color closer to the real effect under natural light conditions. The histogram equalization process significantly improves the contrast of the image, making the boundaries and details of the damaged area more obvious, while the white balance adjustment process corrects the color shift of the image, making the color more realistic and consistent, which helps to more accurately assess vehicle damage.

[0062] The vehicle balance image is input into a trained semantic segmentation model based on deep learning (such as U-Net or DeepLab). The model can classify different objects in the image (such as the vehicle body, tire, and background) at the pixel level, thereby accurately segmenting the vehicle and the damaged area. Therefore, the image is feature extracted through the semantic segmentation model, and then these features are used to identify the intact and damaged areas of the vehicle, and then a segmentation map is output, in which the damaged area is clearly marked, so that the corresponding damaged area image is segmented, providing accurate regional division for damage assessment. By accurately identifying and segmenting the damaged area, a detailed visual basis is provided for damage assessment, and the elimination of other intact areas is also conducive to reducing the amount of data processing and improving the efficiency of damage assessment.

[0063] In one embodiment, Figure 5 As shown, step S203 includes:

[0064] S501, inputting the damaged area image into a trained damage assessment model for damage detection and quantitative detection, and outputting the damage type and damage degree in the damaged area image;

[0065] S502: Obtain the latest damage assessment standard, which includes maintenance standard information corresponding to different damage types and damage degrees;

[0066] S503: According to the damage type and damage degree in the damaged area image, corresponding repair standard information is obtained from the damage assessment standard, and a damage assessment repair list and damage assessment repair amount are generated.

[0067] In this embodiment, the damaged area image is automatically and intelligently assessed through the trained damage assessment model, wherein the damage assessment model adopts a multi-task learning framework to integrate vehicle damage detection, classification and quantitative evaluation in a unified architecture. The framework can handle multiple subtasks at the same time to improve the overall reasoning efficiency and accuracy. The damaged area image is input into the trained damage assessment model for damage detection and quantitative detection, and the damage type and degree in the damaged area image are output. Specifically, the improved YOLO or Faster R-CNN algorithm can be used for real-time detection and positioning of the damaged area. By setting the damage category (such as scratches, dents, and cracks), the model can accurately frame the damaged area and provide its location coordinates. At the same time, deep learning technology (such as Depth Estimation) is used to estimate the depth and volume of the damage, and then the degree of damage (such as mild, moderate, and severe) is obtained. For example, when a car owner's vehicle is damaged in an accident, the uploaded damaged area image shows a crack on the front bumper and a dent on the left fender. The damaged area image is input into the damage assessment model, and the image features are analyzed through a deep learning algorithm to identify the crack on the front bumper as moderate damage and the dent on the fender as slight damage. Through automated damage detection and quantitative detection, the time and cost of manual damage assessment is greatly reduced, the overall damage assessment efficiency is improved, and an accurate basis is provided for subsequent repairs and claims.

[0068] In addition, the intelligent damage assessment platform obtains the latest damage assessment standards from the database, that is, the damage assessment standards are dynamically adjusted according to the changes in the cost and process of the vehicle repair market. By obtaining the latest damage assessment standards, the current repair standard information of different types and degrees of damage can be accurately obtained. For example, for moderate bumper cracks, the standard may stipulate the use of specific types of materials and processes for repair and repair costs, while for mild fender dents, only simple sheet metal repairs and repair costs may be required. By obtaining the latest standardized repair information, damage assessment and claims can be made more reasonably. According to the damage type and degree of damage in the damaged area image, the corresponding repair standard information is obtained from the damage assessment standard, so as to generate a detailed damage assessment repair list and damage assessment repair amount. For example, based on parameters such as damage type, area, damage depth and vehicle location, the repair items are determined and the repair cost is calculated according to the corresponding repair standard, and a damage assessment list is generated. By generating a detailed damage assessment repair list, the damage assessment content can be clearly and accurately displayed, which increases the transparency and efficiency of the claims process.

[0069] In one embodiment, before step S203, the method further includes

[0070] Obtain vehicle maintenance price data and maintenance process information according to a preset update cycle;

[0071] The repair standard information in the damage assessment standard is dynamically updated according to the latest acquired repair price data and repair process information.

[0072] In this embodiment, the repair standard information corresponding to different types of damage and degrees of damage in the damage assessment standard is dynamically adjusted according to market changes. A preset update cycle is set, such as once a quarter, once a month, etc., to obtain the latest vehicle repair price data and repair process information. Of course, the latest repair price data and repair process information can also be obtained in real time. Specifically, information can be obtained from data sources such as automobile manufacturers, repair shops, parts suppliers and industry databases. The collected data includes the price of repair parts, labor costs, repair time and new technologies and processes used for various models. The repair standard information in the damage assessment standard is dynamically updated based on the latest repair price data and repair process information. For example, when the cost of certain repair items changes significantly, or new repair technologies are available to repair vehicles more economically and effectively, the relevant repair standard information in the damage assessment standard will be automatically updated to ensure that all subsequent damage assessment decisions are based on the latest market and technical conditions. This dynamic adjustment mechanism ensures the accuracy and timeliness of the damage assessment results, so that it can reflect the latest market conditions and technological progress, thereby improving the fairness and accuracy of insurance claims and avoiding unnecessary financial losses caused by backward standards.

[0073] In one embodiment, Figure 6 As shown, step S204 includes:

[0074] S601, calculating the next year's premium for the current claim application and the next year's premium for the current abandoned claim application respectively according to the motor vehicle insurance policy information, historical behavior information and damage assessment information;

[0075] S602, calculating premium change data based on the next year's premium for the current claim application and the next year's premium for the current waiver of the claim application;

[0076] S603, performing a tendency analysis on the car owner's claim settlement intention according to the historical behavior information to obtain a claim settlement tendency degree;

[0077] S604: Generate corresponding claim processing suggestion information according to the premium change data and the claim settlement tendency.

[0078] In this embodiment, when generating the claim processing suggestion information, a comprehensive analysis is performed based on the premium change and the customer's historical behavior pattern. Among them, the premium change is first calculated based on the motor insurance policy information, historical behavior information and damage assessment information, and the premium for the next year of the current claim application and the premium for the next year of the current waiver of the claim application are calculated. Since the premium of the motor insurance is related to the historical claim data, for example, if there is no claim history in the past year, the premium for the next year will have a no-claim discount, and if there is a claim history in the past year, the no-claim discount cannot be enjoyed and the premium will increase accordingly. For example, based on the car owner's auto insurance policy information and historical behavior information, it is shown that he had no claims record in the past year and enjoyed a 10% discount on the premium. In the damage assessment and the damage repair list generated this time, the estimated repair cost is 1,500 yuan. Based on historical behavior information (such as no claims record so far) and damage information, it is calculated that if the claim is applied for this time, the premium for the next year will no longer enjoy the no claims discount and will increase in a gradient, for example, the premium will be 9,000 yuan. At the same time, it is also calculated that if the claim is abandoned this time, there will be no claims record, and the premium for the next year can continue to enjoy a discount, for example, it is estimated to be 7,200 yuan. By calculating and showing the specific impact of applying for a claim or not applying for a claim on the premium for the next year, car owners can more clearly understand the impact of their choices on the premium.

[0079] The premium change data is calculated based on the premium for the next year of this claim application and the premium for the next year if the claim application is waived. Usually, the premium for the next year of this claim application is greater than the premium for the next year if the claim application is waived. Therefore, the difference between the two values ​​is calculated to obtain the premium difference under different claim decisions, so that car owners can intuitively understand the impact of the current claim decision on the subsequent premium increase, and make more reasonable claims processing.

[0080] At the same time, while accurately calculating the changes in premiums, we also conduct a tendency analysis on the car owner's willingness to pay claims based on the car owner's historical behavior information to obtain the degree of claim tendency. For example, by analyzing the car owner's historical behavior information, it is found that he has no claim record in the past three years, and his driving habits are good and he has no violation record. At this time, after analyzing the car owner's claim tendency, it is concluded that he is more inclined to maintain a good insurance record, so the claim tendency is lower. By analyzing the car owner's historical behavior, it is convenient to obtain claims suggestions that are closer to the car owner's behavior pattern.

[0081] Based on the premium change data and the degree of claims tendency, the corresponding claims processing suggestion information is comprehensively generated. When providing claims advice to car owners, the repair amount caused by the vehicle damage, the premium changes and personal tendencies can be comprehensively considered. This can optimize the claims decision-making process, reduce unnecessary claims, and assist car owners in making the best decisions. At the same time, it can also significantly reduce the management costs of insurance companies by reducing small claims.

[0082] In one embodiment, step S604 includes:

[0083] When the premium change data is greater than or equal to the preset incremental data and / or the claim settlement tendency is less than or equal to the preset tendency threshold, generating claim settlement processing suggestion information that does not recommend applying for claim settlement this time;

[0084] When the premium change data is less than the preset incremental data and / or the claim tendency degree is greater than the preset tendency threshold, claim processing suggestion information for this recommended claim application is generated.

[0085] In this embodiment, corresponding claims processing suggestions are generated based on the extent of premium changes and the depth of the car owner's claim tendency. Specifically, a preset incremental data and a preset tendency threshold can be set in advance. The preset incremental amount can be a fixed amount such as 1000, 2000, etc., or it can be variable, for example, the damage assessment and repair amount in each damage assessment information is the claim amount. When the premium change data is greater than or equal to the preset incremental data, for example, the premium increase amount for the next year caused by the current claim application is greater than the damage assessment and repair amount, it is recommended that the car owner not apply for a claim this time. Similarly, the preset tendency threshold may be a fixed ratio such as 0.5 (1 means being very inclined to make a claim, 0 means being very unwilling to make a claim). After a tendency analysis of the car owner's willingness to make a claim, the degree of his tendency to make a claim is 0.3, which is less than the preset tendency threshold of 0.5. In this case, the car owner is also advised not to apply for a claim this time. That is, if either of the two conditions, namely, the premium change data is too large and the degree of claim tendency is small, is met, claim processing advice information indicating that it is not recommended to apply for a claim this time is generated. This allows identification of situations where applying for a claim may not be cost-effective, helping car owners avoid premium increases caused by small claims, thereby reducing unnecessary claims. Similarly, when the premium change data is less than the preset incremental data and / or the claim tendency is greater than the preset tendency threshold, the claim processing suggestion information for this recommended claim application is generated, so that when the premium change caused by the claim application is not large, or the vehicle damage is serious (even if the claim premium increase amount is less than the damage assessment repair amount) or the owner tends to claim, the claim processing suggestion information for this recommended claim application is generated. By comprehensively considering the premium changes and claim tendencies, more reasonable claims suggestions can be provided to car owners, helping them to apply for claims when necessary, so that personalized suggestions can be provided based on the specific circumstances of the car owners, thereby improving the flexibility of handling damage assessment claims.

[0086] It should be noted that there is not necessarily a certain order between the above-mentioned steps. A person skilled in the art can understand, based on the description of the embodiments of the present invention, that in different embodiments, the above-mentioned steps may have different execution orders, that is, they may be executed in parallel, may be executed interchangeably, and so on.

[0087] Further references Figure 7 , as a response to the above Figure 2 The present invention provides an embodiment of an intelligent damage assessment processing device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0088] like Figure 7 As shown, the intelligent damage assessment processing device 70 described in this embodiment includes:

[0089] The image receiving module 701 is used to receive the accident vehicle image uploaded by the vehicle owner;

[0090] A preprocessing module 702 is used to preprocess the accident vehicle image and segment it to obtain a damaged area image;

[0091] The damage assessment module 703 is used to assess the damage of the vehicle according to the damaged area image, and generate corresponding damage assessment information according to the assessment result and the currently acquired damage assessment standard;

[0092] The claim settlement suggestion output module 704 is used to obtain the car owner's auto insurance policy information and historical behavior information, and generate corresponding claim settlement processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information.

[0093] The module referred to in the present invention refers to a series of computer program instruction segments that can perform specific functions, which is more suitable for describing the execution process of intelligent damage assessment processing than a program. For the specific implementation method of each module, please refer to the corresponding method embodiment above, which will not be repeated here.

[0094] In one embodiment, the image receiving module 701 includes:

[0095] A calling unit, for responding to a vehicle accident request initiated by a vehicle owner, calling a camera of a device currently logged in by the vehicle owner to collect images of the accident vehicle from multiple angles;

[0096] A shooting guidance unit, used to analyze the real-time shooting picture of the camera, and to overlay and display corresponding shooting guidance information on the real-time shooting picture according to the picture analysis result, so as to guide the car owner to adjust the acquisition parameters of the camera;

[0097] The image receiving unit is used to receive the accident vehicle images at different angles acquired by the camera under corresponding acquisition parameters.

[0098] In one embodiment, the preprocessing module 702 includes:

[0099] A denoising unit, configured to perform denoising processing on the accident vehicle image by using a pre-trained denoising model, identify and eliminate a specified type of noise in the accident vehicle image, and obtain a vehicle denoised image;

[0100] An equalization unit, configured to perform histogram equalization processing and white balance adjustment processing on the denoised vehicle image to obtain a balanced vehicle image;

[0101] The segmentation unit is used to input the vehicle balance image into a trained semantic segmentation model, identify and classify the intact area and the damaged area of ​​the vehicle balance image, and obtain the corresponding damaged area image by segmentation according to the identification result.

[0102] In one embodiment, the damage assessment module 703 includes:

[0103] A damage detection unit, used for inputting the damaged area image into a trained damage assessment model for damage detection and quantitative detection, and outputting the damage type and damage degree in the damaged area image;

[0104] A standard acquisition unit, used to acquire the latest damage assessment standard, wherein the damage assessment standard includes maintenance standard information corresponding to different damage types and damage degrees;

[0105] The damage assessment unit is used to obtain corresponding repair standard information from the damage assessment standard according to the damage type and damage degree in the damaged area image, and generate a damage assessment repair list and a damage assessment repair amount.

[0106] In one embodiment, the device 70 further includes:

[0107] A data acquisition module, used to acquire vehicle maintenance price data and maintenance process information according to a preset update cycle;

[0108] The dynamic update module is used to dynamically update the maintenance standard information in the damage assessment standard according to the latest acquired maintenance price data and maintenance process information.

[0109] In one embodiment, the claim advice output module 704 includes:

[0110] A premium calculation unit, used to calculate the premium for the next year for the current claim application and the premium for the next year for the current abandoned claim application, respectively, based on the motor insurance policy information, historical behavior information and damage assessment information;

[0111] The premium evaluation unit is used to calculate the premium change data according to the premium for the next year of the current claim application and the premium for the next year of the current waiver of the claim application;

[0112] A claim settlement tendency analysis unit, used to perform a tendency analysis on the car owner's claim settlement willingness according to the historical behavior information to obtain a claim settlement tendency degree;

[0113] The suggestion output unit is used to generate corresponding claim processing suggestion information according to the premium change data and the claim tendency degree.

[0114] In one embodiment, the suggestion output unit is specifically configured to:

[0115] When the premium change data is greater than or equal to the preset incremental data and / or the claim settlement tendency is less than or equal to the preset tendency threshold, generating claim settlement processing suggestion information that does not recommend applying for claim settlement this time;

[0116] When the premium change data is less than the preset incremental data and / or the claim tendency degree is greater than the preset tendency threshold, claim processing suggestion information for this recommended claim application is generated.

[0117] In the above embodiment, the present invention discloses an intelligent damage assessment processing device, which receives the accident vehicle image uploaded by the car owner; pre-processes the accident vehicle image and segments it to obtain the damaged area image; performs vehicle damage assessment based on the damaged area image, and generates corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; obtains the car owner's auto insurance policy information and historical behavior information, and generates corresponding claims processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information. By receiving the accident vehicle image and performing an automated vehicle damage assessment, the claims damage assessment information is obtained based on the assessment result and the latest acquired damage assessment standard, and at the same time, the claims decision of this accident is comprehensively outputted in combination with the car owner's insurance policy and claims history information, so that the efficiency and flexibility of auto insurance damage assessment can be improved, saving time and cost while also being more adaptable to the needs of different customers and vehicle damage situations.

[0118] Another embodiment of the present invention provides a computer device, such as Figure 8 As shown, the computer device 80 includes:

[0119] One or more processors 801 and memory 802, Figure 8 A processor 801 is used as an example for the description. The processor 801 and the memory 802 may be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0120] Processor 801 is used to complete various control logics of computer device 80, and it can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component or any combination of these components. In addition, processor 801 can also be any traditional processor, microprocessor or state machine. Processor 801 can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP and / or any other such configuration.

[0121] The memory 802 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions corresponding to the intelligent damage assessment processing method in the embodiment of the present invention. The processor 801 executes various functional applications and data processing of the computer device 80 by running the non-volatile software programs, instructions and units stored in the memory 802, that is, implements the intelligent damage assessment processing method in the above method embodiment.

[0122] The memory 802 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device 80, etc. In addition, the memory 802 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 802 may optionally include a memory remotely arranged relative to the processor 801, and these remote memories may be connected to the computer device 80 via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. One or more units are stored in the memory 802, and when executed by one or more processors 801, the steps of the intelligent damage assessment processing method in any of the above-mentioned method embodiments are executed.

[0123] In the above embodiment, the present invention discloses a computer device, which receives an image of an accident vehicle uploaded by a car owner; pre-processes the image of the accident vehicle and segments it to obtain an image of a damaged area; performs a vehicle damage assessment based on the image of the damaged area, and generates corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; obtains the car owner's auto insurance policy information and historical behavior information, and generates corresponding claims processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information. By receiving the image of the accident vehicle and performing an automated vehicle damage assessment, claim damage assessment information is obtained based on the assessment result and the latest acquired damage assessment standard, and a comprehensive suggestion output is also made for the claim decision of this accident in combination with the car owner's insurance policy and claim history information, so that the efficiency and flexibility of auto insurance damage assessment can be improved, time and cost can be saved, and the needs of different customers and vehicle damage situations can be better adapted.

[0124] An embodiment of the present invention provides a non-volatile computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the steps of the intelligent damage assessment processing method in any of the above method embodiments are executed.

[0125] In the above embodiment, the present invention discloses a non-volatile computer-readable storage medium, which receives an image of an accident vehicle uploaded by a car owner; pre-processes the image of the accident vehicle and segments it to obtain an image of a damaged area; performs a vehicle damage assessment based on the image of the damaged area, and generates corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; obtains the car owner's auto insurance policy information and historical behavior information, and generates corresponding claims processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information. By receiving the image of the accident vehicle and performing an automated vehicle damage assessment, claim damage assessment information is obtained based on the assessment result and the latest acquired damage assessment standard, and a comprehensive suggestion output is also made for the claim decision of this accident in combination with the car owner's insurance policy and claim history information, so that the efficiency and flexibility of auto insurance damage assessment can be improved, time and cost can be saved, and the needs of different customers and vehicle damage situations can be better adapted.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0127] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0128] In summary, the present invention discloses an intelligent damage assessment processing method, device, equipment and medium, the method includes: receiving the accident vehicle image uploaded by the car owner; pre-processing the accident vehicle image, segmenting to obtain the damaged area image; performing vehicle damage assessment based on the damaged area image, generating corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; obtaining the car owner's auto insurance policy information and historical behavior information, and generating corresponding claims processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information. By receiving the accident vehicle image and performing an automated vehicle damage assessment, the claim damage assessment information is obtained based on the assessment result and the latest acquired damage assessment standard, and at the same time, the car owner's insurance policy and claims history information are combined to output a comprehensive suggestion for the claim decision of this accident, so that the efficiency and flexibility of auto insurance damage assessment can be improved, saving time and cost while also being more adaptable to the needs of different customers and vehicle damage situations.

[0129] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium, and the computer program can include the processes of the above-mentioned method embodiments when executed. The storage medium can be a memory, a disk, a floppy disk, a flash memory, an optical storage device, etc.

[0130] It should be noted that if any software tools or components other than those of the Company appear in the embodiments of the present application, they are merely used for illustration and do not represent actual use. It should be understood that the application of the present invention is not limited to the above examples, and that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes shall fall within the scope of protection of the claims attached to the present invention.

Claims

1. An intelligent damage assessment method, characterized in that: include: Receive accident vehicle images uploaded by vehicle owners; Preprocessing the accident vehicle image and segmenting it to obtain a damaged area image; Performing a vehicle damage assessment based on the damaged area image, and generating corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; The vehicle owner's auto insurance policy information and historical behavior information are obtained, and corresponding claims processing suggestion information is generated based on the auto insurance policy information, historical behavior information and damage assessment information.

2. The intelligent damage assessment processing method according to claim 1 is characterized in that: The receiving of the accident vehicle image uploaded by the vehicle owner includes: In response to a vehicle accident request initiated by the car owner, the camera of the car owner's currently logged-in device is called to collect images of the accident vehicle from multiple angles; Analyze the real-time shooting picture of the camera, and overlay corresponding shooting guidance information on the real-time shooting picture according to the picture analysis result to guide the car owner to adjust the acquisition parameters of the camera; Receive images of the accident vehicle at different angles captured by the camera under corresponding acquisition parameters.

3. The intelligent damage assessment processing method according to claim 1 is characterized in that: The preprocessing of the accident vehicle image to obtain a damaged area image by segmentation includes: Performing denoising processing on the accident vehicle image by using a pre-trained denoising model, identifying and eliminating a specified type of noise in the accident vehicle image, and obtaining a vehicle denoised image; Performing histogram equalization processing and white balance adjustment processing on the denoised vehicle image to obtain a vehicle balanced image; The vehicle balance image is input into a trained semantic segmentation model, the intact area and the damaged area of ​​the vehicle balance image are identified and classified, and the corresponding damaged area image is obtained by segmentation according to the identification result.

4. The intelligent damage assessment processing method according to claim 1 is characterized in that: The vehicle damage assessment is performed according to the damaged area image, and corresponding damage assessment information is generated according to the assessment result and the currently acquired damage assessment standard, including: Inputting the damaged area image into a trained damage assessment model for damage detection and quantitative detection, and outputting the damage type and damage degree in the damaged area image; Obtain the latest damage assessment standards, which include repair standard information corresponding to different damage types and damage degrees; According to the damage type and damage degree in the damaged area image, corresponding repair standard information is obtained from the damage assessment standard to generate a damage assessment repair list and a damage assessment repair amount.

5. The intelligent damage assessment processing method according to claim 4 is characterized in that: Before performing the vehicle damage assessment based on the damaged area image and generating corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard, the method further includes: Obtain vehicle maintenance price data and maintenance process information according to a preset update cycle; The repair standard information in the damage assessment standard is dynamically updated according to the latest acquired repair price data and repair process information.

6. The intelligent damage assessment processing method according to claim 1 is characterized in that: The obtaining of the vehicle owner's auto insurance policy information and historical behavior information, and generating corresponding claim settlement suggestion information according to the auto insurance policy information, historical behavior information and damage assessment information, includes: Calculate the premium for the next year for the current claim application and the premium for the next year for the current abandoned claim application based on the auto insurance policy information, historical behavior information and damage assessment information; Calculate the premium change data based on the premium for the next year of the current claim application and the premium for the next year of the current waiver of the claim application; Performing a tendency analysis on the car owner's willingness to pay claims based on the historical behavior information to obtain a degree of the tendency to pay claims; Corresponding claim processing suggestion information is generated based on the premium change data and the degree of claim tendency.

7. The intelligent damage assessment processing method according to claim 6 is characterized in that: Generating corresponding claim processing suggestion information according to the premium change data and claim tendency degree includes: When the premium change data is greater than or equal to the preset incremental data and / or the claim settlement tendency is less than or equal to the preset tendency threshold, generating claim settlement processing suggestion information that does not recommend applying for claim settlement this time; When the premium change data is less than the preset incremental data and / or the claim tendency degree is greater than the preset tendency threshold, claim processing suggestion information for this recommended claim application is generated.

8. An intelligent damage assessment processing device, characterized in that: include: An image receiving module is used to receive accident vehicle images uploaded by the vehicle owner; A preprocessing module, used for preprocessing the accident vehicle image and segmenting it to obtain a damaged area image; A damage assessment module, used to assess vehicle damage based on the damaged area image, and generate corresponding damage assessment information based on the assessment result and the currently acquired damage assessment standard; The claim settlement suggestion output module is used to obtain the car owner's auto insurance policy information and historical behavior information, and generate corresponding claim settlement processing suggestion information based on the auto insurance policy information, historical behavior information and damage assessment information.

9. A computer device, characterized in that: comprising at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent damage assessment processing method described in any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores computer-executable instructions, which, when executed by one or more processors, enable the one or more processors to execute the intelligent damage assessment processing method described in any one of claims 1-7.

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