Car insurance claim settlement photo intelligent classification method and device, computer equipment and medium

Abstract

The embodiment of the invention belongs to the field of artificial intelligence, and relates to a vehicle insurance claim settlement photo intelligent classification method, which comprises the steps of obtaining a vehicle insurance claim settlement photo and preprocessing the vehicle insurance claim settlement photo; performing feature extraction on the preprocessed vehicle insurance claim settlement photo according to a pre-trained deep learning model to obtain vehicle damage information; and classifying the vehicle damage information according to a pre-trained A I identification model to obtain a classification result. The invention further provides a car insurance claim settlement photo intelligent classification device, computer equipment and a storage medium. According to the method, the vehicle damage information is classified according to the pre-trained AI recognition model, the classification result is obtained, automatic classification of the claim photos is realized, and the efficiency and accuracy of claim auditing are improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device, computer equipment and storage medium for intelligent classification of automobile insurance claim photos. Background Art

[0002] In the process of car insurance claims, claims adjusters usually need to take a large number of photos of the accident scene as the basis for claims. However, due to the tight time limit for on-site operations, claims adjusters often do not classify these photos in detail, resulting in subsequent auditors having to search for relevant information from hundreds or thousands of unclassified photos when conducting loss assessment, which is inefficient and prone to errors. Summary of the invention

[0003] The purpose of the embodiments of the present application is to propose a method, device, computer equipment and storage medium for intelligent classification of motor vehicle insurance claim photos, the main purpose of which is to achieve automatic classification of claim photos and improve the efficiency and accuracy of claim review.

[0004] In order to solve the above technical problems, the embodiment of the present application provides a method for intelligent classification of auto insurance claim photos, which adopts the following technical solutions:

[0005] A method for intelligently classifying car insurance claim photos, comprising the following steps:

[0006] Obtaining car insurance claim photos and pre-processing the car insurance claim photos;

[0007] Extracting features from the pre-processed auto insurance claim photos according to a pre-trained deep learning model to obtain vehicle damage information;

[0008] The vehicle damage information is classified according to a pre-trained AI recognition model to obtain a classification result.

[0009] Furthermore, the step of obtaining the car insurance claim photos and preprocessing the car insurance claim photos specifically includes:

[0010] Obtaining a car insurance claim photo, and performing pre-processing of format conversion and size adjustment on the car insurance claim photo to obtain a processed car insurance claim photo.

[0011] Furthermore, the step of extracting features from the pre-processed auto insurance claim photos according to the pre-trained deep learning model to obtain vehicle damage information specifically includes:

[0012] establishing at least one identification task for vehicle identification;

[0013] According to the recognition task, a convolutional neural network (CNN) is used to train the car insurance claim photos to obtain a pre-trained deep learning model;

[0014] The pre-trained deep learning model corresponding to the recognition task is used to extract features from the pre-processed auto insurance claim photos to obtain information on vehicle components, damage types, and damage levels in the auto insurance claim photos.

[0015] Furthermore, before classifying the vehicle damage information according to the pre-trained AI recognition model, the method further includes:

[0016] Obtaining a data set of car insurance claim photos, wherein the car insurance claim photos include label information, and the label information is used to indicate vehicle damage;

[0017] Predefine classification rules for car insurance claim photos;

[0018] The data set is trained using a convolutional neural network and according to the classification rules to obtain the pre-trained AI recognition model.

[0019] Furthermore, after the step of classifying the vehicle damage information according to the pre-trained AI recognition model to obtain the classification result, the method further includes:

[0020] A conditional search column is set, and the classification result is output according to the input or selection in the conditional search column.

[0021] Furthermore, after the step of setting a conditional search column and viewing the classification results according to the input or selection in the conditional search column, the step further includes:

[0022] Analyze the classification results to determine whether there are any car insurance claim photos that cannot be classified;

[0023] If there are any car insurance claim photos that cannot be classified, the car insurance claim photos will be marked as pending review;

[0024] Assign a classification type to the automobile insurance claim photos in the pending review state and add additional annotations.

[0025] Furthermore, after the step of specifying the classification type and supplementing the annotation for the pending auto insurance claim photos, the method further includes:

[0026] Collect the specified classification type and add the marked auto insurance claim photos in the pending review state according to the set time;

[0027] Adding the automobile insurance claim photos of the specified classification type and supplementary annotations in the pending review state to the data set of the automobile insurance claim photos;

[0028] The pre-trained AI recognition model is retrained and updated with the updated data set of the auto insurance claim photos.

[0029] In order to solve the above technical problems, the embodiment of the present application also provides a device for intelligent classification of auto insurance claim photos, which adopts the following technical solution:

[0030] An intelligent classification device for automobile insurance claim photos, comprising:

[0031] An acquisition module, used to acquire and pre-process the car insurance claim photos;

[0032] A feature extraction module, used to extract features from the pre-processed car insurance claim photos to obtain vehicle damage information;

[0033] The classification module is used to classify the vehicle damage information according to the pre-trained AI recognition model to obtain a classification result.

[0034] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0035] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the above-mentioned method for intelligent classification of automobile insurance claim photos are implemented.

[0036] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0037] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the method for intelligent classification of automobile insurance claim photos as described above.

[0038] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects: the present application first obtains the auto insurance claim photos and pre-processes the auto insurance claim photos; then, according to the pre-trained deep learning model, the pre-processed auto insurance claim photos are feature extracted to obtain vehicle damage information, including vehicle parts, damage type, damage degree and other information; finally, according to the pre-trained AI recognition model, the vehicle damage information is classified to obtain the classification results, thereby realizing the automatic classification of claim photos and improving the efficiency and accuracy of claim review. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0041] Figure 2 A flowchart of an embodiment of a method for intelligent classification of auto insurance claim photos according to the present application;

[0042] Figure 3 yes Figure 2 A flowchart of a specific implementation of step S202;

[0043] Figure 4 yes Figure 2 A flowchart of an implementation method before step S203;

[0044] Figure 5 It is a structural schematic diagram of an embodiment of an intelligent classification device for automobile insurance claim photos according to the present application;

[0045] Figure 6 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0047] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0049] like Figure 1As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0050] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0051] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture E perts Group Audio Layer III, dynamic image expert compression standard audio layer 3), MP4 (Moving Picture E perts Group Audio Layer IV, dynamic image expert compression standard audio layer 4) players, laptop computers and desktop computers, etc.

[0052] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0053] It should be noted that the method for intelligent classification of car insurance claim photos provided in the embodiment of the present application is generally executed by a server, and accordingly, the device for intelligent classification of car insurance claim photos is generally set in a terminal device.

[0054] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0055] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for intelligently classifying car insurance claim photos according to the present application. The method for intelligently classifying car insurance claim photos comprises the following steps:

[0056] Step S201, obtaining a car insurance claim photo and pre-processing the car insurance claim photo.

[0057] In this embodiment, the electronic device (eg Figure 1 shown server ) can be connected by wire or by wireless connection. It should be noted that the above wireless connection methods may include but are not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMA auto insurance claim photo intelligent classification connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0058] Ways to obtain car insurance claims photos include photos taken on-site by the claims adjuster, photos taken by the repair shop, photos uploaded by partners, photos uploaded by customers themselves, etc.

[0059] In this embodiment, preprocessing the auto insurance claim photos specifically includes: preprocessing the auto insurance claim photos by format conversion and size adjustment to obtain processed auto insurance claim photos, which are adapted to the needs of the AI ​​recognition model.

[0060] Step S202: extract features from the pre-processed car insurance claim photos according to the pre-trained deep learning model to obtain vehicle damage information.

[0061] Continue to refer Figure 3 In some optional implementations of this embodiment, step S202 specifically includes:

[0062] Step S2021, establishing at least one identification task for vehicle identification.

[0063] Step S2022: According to the recognition task, a convolutional neural network (CNN) is used to train the car insurance claim photos to obtain a pre-trained deep learning model.

[0064] Step S2023, using the pre-trained deep learning model corresponding to the recognition task to extract features from the pre-processed auto insurance claim photos, to obtain information such as vehicle parts, damage types, and damage extent in the auto insurance claim photos.

[0065] In this embodiment, the pre-trained deep learning model can be obtained by training the car insurance claim photos using a convolutional neural network (CNN).

[0066] It should be noted that the recognition task can be a customized or preset task such as body damage recognition, part recognition, etc. When the recognition task is body damage recognition, step S2022 is a deep learning model trained based on the body damage picture. When the recognition task is part recognition, step S2022 is a deep learning model trained based on the part picture.

[0067] In this embodiment, one recognition task can be used for feature extraction to obtain information such as vehicle components, damage types, and damage degree information. In order to improve recognition accuracy, multiple pre-trained deep learning models can be used to perform joint feature extraction processing on different recognition tasks (such as vehicle body damage recognition and parts recognition).

[0068] Step S203: classify the vehicle damage information according to the pre-trained AI recognition model to obtain a classification result.

[0069] Continue to refer Figure 4 In some optional implementations of this embodiment, before classifying the vehicle damage information according to the pre-trained AI recognition model, the method further includes:

[0070] Step S2031, obtaining a data set of car insurance claim photos, wherein the car insurance claim photos include label information, and the label information is used to indicate vehicle damage, such as "left front door scratches", "front bumper cracks", etc.

[0071] Step S2032, pre-define classification rules for car insurance claim photos, such as classification by damage type (body scratches, dents, breakage, etc.), by vehicle parts (headlights, front bumpers, rearview mirrors, engine compartment, etc.).

[0072] Step S2033, using a convolutional neural network and training the data set according to the classification rules to obtain the pre-trained AI recognition model.

[0073] In some optional implementations of this embodiment, after step S203, the following steps may also be performed:

[0074] A conditional search column is set, and the classification result is output according to the input or selection in the conditional search column.

[0075] This step can provide an intuitive user interface to facilitate auditors to view classification results, and supports quick search and viewing of classification results by classification labels, time, location, and other conditions.

[0076] In some optional implementations of this embodiment, after setting a conditional search column and outputting the classification result according to the input or selection in the conditional search column, the following steps may also be performed:

[0077] Analyze the classification results to determine whether there are any auto insurance claim photos that cannot be classified.

[0078] If there are any auto insurance claim photos that cannot be classified, the auto insurance claim photos will be marked as pending review.

[0079] Assign a classification type to the automobile insurance claim photos in the pending review state and add additional annotations.

[0080] Specifically, the car insurance claim photos in the pending review status can be manually assigned classification types and then supplemented with annotations.

[0081] In this embodiment, anomaly detection can be implemented to notify the auditor for further processing.

[0082] In some optional implementations of this embodiment, after specifying the classification type and supplementing the annotation for the pending auto insurance claim photos, the following steps may be performed:

[0083] Collect the specified classification type and add the marked auto insurance claim photos in the pending review status according to the set time.

[0084] Adding the automobile insurance claim photos of the specified classification type and supplementary annotations in the pending review state to the data set of the automobile insurance claim photos;

[0085] The pre-trained AI recognition model is retrained and updated with the updated data set of the auto insurance claim photos.

[0086] Through this step, the classification rules and AI recognition models can be continuously optimized to improve their accuracy and recognition precision.

[0087] This application first obtains auto insurance claim photos and pre-processes the auto insurance claim photos; then extracts features of the pre-processed auto insurance claim photos according to a pre-trained deep learning model to obtain vehicle damage information, including vehicle parts, damage type, damage degree and other information; finally, classifies the vehicle damage information according to the pre-trained AI recognition model to obtain classification results, thereby realizing automatic classification of claim photos and improving the efficiency and accuracy of claim review.

[0088] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0089] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0091] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0092] Further references Figure 5 , as a response to the above Figure 2 The present application provides an embodiment of a device for intelligently classifying car insurance claim photos. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0093] like Figure 5 As shown, the vehicle insurance claim photo intelligent classification device 500 described in this embodiment includes: an acquisition module 501, a feature extraction module 502, and a classification module 503. Among them:

[0094] The acquisition module 501 is used to acquire the car insurance claim photos and pre-process the car insurance claim photos.

[0095] The feature extraction module 502 is used to extract features from the pre-processed car insurance claim photos to obtain vehicle damage information.

[0096] The classification module 503 is used to classify the vehicle damage information according to the pre-trained AI recognition model to obtain a classification result.

[0097] This application first obtains the car insurance claim photos through the acquisition module 501 and pre-processes the car insurance claim photos; then the feature extraction module 502 extracts features from the pre-processed car insurance claim photos according to the pre-trained deep learning model to obtain vehicle damage information, including vehicle parts, damage type, damage degree and other information; finally, the classification module 503 classifies the vehicle damage information according to the pre-trained AI recognition model to obtain classification results, thereby realizing automatic classification of claim photos and improving the efficiency and accuracy of claim review.

[0098] To solve the above technical problems, the present application also provides a computer device. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0099] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 6 with components 61-63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0100] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0101] The memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D auto insurance claim photo intelligent classification memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as a hard disk or memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 61 can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions of the auto insurance claim photo intelligent classification method, etc. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0102] The processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to run computer-readable instructions stored in the memory 61 or process data, such as computer-readable instructions for running the method for intelligent classification of auto insurance claim photos.

[0103] The network interface 63 may include a wireless network interface or a wired network interface. The network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0104] This application first obtains auto insurance claim photos and pre-processes the auto insurance claim photos; then extracts features of the pre-processed auto insurance claim photos according to a pre-trained deep learning model to obtain vehicle damage information, including vehicle parts, damage type, damage degree and other information; finally, classifies the vehicle damage information according to the pre-trained AI recognition model to obtain classification results, thereby realizing automatic classification of claim photos and improving the efficiency and accuracy of claim review.

[0105] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned method for intelligent classification of car insurance claim photos.

[0106] This application first obtains auto insurance claim photos and pre-processes the auto insurance claim photos; then extracts features of the pre-processed auto insurance claim photos according to a pre-trained deep learning model to obtain vehicle damage information, including vehicle parts, damage type, damage degree and other information; finally, classifies the vehicle damage information according to the pre-trained AI recognition model to obtain classification results, thereby realizing automatic classification of claim photos and improving the efficiency and accuracy of claim review.

[0107] The non-Company software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

[0108] 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 application, 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, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0109] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for intelligent classification of auto insurance claim photos, characterized in that: The steps include: Obtaining car insurance claim photos and pre-processing the car insurance claim photos; Extracting features from the pre-processed auto insurance claim photos according to a pre-trained deep learning model to obtain vehicle damage information; The vehicle damage information is classified according to a pre-trained AI recognition model to obtain a classification result.

2. The method for intelligent classification of auto insurance claim photos according to claim 1 is characterized in that: The step of obtaining the car insurance claim photos and preprocessing the car insurance claim photos specifically includes: Obtaining a car insurance claim photo, and performing pre-processing of format conversion and size adjustment on the car insurance claim photo to obtain a processed car insurance claim photo.

3. The method for intelligent classification of auto insurance claim photos according to claim 1 is characterized in that: The step of extracting features from the pre-processed auto insurance claim photos according to the pre-trained deep learning model to obtain vehicle damage information specifically includes: establishing at least one identification task for vehicle identification; According to the recognition task, a convolutional neural network (CNN) is used to train the car insurance claim photos to obtain a pre-trained deep learning model; The pre-trained deep learning model corresponding to the recognition task is used to extract features from the pre-processed auto insurance claim photos to obtain information on vehicle components, damage types, and damage levels in the auto insurance claim photos.

4. The method for intelligent classification of auto insurance claim photos according to claim 1 is characterized in that: Before classifying the vehicle damage information according to the pre-trained AI recognition model, the method further includes: Obtaining a data set of car insurance claim photos, wherein the car insurance claim photos include label information, and the label information is used to indicate vehicle damage; Predefine classification rules for car insurance claim photos; The data set is trained using a convolutional neural network and according to the classification rules to obtain the pre-trained AI recognition model.

5. The method for intelligent classification of auto insurance claim photos according to claim 4 is characterized in that: After the step of classifying the vehicle damage information according to the pre-trained AI recognition model to obtain the classification result, the method further includes: A conditional search column is set, and the classification result is output according to the input or selection in the conditional search column.

6. The method for intelligent classification of auto insurance claim photos according to claim 5 is characterized in that: After the step of setting the conditional search column and viewing the classification results according to the input or selection in the conditional search column, the step further includes: Analyze the classification results to determine whether there are any car insurance claim photos that cannot be classified; If there are any car insurance claim photos that cannot be classified, the car insurance claim photos will be marked as pending review; Assign a classification type to the automobile insurance claim photos in the pending review state and add additional annotations.

7. The method for intelligent classification of auto insurance claim photos according to claim 6 is characterized in that: After the step of specifying the classification type and supplementing the annotation for the pending auto insurance claim photos, the method further includes: Collect the specified classification type and add the marked auto insurance claim photos in the pending review state according to the set time; Adding the automobile insurance claim photos of the specified classification type and supplementary annotations in the pending review state to the data set of the automobile insurance claim photos; The pre-trained AI recognition model is retrained and updated with the updated data set of the auto insurance claim photos.

8. An intelligent classification device for car insurance claim photos, characterized in that: include: An acquisition module, used to acquire and pre-process the car insurance claim photos; A feature extraction module, used to extract features from the pre-processed car insurance claim photos to obtain vehicle damage information; The classification module is used to classify the vehicle damage information according to the pre-trained AI recognition model to obtain a classification result.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the method for intelligent classification of automobile insurance claim photos according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the method for intelligent classification of auto insurance claim photos according to any one of claims 1 to 7.

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