Vehicle loss assessment reminding method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle loss assessment reminding method and device, electronic equipment and a storage medium, and relates to the technical field of vehicle insurance loss assessment. The method comprises the steps that whether a part is damaged or not is judged according to an actual image of the part, and the part which is determined to be damaged serves as a key damaged object; obtaining a part adjacent to the position of the key damaged object and taking the part as a possible damaged object; in all the shot actual images, trying to identify an actual image containing a possible damaged object and taking the actual image as a target image; if the actual image containing the possibly damaged object cannot be identified, generating first reminding information; and if the actual image containing the possible damaged object is successfully identified and whether the possible damaged object is damaged or not is not determined, second reminding information is generated. According to the vehicle loss assessment reminding method, the problem that a traditional loss assessment mode depends on personnel experience extremely is solved, and the effects of reducing the influence of human factors and improving the comprehensiveness and accuracy of the loss assessment process are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle insurance damage assessment, and in particular to a vehicle damage assessment reminder method, device, electronic equipment and storage medium. Background Art

[0002] In existing vehicle damage assessment schemes, vehicle damage assessment generally requires the assessor to take photos of damaged parts for evidence collection and give a corresponding damage assessment price. However, this damage assessment method is extremely dependent on the professional experience of the assessor. If the assessor is inexperienced, he or she may often only observe the obviously damaged parts and ignore or misjudge the damage to other related parts, resulting in the omission of photos of some damaged parts during the shooting process, which in turn brings trouble to the subsequent claims.

[0003] There is currently no effective technical solution to the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a vehicle damage assessment reminder method, device, electronic device and storage medium, which solves the problem that traditional damage assessment methods are extremely dependent on human experience, thereby reducing the impact of human factors and improving the comprehensiveness and accuracy of the damage assessment process.

[0005] In a first aspect, the present invention provides a vehicle damage assessment reminder method, comprising the following steps: S1. judging whether the component is damaged according to the actual image of the component, and taking the damaged component as the key damaged object; S2. According to a preset database, parts adjacent to the location of the key damaged object are obtained as possible damaged objects; S3. Among all the actual images that have been taken, try to identify the actual image containing the potentially damaged object and use it as the target image, specifically including: S31. After attempting to identify the actual image containing the potentially damaged object, executing: S311. If the actual image containing the potentially damaged object cannot be identified, generating a first reminder message for reminding the user to take a photo of the actual image of the potentially damaged object; S312. If the actual image containing the potentially damaged object is successfully identified, whether the potentially damaged object is damaged is determined based on the target image, and when it is uncertain whether the potentially damaged object is damaged, a second reminder message is generated to remind the user to review the target image, and when it is determined that the potentially damaged object is damaged, the potentially damaged object is converted into the key damaged object.

[0006] The vehicle damage assessment reminder method provided by the present invention gives reminders to the damage assessor when necessary based on the positional correlation between parts and combined with the recognition and analysis of images, so that the damage assessor can conduct damage assessment in a more unified and standard manner, which is conducive to reducing the situation of misjudgment and omission of evidence collection by the damage assessor, and achieves the effect of improving the comprehensiveness and accuracy of the damage assessment process.

[0007] Furthermore, the specific steps in step S312 include: S3121. Potentially damaged objects that are uncertain whether they are damaged and are adjacent to multiple key damaged objects are taken as key inspection objects, and when the key inspection objects appear, additional mandatory requirement information is generated; the mandatory requirement information includes information requiring the user to take an actual image of the key inspection object at a specified angle.

[0008] The key inspection objects are more likely to be damaged and should be given special attention and a comprehensive inspection. The purpose of this is to obtain more comprehensive and clearer image information so as to more accurately determine whether these parts are damaged.

[0009] Furthermore, the mandatory requirement information includes information requiring the user to take six basic views of the key inspection object as actual images of the key inspection object.

[0010] By requiring the capture of six basic views, the problem of insufficient information or incomplete angles that may occur in traditional damage assessment methods is effectively solved. With these comprehensive image data, damage assessors can more accurately assess the damage to the key inspection objects and reduce misjudgments or omissions caused by limited viewing angles.

[0011] Furthermore, the specific steps in step S3 also include: S32. Before step S31, it also includes the following steps: S321. Grouping all the actual images that have been taken according to the assembly to which each component belongs, so that all the actual images corresponding to all components belonging to the same assembly are classified into the same atlas; S322. According to the assembly to which the possibly damaged object belongs, attempt to identify an actual image containing the possibly damaged object in the corresponding atlas.

[0012] By grouping first, a more accurate search range is provided for the subsequent recognition process, greatly reducing the number of images that need to be processed. This not only improves the recognition speed, but also reduces the probability of misidentification.

[0013] Furthermore, the specific steps in step S1 include: S11. Classify all damaged parts into slightly damaged and severely damaged categories according to the degree of damage of each damaged part, and regard the parts belonging to the severely damaged category as the key damaged objects.

[0014] Furthermore, the specific steps in step S11 include: S111. Take six basic views of the damaged vehicle as the damaged image of the damaged vehicle; S112. Determine the main damaged position of the damaged vehicle according to the damage image; S113. Classify the damaged parts located at the main damaged position into the severely damaged category.

[0015] Furthermore, the specific steps in step S112 include: S1121. Obtain the accident type of the damaged vehicle; S1122. Determine the main damaged direction according to the damage image and the accident type.

[0016] In a second aspect, the present invention provides a vehicle damage assessment reminder device, comprising: A judgment module, used to judge whether the component is damaged according to the actual image of the component, and to regard the damaged component as a key damaged object; An acquisition module, used for acquiring parts adjacent to the location of the key damaged object as possible damaged objects according to a preset database; The recognition module is used to try to recognize the actual image containing the potentially damaged object from all the actual images that have been taken and use it as the target image, specifically including: S31. After attempting to identify the actual image containing the potentially damaged object, executing: S311. If the actual image containing the potentially damaged object cannot be identified, generating a first reminder message for reminding the user to take a photo of the actual image of the potentially damaged object; S312. If the actual image containing the potentially damaged object is successfully identified, whether the potentially damaged object is damaged is determined based on the target image, and when it is uncertain whether the potentially damaged object is damaged, a second reminder message is generated to remind the user to review the target image, and when it is determined that the potentially damaged object is damaged, the potentially damaged object is converted into the key damaged object.

[0017] The vehicle damage assessment reminder device provided by the present invention assists the damage assessor to complete a more comprehensive and accurate damage assessment through a double reminder method, thereby greatly reducing the possibility of human error.

[0018] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the vehicle damage assessment reminder method provided in the first aspect are executed.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the vehicle damage assessment reminder method provided in the first aspect are performed.

[0020] From the above, it can be seen that the vehicle damage assessment reminder method provided by the present invention, based on the correlation between parts and combined with image recognition technology, avoids the damage assessor from missing the damage inspection of other related parts, reduces the degree of reliance on the professional experience of the damage assessor in the damage assessment process, and conducts damage assessment in a more standardized and objective manner, which is conducive to reducing human misjudgment, omissions and other problems, and achieves the effect of improving the comprehensiveness and accuracy of the damage assessment process.

[0021] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a vehicle damage assessment reminder method provided in an embodiment of the present invention.

[0023] Figure 2 A schematic diagram of the structure of a vehicle damage assessment reminder device provided in an embodiment of the present invention.

[0024] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0025] Description of labels: 100, judgment module; 200, acquisition module; 300, identification module; 13, electronic device; 1301, processor; 1302, memory; 1303, communication bus. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0028] With the rapid development of automobile insurance business, the accuracy and efficiency of vehicle damage assessment process are becoming more and more important. The traditional method of vehicle damage assessment mainly relies on the experience and expertise of the damage assessor to assess the degree of damage to the vehicle through on-site inspection and photo recording. However, this method has some limitations, such as the high reliance on personal experience in damage assessment accuracy, the difficulty in finding some damaged parts in complex accident scenes, the risk of omissions caused by time pressure, and the challenges brought by the increasingly complex structure of modern vehicles.

[0029] For example, a sedan was involved in a side collision at an intersection, with the right door significantly dented and the right rearview mirror knocked off. Upon arriving at the scene, the damage assessor quickly took photos of the damaged door and rearview mirror. However, due to time constraints and limited experience, he may have overlooked potential damage to the right B-pillar and wheel hub. These overlooked damage points may lead to an underestimation of subsequent repair costs and even affect the safety of the vehicle.

[0030] For this, please refer to the attached Figure 1 The present invention provides a vehicle damage assessment reminder method, comprising the following steps: S1. Determine whether the component is damaged according to the actual image of the component, and take the damaged component as the key damaged object (it should be noted that this embodiment does not limit all the damaged components to be the key damaged objects. In fact, all the damaged components can be the key damaged objects, or some of them can be selected as the key damaged objects. For example, in the following embodiment, all the damaged components are divided into slightly damaged and severely damaged categories, among which the severely damaged category belongs to some of the damaged components, and in this embodiment, only the severely damaged components are taken as the key damaged objects); S2. According to a preset database, components adjacent to the location of the key damaged object are obtained and regarded as possible damaged objects; S3. Among all the actual images that have been taken, try to identify the actual images that contain the potentially damaged objects and use them as target images, including: S31. After attempting to identify the actual image containing the potentially damaged object, perform: S311. If the actual image containing the possibly damaged object cannot be identified, a first reminder message is generated to remind the user to take an actual image of the possibly damaged object; S312. If the actual image containing the potentially damaged object is successfully identified, whether the potentially damaged object is damaged is determined based on the target image, and when it is uncertain whether the potentially damaged object is damaged, a second reminder message is generated to remind the user to review the target image, and when it is determined that the potentially damaged object is damaged, the potentially damaged object is converted into a key damaged object.

[0031] This embodiment assists the damage assessor in completing damage assessment by means of double reminders. First, when the system finds that the damage assessor has not taken an actual image of a potentially damaged object associated with a key damaged object, the system gives the damage assessor a first reminder so that the damage assessor can correct the error, which helps to achieve a comprehensive damage assessment. Furthermore, when it is found that the actual image of the potentially damaged object taken by the damage assessor cannot determine whether it is damaged, the system gives the damage assessor a second reminder so that the damage assessor can review the potentially damaged object, which helps to achieve an accurate damage assessment.

[0032] Specifically, first, the mobile device's camera captures an image of the damaged part of the vehicle. After the image is taken, the application calls a pre-trained deep learning model, such as a convolutional neural network based on the ResNet-50 architecture, to analyze the image. The model can identify the vehicle parts in the image and determine whether they are damaged.

[0033] When the system identifies a damaged part, it marks it as a key damaged object. The system then queries a preset database that stores the positional relationships between the vehicle's components. For example, if the front bumper is identified as damaged, the system automatically identifies adjacent components such as the headlights, radiator grille, and front fenders, and marks them as possible damaged objects.

[0034] Next, the system searches for these potentially damaged objects in all the images that have been taken. If the images of some potentially damaged objects are missing or unclear, the system will generate a reminder message, asking the user to take additional photos or retake them. For potentially damaged objects with existing images, the system will use the deep learning model to analyze whether they are damaged. If it is uncertain whether they are damaged, the system will generate a reminder message, asking the user to recheck.

[0035] This method effectively reduces omissions caused by human factors through a systematic analysis and reminder mechanism, and improves the accuracy and comprehensiveness of vehicle damage assessment. It can not only help inexperienced damage assessors avoid missing important information, but also provide assistance to experienced damage assessors to ensure the integrity of the damage assessment process.

[0036] The innovation of the present invention is that it combines the correlation of adjacent parts, image recognition technology and an intelligent reminder system to form a closed-loop damage assessment process. This method can more comprehensively assess the damage to the vehicle and greatly reduce the possibility of human error. At the same time, it also improves the efficiency of damage assessment. The traditional method may require the assessor to check the vehicle back and forth many times, while the method of the present invention can record all damage as completely as possible during a single inspection through an intelligent reminder system, reducing duplication of work and saving time and labor costs.

[0037] Compared with the prior art, the solution of the present invention not only uses advanced deep learning technology for damage identification, but also introduces component correlation analysis and intelligent reminder mechanism. This method can systematically consider the potential damage of adjacent components, rather than just being limited to identifying obvious damaged parts.

[0038] In addition, the solution of the present invention has strong adaptability and scalability. As vehicle technology continues to advance, new models and parts continue to emerge. The system of the present invention can adapt to these changes by updating the preset database and deep learning model to maintain its effectiveness and accuracy.

[0039] The vehicle damage assessment reminder method proposed in the present invention effectively solves the problem of possible omission of damaged parts in the vehicle damage assessment process by combining advanced image recognition technology, parts correlation analysis and intelligent reminder system, significantly improves the accuracy, comprehensiveness and efficiency of damage assessment, and provides insurance companies and car owners with more reliable and efficient damage assessment services.

[0040] In some embodiments, the specific steps in step S312 include: S3121. Potentially damaged objects that are uncertain whether they are damaged and are adjacent to multiple key damaged objects are taken as key inspection objects, and when key inspection objects appear, additional mandatory requirement information is generated; the mandatory requirement information includes information requiring users to take actual images of the key inspection objects at specified angles.

[0041] In order to more effectively identify and verify potentially damaged vehicle parts, especially those adjacent to multiple confirmed damaged parts, the concept of "key inspection objects" is introduced in this embodiment, and a special processing flow is set for them. Key inspection objects refer to potentially damaged objects that are uncertain whether they are damaged but are adjacent to multiple confirmed damaged parts (key damaged objects). For these key inspection objects, the system will additionally generate mandatory information. This mandatory information requires the user to take actual images of the key inspection objects at a specified angle. Compared with other adjacent parts, key inspection objects are more likely to be damaged and should be paid special attention to and inspected in all aspects. The purpose of this is to obtain more comprehensive and clearer image information so as to more accurately determine whether these parts are damaged.

[0042] There are also multiple ways to generate mandatory information. The system can automatically generate the best shooting angle suggestions based on the location and characteristics of the key inspection objects. For example, for the external parts of the car body, multi-angle shooting may be required; while for the internal parts, specific disassembly and shooting guidance may be required.

[0043] In addition, this technical solution can also be combined with image recognition technology to automatically determine whether the image provided by the user meets the requirements. If the image quality is insufficient or the angle is inappropriate, the system can provide immediate feedback and ask the user to retake the photo, thereby ensuring that high-quality image data is obtained.

[0044] This method not only improves the accuracy of vehicle damage assessment, but also reduces missed detections and misjudgments, thereby improving the efficiency and reliability of the entire damage assessment process. By forcing users to shoot at specified angles, it can also standardize user operations to a certain extent, ensure the acquisition of high-quality image data, and provide a more reliable basis for subsequent damage assessment analysis.

[0045] During the implementation process, the system first identifies the key damaged objects, and then determines the parts adjacent to these key damaged objects based on the preset database. Next, the system analyzes which of these adjacent parts are uncertain whether they are damaged, and marks them as possible damaged objects. Among these possible damaged objects, the system further selects parts adjacent to multiple key damaged objects and marks them as key inspection objects.

[0046] Once the key inspection objects are determined, the system will generate mandatory information. This information includes not only instructions for users to take photos of the key inspection objects, but also specific shooting angle guidance. For example, the system may require users to take a picture from the front, side, and top view to ensure that possible damage can be fully captured.

[0047] After the user takes and uploads the pictures as instructed, the system will analyze them. If the picture quality is insufficient or the angle does not meet the requirements, the system will prompt the user to retake the picture. Only when all the necessary pictures meet the requirements will the system enter the next stage of damage analysis.

[0048] In actual applications, a certain component is adjacent to multiple damaged components (key damaged objects), but no damage is found during the initial inspection. In some cases, it may be because the angle of observing the component during the initial inspection is not appropriate, and its damaged point is obscured or appears at another angle, so that the component is missed. Specifically, suppose that in a front-end collision accident, the system has confirmed that the front bumper and hood are damaged. According to the preset database, the system knows that the headlights are adjacent to the front bumper and the hood. At this time, the headlights are marked as key inspection objects. Compared with other components that are only adjacent to one damaged component, the headlights have a higher probability of damage, but the headlights were not found to be damaged during the initial inspection. After careful inspection, it was found that at the angle of the initial inspection, the ambient light concealed the cracks on the headlights, while the cracks could be clearly observed at another angle.

[0049] When it is determined that there are key inspection objects, the system will generate mandatory information, requiring users to take photos of these key inspection objects from multiple angles. For example, for headlights, the system may require users to take three photos of the front, 45-degree angle and side; for the hood, the system may require users to take a top view and a side view; for the radiator, the system may require users to open the hood and take photos of the front and side of the radiator.

[0050] In this way, the present invention can more comprehensively capture damage that may be overlooked. For example, although the headlight may not have obvious damage on the outside, through multi-angle shooting, it may be found that the lampshade has fine cracks or the internal structure is slightly deformed. Similarly, the hood may look intact on the outside, but a closer inspection may reveal slight dents or paint damage. For the radiator, although the appearance may be fine, a close-up shot may reveal a small leak or fin deformation.

[0051] Compared with the prior art, the traditional vehicle damage assessment method mainly relies on the experience of the damage assessor, which is prone to omissions or misjudgments. In particular, parts that are adjacent to obviously damaged parts but have less obvious damage themselves are easily overlooked. The present invention, by introducing the concept of key inspection objects and combining it with the mandatory photography requirement, can systematically identify and inspect these easily overlooked parts.

[0052] In addition, in the traditional method, even experienced damage assessors may fail to find some hidden damage due to improper shooting angles. The present invention ensures that each key inspection object can be recorded from the best angle by specifying the shooting angle, greatly improving the accuracy of damage identification.

[0053] The technical solution of the present invention effectively solves the problems of omissions and misjudgments that may occur in the process of vehicle damage assessment through a systematic and standardized method, improves the accuracy and comprehensiveness of damage assessment, and provides a more reliable basis for subsequent claims settlement.

[0054] In some embodiments, the mandatory requirement information includes information requiring the user to capture six basic views of the key verification object as actual images of the key verification object.

[0055] In order to improve the accuracy and efficiency of vehicle damage assessment by obtaining more comprehensive image information of key inspection objects, this embodiment requires users to take six basic views of key inspection objects, which can ensure that the image information of key inspection objects is fully obtained for most damaged parts. These six basic views may include images from different angles such as front, back, left, right, top, and bottom, thereby providing more comprehensive visual information of key inspection objects.

[0056] Specifically, the six basic views in the present invention generally include: a front view, a rear view, a left view, a right view, a top view, and a bottom view. This all-round shooting method can ensure that every angle of the key inspection object is captured, thereby minimizing the possibility of missing key information.

[0057] In practical applications, these views can be captured by a mobile device camera or a professional camera. To improve the accuracy and consistency of the capture, auxiliary lines or angle indicators can be provided on the capture interface to help users accurately locate and align the capture angle. For example, a virtual cube frame can be displayed on the capture interface to guide users to capture the key inspection object from the directions of the six faces.

[0058] In addition, the present invention can also be combined with image recognition technology to automatically detect whether the captured image meets the requirements of the six basic views. If the image of a certain perspective is unclear or incomplete, the system can prompt the user to reshoot to ensure high-quality image data.

[0059] This method, combined with the aforementioned step of focusing on inspecting potentially damaged objects that are uncertain whether they are damaged and adjacent to multiple key damaged objects, can more comprehensively assess potential damage. By taking six-view photos of these key inspection objects, damage assessors can more accurately determine the actual damage status of these components and reduce misjudgments or omissions caused by viewing angle limitations.

[0060] In the process of vehicle damage assessment, the technical solution of the present invention effectively solves the problem of insufficient information or incomplete angles that may occur in traditional damage assessment methods by requiring the capture of six basic views. Damage assessors can use these comprehensive image data to more accurately assess the damage to key inspection objects and reduce misjudgments or omissions caused by limited viewing angles. At the same time, this standardized shooting requirement also improves the efficiency of the damage assessment process, because damage assessors can review and evaluate these images according to unified standards.

[0061] For example, suppose that in a vehicle collision accident, the front bumper is identified as a key inspection object. Through the method of the present invention, the user will be asked to capture six basic views of the front bumper. This includes not only the conventional front view, but also an upward view taken from below, which may reveal hidden structural damage under the bumper. Similarly, the side view may show the misalignment between the bumper and the adjacent panel, while the top view may reveal surface dents or cracks. With this comprehensive image acquisition, the damage assessor can more accurately assess the extent of the damage, including those subtle damages that may be overlooked by a single angle.

[0062] Compared with traditional vehicle damage assessment methods, which usually rely on the experience of the assessor and may only focus on obvious damaged parts while ignoring potential hidden damage, the present invention ensures a comprehensive inspection of key inspection objects by requiring the capture of six basic views. This not only improves the accuracy of damage assessment, but also reduces the risk of repeated assessments or subsequent disputes due to insufficient information. In addition, the standardized shooting process also makes the assessment results between different assessors more consistent and comparable, further improving the fairness and reliability of the entire damage assessment process.

[0063] In some embodiments, the specific steps in step S3 further include: S32. Before step S31, it also includes the following steps: S321. Grouping all actual images that have been taken according to the assembly to which each component belongs, so that all actual images corresponding to all components belonging to the same assembly are classified into the same atlas; S322. According to the assembly to which the possibly damaged object belongs, try to identify the actual image containing the possibly damaged object in the corresponding atlas.

[0064] In order to more efficiently identify the actual images of potentially damaged objects, this embodiment mainly involves two key technical features: grouping the actual images by assembly parts, and identifying potentially damaged objects in the atlas of the corresponding assembly parts. These features play an important role in improving the efficiency of identifying the actual images of potentially damaged objects.

[0065] Specifically, grouping actual images by assembly can be achieved in a variety of ways. For example, the information of components belonging to the same assembly can be pre-stored based on the vehicle structure diagram or the component relationship database. When taking actual images, each image can be associated with the corresponding assembly through image recognition technology or manual annotation. Another method is to use image metadata to automatically record the shooting position or angle information when shooting, and then infer the assembly to which the image belongs based on this information.

[0066] When identifying potentially damaged objects in the atlas of the corresponding assembly, a variety of recognition algorithms can be used. For example, deep learning-based target detection algorithms such as YOLO or Faster R-CNN can be used. These algorithms have been specifically trained for vehicle parts and can quickly and accurately locate and identify parts in images. In addition, traditional computer vision techniques such as edge detection and shape matching can also be combined to improve recognition accuracy.

[0067] By grouping first, a more precise search range is provided for the subsequent recognition process, greatly reducing the number of images that need to be processed. This not only improves the recognition speed, but also reduces the probability of misidentification. For example, if a potentially damaged bumper belongs to the exterior assembly of the body, the system only needs to search in the atlas of the exterior assembly of the body, without having to process images of the engine compartment or interior.

[0068] In practical applications, this approach can significantly improve the efficiency and accuracy of the vehicle damage assessment process. For example, in an accident involving damage to the front bumper, the system may identify the front bumper as the key damaged object. It will then mark the parts adjacent to the front bumper, such as the headlights, radiator grille, etc., as possible damaged objects. When trying to identify actual images of these possible damaged objects, the system first groups all captured images by assembly parts. Since the front bumper, headlights, and radiator grille all belong to the front body assembly, the system will search in the atlas of the front body assembly without wasting time on other irrelevant images.

[0069] This approach not only improves recognition efficiency, but also helps adjusters more comprehensively assess vehicle damage. By systematically organizing and analyzing images, the risk of missing potentially damaged parts can be reduced, thereby improving the accuracy and completeness of damage assessments. For example, even if an adjuster may have overlooked small parts (such as fog lights or vehicle logos) adjacent to an obviously damaged front bumper, this approach can remind them to check these potentially overlooked parts.

[0070] In addition, this method has good scalability and adaptability. As vehicle models are updated and parts change, it can adapt to new vehicle models and parts configurations by simply updating the relational database of assemblies and parts. This flexibility enables the system to be continuously optimized and improved to adapt to changing vehicle technology and market needs.

[0071] Compared with traditional vehicle damage assessment methods, which mainly rely on the experience and subjective judgment of the assessor, and are easily limited by personal ability and attention, some damaged parts may be overlooked. The method of the present invention can more comprehensively and objectively assess vehicle damage through systematic image organization and intelligent recognition, reducing human errors and omissions. At the same time, this method can also improve the efficiency of the damage assessment process, reduce the workload of the assessor, and enable them to focus more on complex situations that require professional judgment.

[0072] The present invention optimizes the recognition process of the actual image of the potentially damaged object by rationally organizing and utilizing the existing image information, which not only improves the efficiency and accuracy of vehicle damage assessment, but also provides a more reliable basis for the insurance claims process, thus helping to improve the quality of the entire vehicle insurance service.

[0073] In some embodiments, the specific steps in step S1 include: S11. All damaged parts are classified into slightly damaged and severely damaged categories according to the degree of damage of each damaged part, and the severely damaged parts are regarded as the key damaged objects.

[0074] Regarding the problem of how to classify the damaged parts according to the degree of damage of the parts so as to carry out subsequent processing more targetedly, in this embodiment, by classifying the damaged parts according to the degree of damage into two categories, namely, light and severe, in actual application, severely damaged parts often have adjacent parts that are damaged in conjunction with them more generally. Therefore, the severely damaged category is taken as the key damaged object and the damage of other related parts is checked in subsequent steps to ensure that these parts that may cause other parts to be damaged in conjunction with them are treated first. The problem of how to classify and prioritize damaged parts is effectively solved. This classification method can help damage assessors identify parts that need to be focused on more quickly, thereby improving the efficiency of damage assessment. At the same time, this classification method provides a basis for the subsequent damage assessment process. For example, adjacent parts that may be damaged can be further identified according to the location of the key damaged object, thereby more comprehensively evaluating the damage of the vehicle. In addition, this classification method may also help improve the accuracy of damage assessment. By distinguishing between light and severe damage, damage assessors can adopt different assessment strategies for different degrees of damage, thereby more accurately estimating the cost and time of repair. In general, this classification method based on the degree of damage provides a systematic and targeted processing framework for vehicle damage assessment, which helps to improve the efficiency, accuracy and comprehensiveness of damage assessment.

[0075] In specific implementation, the present invention can use a variety of methods to determine the degree of damage to the parts. For example, the actual image of the parts can be analyzed by image analysis technology, and the degree of damage can be evaluated based on factors such as the damaged area, depth, and location. Another method is to combine a preset damage assessment model, which may take into account factors such as the importance of the parts, the difficulty of replacement, and the impact on the overall performance of the vehicle.

[0076] For the classification criteria of slight damage and severe damage, the present invention can set a threshold. For example, parts with a damage degree score of more than 70 points (out of 100 points) can be classified as severe damage, and parts with a damage degree score of 70 points or less can be classified as slight damage. This threshold can be adjusted according to actual conditions to adapt to different types of vehicles or different insurance policy requirements.

[0077] Combined with the above embodiments, significant technical effects can be achieved. For example, when identifying potentially damaged objects, priority is given to parts adjacent to severely damaged parts, so that inspections can be conducted more targeted and the efficiency and accuracy of damage assessment can be improved. At the same time, when generating reminder information, the priority and content of the reminder can be adjusted according to the degree of damage to the parts, ensuring that the damage assessor can pay more attention to severely damaged parts.

[0078] In practical applications, the method of the present invention can be implemented as follows: First, the damage assessment system analyzes the damage of each component of the vehicle through image recognition technology. The system uses a preset scoring model that takes into account factors such as the damaged area (percentage of the total area of ​​the component), the damage depth (surface scratches, dents or structural damage), and the importance of the component (the impact on the safety and functionality of the vehicle). Each factor has a weight, and ultimately a comprehensive score of 0-100 is obtained.

[0079] For example, the damage to a front bumper is as follows: Damaged area: 40% (score: 40 points; weight 0.3); Damage depth: deep depression (score: 70 points; weight 0.5); Component importance: medium (weight: 1.2); Comprehensive score calculation: (40 * 0.3 + 70 * 0.5) * 1.2 = 54 points Assuming the system sets the severe damage threshold at 60 points, the front bumper will be classified as slightly damaged. However, if the anti-collision beam behind the front bumper is also found to be deformed during the inspection, the system will re-evaluate and may raise the score of the front bumper to more than 60 points, thus classifying it as severely damaged and making it a key damaged object.

[0080] This classification method based on the degree of damage has significant advantages over traditional damage assessment methods. Traditional methods usually rely on the experience and judgment of the damage assessor, which is prone to subjective bias and omissions. The method of the present invention can more accurately and consistently assess the degree of damage to parts through an objective scoring system. In addition, by focusing on severely damaged parts as key damaged objects, the damage assessor can conduct more targeted in-depth inspections and assessments, thereby improving the accuracy and efficiency of damage assessment.

[0081] Compared with the prior art, the method of the present invention can not only solve the problem caused by the lack of experience of the damage assessor, but also provide a standardized damage assessment process. This method can reduce human errors and improve the consistency of damage assessment, while also providing insurance companies with more detailed and reliable loss assessment data. In addition, by distinguishing between minor and major damage, the method of the present invention can better allocate damage assessment resources, ensuring that important damages receive timely and adequate attention, thereby improving the efficiency and quality of the entire damage assessment process.

[0082] In some embodiments, the specific steps in step S11 include: S111. Take six basic views of the damaged vehicle as the damaged image of the damaged vehicle; S112. Determine the main damaged position of the damaged vehicle according to the damage image; S113. The damaged parts located in the main damaged direction shall be classified as severely damaged.

[0083] In actual applications, the situation where multiple parts are damaged together generally occurs in the area of ​​the vehicle that is most severely damaged. This embodiment more accurately determines the main damaged direction of the vehicle, thereby more effectively identifying severely damaged parts. This embodiment improves the accuracy of determining the main damaged direction by systematically collecting and analyzing vehicle damage information. By taking six basic views of the damaged vehicle as damage images, the damage to the vehicle can be fully captured. Judging the main damaged direction based on these damage images can more accurately locate the severely damaged area. Classifying the damaged parts located in the main damaged direction as severely damaged helps to more comprehensively and efficiently find all damaged parts in the area.

[0084] In specific implementation, the six basic views usually include the front view, rear view, left view, right view, top view and bottom view of the vehicle. These views can be obtained by shooting with a high-definition camera or professional vehicle inspection equipment. When shooting, it should be ensured that each view can clearly show the overall condition and details of the vehicle.

[0085] The process of determining the main damage location can be combined with image recognition technology and expert systems. For example, a deep learning algorithm can be used to analyze the damage image to identify the location, area and severity of the damaged area. At the same time, a rule-based expert system can be established to infer the most likely main damage location based on different types of accidents (such as rear-end collisions, side collisions, etc.) and damage characteristics.

[0086] In the process of classifying parts as severely damaged, some specific criteria can be set. For example, a comprehensive assessment can be made based on factors such as the location of the part in the main damaged direction (center or edge), the proportion of the damaged area (such as more than 50%), and the degree of damage (such as the severity of deformation). In addition, the importance and safety of the parts can also be considered. For example, key structural parts or safety system components may be prioritized in the severely damaged category.

[0087] For example, suppose a sedan is involved in a side collision. First, a camera is used to capture six basic views of the vehicle. When analyzing these images, the image recognition system finds that there is a significant dent and deformation in the right view, covering an area of ​​about 40% of the right body. At the same time, the system also detects that the right front door and the right rear door cannot be opened normally. Based on this information, the judgment system determines that the main damage is to the right side of the vehicle.

[0088] The system can then classify all damaged parts on the right side as severely damaged, or it can classify all damaged parts on the right side. For example, the right front door and right rear door are classified as severely damaged because they are severely deformed and cannot work properly. The right fender only has scratches on the surface and is not classified as severely damaged for the time being. Although the appearance of the right B-pillar is not obviously damaged, considering its importance to the structural strength of the vehicle, the system recommends that it be classified as severely damaged as well, and reminds you to conduct further structural inspections.

[0089] Compared with traditional vehicle damage assessment methods, traditional methods often rely on the subjective judgment of the assessor, which is easily limited by personal experience and observation angles and may overlook some imperceptible but important damaged parts. The present invention greatly improves the comprehensiveness and accuracy of damage assessment through systematic six-view shooting and intelligent analysis. In particular, when judging the main damaged position, the combination of multi-angle visual information and intelligent analysis system can more objectively and accurately determine the severely damaged areas.

[0090] In addition, the method of the present invention provides a more targeted inspection and repair strategy by classifying the components in the main damaged positions as severely damaged. This not only helps to improve the efficiency and quality of repairs, but also reduces the risk of missed inspections and misjudgments. For example, in the above-mentioned side collision case, even if the B-pillar has no obvious external damage, the system will recommend that it be classified as severely damaged. This approach can effectively prevent safety hazards caused by ignoring potential damage to key structural parts.

[0091] The method proposed in this invention significantly improves the accuracy and efficiency of vehicle damage assessment by combining comprehensive visual information collection and intelligent analysis and processing. It can not only more accurately determine the main damaged location, but also more effectively identify severely damaged parts, thereby providing a more reliable basis for subsequent repairs and claims, and ultimately improving the quality and efficiency of the entire vehicle damage assessment and repair process.

[0092] In some embodiments, the specific steps in step S112 include: S1121. Obtain the accident type of the damaged vehicle; S1122. Determine the main damaged location based on the damage image and accident type.

[0093] In order to more accurately determine the main damaged position of the vehicle, thereby improving the accuracy and efficiency of vehicle damage assessment, this embodiment involves two main technical features: obtaining the accident type of the damaged vehicle, and determining the main damaged position based on the damage image and accident type. Obtaining the accident type provides important background information for determining the main damaged position, and combining the damage image and accident type to determine the main damaged position can more comprehensively and accurately assess the damage to the vehicle.

[0094] In specific implementations, the accident type of the damaged vehicle can be obtained in a variety of ways. For example, the accident type can be inferred through manual input by the user, automatic extraction from the accident report of the insurance company, or intelligent analysis of the vehicle damage image. The accident type may include but is not limited to: rear-end collision, side collision, frontal collision, rollover, etc.

[0095] When determining the main damage location based on the damage image and accident type, the system can use machine learning algorithms, such as convolutional neural networks (CNN), to analyze the damage image. At the same time, the system will use a pre-trained model to predict the most likely damage location based on the accident type information. For example, for a rear-end collision, the system will focus on analyzing the damage to the front and rear of the vehicle; for a side collision, the system will pay more attention to the damage to the side of the vehicle.

[0096] This method, combined with the practice of taking six basic views of the vehicle in the previous step, can produce a significant synergistic effect. The six basic views provide all-round damage information of the vehicle, and the introduction of the accident type provides important clues for the interpretation of these images. This combination not only improves the accuracy of the judgment of the main damage orientation, but also helps to identify some minor damage that may be overlooked.

[0097] In actual applications, the system may use a probabilistic model to comprehensively consider the damage image and the accident type. For example, for a side collision accident, if the damage image shows obvious damage on the right side of the vehicle, the system may assign an 80% probability of damage to the right side, while also considering possible minor damage in other parts, such as a 20% probability of damage to the front. This probabilistic approach can more comprehensively reflect the damage to the vehicle and help the subsequent damage assessment process.

[0098] This method allows the system to more accurately identify the main damaged parts of the vehicle, which in turn helps to more accurately identify the severely damaged parts. This not only improves the accuracy of damage assessment, but also improves the efficiency of damage assessment, because the system can focus more specifically on the parts with the main damaged parts. In addition, this method may also help identify some damage that is not easy to detect. For example, certain types of accidents may cause seemingly minor but actually serious internal damage. By combining the accident type and damage image, the system may prompt to check these difficult-to-detect damage areas.

[0099] For example, suppose a car is involved in a side collision accident. First, the system automatically obtains the accident type of "side collision" through the insurance company's accident report. Then, the system analyzes the damage images of the six basic views that have been taken. Using the pre-trained CNN model, the system recognizes that the right door and the right fender have obvious dents and scratches. Combined with the accident type information of "side collision", the system further infers that the probability that the right side is the main damaged position is 90%. At the same time, the system also noticed that there were slight scratches on the front bumper. Considering that the side collision may cause a slight deflection of the vehicle, the system lists the front as a secondary damaged position with a probability of 30%.

[0100] Based on this information, the system automatically classifies the right-side parts such as the right door and right fender as severely damaged, and prompts the appraiser to focus on inspecting these parts. At the same time, the system also recommends inspecting the front bumper and related connecting structures to ensure that no potential damage is missed.

[0101] Compared with the prior art, traditional damage assessment methods mainly rely on the experience of the damage assessor, which is prone to subjective judgment bias or omission of certain inconspicuous damage. The present invention combines the accident type and damage image, and uses machine learning algorithms for objective analysis, which can not only more accurately determine the main damaged location, but also identify minor damage that may be overlooked. This method greatly reduces human errors and improves the accuracy and comprehensiveness of damage assessment. At the same time, because the system can quickly process and analyze information, the technical solution of the present invention also significantly improves the efficiency of damage assessment compared to traditional methods. This not only helps insurance companies to more accurately assess the amount of compensation, but also provides car owners with faster and fairer claims services.

[0102] Please refer to Figure 2 , Figure 2 A vehicle damage assessment reminder device in some embodiments of the present invention is integrated into a back-end control device in the form of a computer program, including: The judging module 100 is used to judge whether the component is damaged according to the actual image of the component, and to regard the damaged component as a key damaged object; The acquisition module 200 is used to acquire components adjacent to the location of the key damaged object and use them as possible damaged objects according to a preset database; The recognition module 300 is used to try to recognize the actual image containing the possibly damaged object from all the actual images that have been taken and use it as the target image, specifically including: S31. After attempting to identify the actual image containing the potentially damaged object, perform: S311. If the actual image containing the possibly damaged object cannot be identified, a first reminder message is generated to remind the user to take an actual image of the possibly damaged object; S312. If the actual image containing the potentially damaged object is successfully identified, whether the potentially damaged object is damaged is determined based on the target image, and when it is uncertain whether the potentially damaged object is damaged, a second reminder message is generated to remind the user to review the target image, and when it is determined that the potentially damaged object is damaged, the potentially damaged object is converted into a key damaged object.

[0103] In some embodiments, the recognition module 300 is used to try to recognize an actual image containing a potentially damaged object from all actual images that have been taken and use it as a target image, and when the actual image containing the potentially damaged object is successfully recognized and it is determined whether the potentially damaged object is damaged, perform the following: S3121. Potentially damaged objects that are uncertain whether they are damaged and are adjacent to multiple key damaged objects are taken as key inspection objects, and when key inspection objects appear, additional mandatory requirement information is generated; the mandatory requirement information includes information requiring users to take actual images of the key inspection objects at specified angles.

[0104] In some embodiments, the mandatory requirement information includes information requiring the user to capture six basic views of the key verification object as actual images of the key verification object.

[0105] In some embodiments, the recognition module 300 performs the following when trying to recognize an actual image containing a potentially damaged object as a target image among all actual images that have been captured: S32. Before step S31, it also includes the following steps: S321. Grouping all actual images that have been taken according to the assembly to which each component belongs, so that all actual images corresponding to all components belonging to the same assembly are classified into the same atlas; S322. According to the assembly to which the possibly damaged object belongs, try to identify the actual image containing the possibly damaged object in the corresponding atlas.

[0106] In some embodiments, the judging module 100 is used to judge whether a component is damaged according to an actual image of the component, and to take the damaged component as a key damaged object when performing: S11. All damaged parts are classified into slightly damaged and severely damaged categories according to the degree of damage of each damaged part, and the severely damaged parts are regarded as the key damaged objects.

[0107] In some embodiments, the judgment module 100 is used to classify all the damaged components into slightly damaged and severely damaged categories according to the damage degree of each damaged component, and to regard the severely damaged components as the key damaged objects when: S111. Take six basic views of the damaged vehicle as the damaged image of the damaged vehicle; S112. Determine the main damaged position of the damaged vehicle according to the damage image; S113. The damaged parts located in the main damaged direction shall be classified as severely damaged.

[0108] In some embodiments, when the judgment module 100 is used to judge the main damaged position of the damaged vehicle according to the damage image, it executes: S1121. Obtain the accident type of the damaged vehicle; S1122. Determine the main damaged location based on the damage image and accident type.

[0109] Please refer to Figure 3 , Figure 3 The present invention provides a structural diagram of an electronic device provided by an embodiment of the present invention. The present invention provides an electronic device 13, including: a processor 1301 and a memory 1302. The processor 1301 and the memory 1302 are interconnected and communicate with each other through a communication bus 1303 and / or other forms of connection mechanisms (not shown). The memory 1302 stores computer-readable instructions executable by the processor 1301. When the electronic device is running, the processor 1301 executes the computer-readable instructions to execute the vehicle damage assessment reminder method in any optional implementation of the above embodiment to achieve the following functions: determine whether the component is damaged according to the actual image of the component, and use the damaged component as the key damaged object; obtain the vehicle damage assessment reminder method in any optional implementation of the above embodiment according to the preset database. Taking parts adjacent to the position of the key damaged object as possible damaged objects; trying to identify the actual image containing the possible damaged object among all the actual images that have been taken and using it as the target image, specifically including: after trying to identify the actual image containing the possible damaged object, executing: if the actual image containing the possible damaged object cannot be identified, generating a first reminder message for reminding the user to take an actual image of the possible damaged object; if the actual image containing the possible damaged object is successfully identified, judging whether the possible damaged object is damaged according to the target image, and generating a second reminder message for reminding the user to review the target image when it is uncertain whether the possible damaged object is damaged; and converting the possible damaged object into a key damaged object when it is determined that the possible damaged object is damaged.

[0110] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the vehicle damage assessment reminder method in any optional implementation of the above-mentioned embodiment is executed to achieve the following functions: judging whether a component is damaged according to an actual image of the component, and treating the damaged component as a key damaged object; obtaining components adjacent to the position of the key damaged object according to a preset database and treating them as possible damaged objects; attempting to identify an actual image containing a possible damaged object among all actual images that have been taken and treating it as a target image, specifically comprising: after attempting to identify an actual image containing a possible damaged object, executing: if the actual image containing the possible damaged object cannot be identified, generating a first reminder message for reminding a user to take a photo of the actual image of the possible damaged object; if the actual image containing the possible damaged object is successfully identified, judging whether the possible damaged object is damaged according to the target image, and generating a second reminder message for reminding the user to review the target image when it is uncertain whether the possible damaged object is damaged, and converting the possible damaged object into a key damaged object when it is determined that the possible damaged object is damaged.

[0111] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0112] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0113] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] Furthermore, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0115] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0116] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A vehicle damage assessment reminder method, characterized in that: The following steps are involved: S1. judging whether the component is damaged according to the actual image of the component, and taking the damaged component as the key damaged object; S2. According to a preset database, parts adjacent to the location of the key damaged object are obtained as possible damaged objects; S3. Among all the actual images that have been taken, try to identify the actual image containing the potentially damaged object and use it as the target image, specifically including: S31. After attempting to identify the actual image containing the potentially damaged object, executing: S311. If the actual image containing the potentially damaged object cannot be identified, generating a first reminder message for reminding the user to take a photo of the actual image of the potentially damaged object; S312. If the actual image containing the potentially damaged object is successfully identified, whether the potentially damaged object is damaged is determined based on the target image, and when it is uncertain whether the potentially damaged object is damaged, a second reminder message is generated to remind the user to review the target image, and when it is determined that the potentially damaged object is damaged, the potentially damaged object is converted into the key damaged object.

2. The vehicle damage assessment reminder method according to claim 1, characterized in that: The specific steps in step S312 include: S3121. Potentially damaged objects that are uncertain whether they are damaged and are adjacent to multiple key damaged objects are taken as key inspection objects, and when the key inspection objects appear, additional mandatory requirement information is generated; the mandatory requirement information includes information requiring the user to take an actual image of the key inspection object at a specified angle.

3. The vehicle damage assessment reminder method according to claim 2, characterized in that: The mandatory requirement information includes information requiring the user to take six basic views of the key verification object as actual images of the key verification object.

4. The vehicle damage assessment reminder method according to claim 1, characterized in that: The specific steps in step S3 also include: S32. Before step S31, it also includes the following steps: S321. Grouping all the actual images that have been taken according to the assembly to which each component belongs, so that all the actual images corresponding to all components belonging to the same assembly are classified into the same atlas; S322. According to the assembly to which the possibly damaged object belongs, attempt to identify an actual image containing the possibly damaged object in the corresponding atlas.

5. The vehicle damage assessment reminder method according to claim 1, characterized in that: The specific steps in step S1 include: S11. Classify all damaged parts into slightly damaged and severely damaged categories according to the degree of damage of each damaged part, and regard the parts belonging to the severely damaged category as the key damaged objects.

6. The vehicle damage assessment reminder method according to claim 5, characterized in that: The specific steps in step S11 include: S111. Take six basic views of the damaged vehicle as the damaged image of the damaged vehicle; S112. Determine the main damaged position of the damaged vehicle according to the damage image; S113. Classify the damaged parts located at the main damaged position into the severely damaged category.

7. The vehicle damage assessment reminder method according to claim 6, characterized in that: The specific steps in step S112 include: S1121. Obtain the accident type of the damaged vehicle; S1122. Determine the main damaged direction according to the damage image and the accident type.

8. A vehicle damage assessment reminder device, characterized in that: include: A judgment module, used to judge whether the component is damaged according to the actual image of the component, and to regard the damaged component as a key damaged object; An acquisition module, used for acquiring parts adjacent to the location of the key damaged object as possible damaged objects according to a preset database; The recognition module is used to try to recognize the actual image containing the potentially damaged object from all the actual images that have been taken and use it as the target image, specifically including: S31. After attempting to identify the actual image containing the potentially damaged object, executing: S311. If the actual image containing the potentially damaged object cannot be identified, generating a first reminder message for reminding the user to take a photo of the actual image of the potentially damaged object; S312. If the actual image containing the potentially damaged object is successfully identified, whether the potentially damaged object is damaged is determined based on the target image, and when it is uncertain whether the potentially damaged object is damaged, a second reminder message is generated to remind the user to review the target image, and when it is determined that the potentially damaged object is damaged, the potentially damaged object is converted into the key damaged object.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the vehicle damage assessment reminder method as described in any one of claims 1 to 7 are executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps in the vehicle damage assessment reminder method as described in any one of claims 1-7 are executed.

Images