Vehicle insurance claim repair reservation method and device, computer device and storage medium

Abstract

The application relates to the technical field of artificial intelligence, and discloses a vehicle insurance claim maintenance reservation method and device, computer equipment and a storage medium, which comprise the following steps: obtaining a maintenance service point image submitted by a target claim object for a vehicle insurance claim; performing position recognition on the maintenance service point image to obtain maintenance service point position information; filtering a preset repairer database based on the maintenance service point position information to obtain a target recommended repairer, and recommending the target recommended repairer to the target claim object; and in response to a maintenance confirmation request of the target recommended repairer by the target claim object, performing maintenance reservation based on the target claim object and the target recommended repairer to obtain a target reservation form. The application can be applied to a vehicle insurance claim maintenance reservation scene of financial technology, and improves the reliability of vehicle insurance claim maintenance reservation.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence technology and natural language processing technology, and is applicable to the financial technology field. In particular, it relates to a method, device, computer equipment and storage medium for car insurance claim reporting and repair appointment. Background Technology

[0002] Car insurance claim repair appointment refers to the service process where, after a vehicle accident, the insurance company guides the car owner to pre-arrange the repair time, location, and services with a partner repair shop, and then guides the vehicle to be taken to the designated or partner repair shop for repairs. Currently, two main methods are used to determine repair shop information: The first method involves the car owner manually entering the repair shop's name or address into the insurance application, but in practice, this is prone to errors such as homophones or abbreviations. The second method uses optical character recognition (OCR) technology to analyze screenshots or map images uploaded by the car owner to extract text information such as the repair shop's name and address. However, OCR technology can only output the character coordinates and text elements in the image, lacking a deep understanding of interface layout and spatial semantics. It struggles to distinguish target business names, street names, surrounding shop signs, or interfering controls, resulting in noisy extraction results and low accuracy. Neither of these methods can accurately determine the repair shop, affecting the reliability of car insurance claim repair appointments. Summary of the Invention

[0003] This application provides a method, device, computer equipment, and storage medium for making car insurance claim and repair appointments, in order to solve the technical problem that manual input is prone to input errors and optical character recognition is difficult to accurately extract repair shop information, which leads to the inability to accurately determine the repair shop and thus affects the reliability of car insurance claim and repair appointments.

[0004] Firstly, it provides a method for reporting car insurance claims and scheduling repairs, including: Obtain images of repair service points submitted by the target claimant in connection with a car insurance claim; The location information of the maintenance service point is obtained by performing location recognition on the image of the maintenance service point; Based on the location information of the repair service points, a preset database of repair service providers is filtered to obtain target recommended repair service providers, and the target recommended repair service providers are recommended to the target reporting object; In response to the repair confirmation request from the target reporting party to the target recommended repairer, a repair appointment is made based on the target reporting party and the target recommended repairer, resulting in a target appointment form.

[0005] Secondly, a vehicle insurance claim reporting and repair appointment device is provided, including: The image acquisition module is used to acquire images of repair service points submitted by the target claimant in connection with a car insurance claim. The location recognition module is used to perform location recognition on the image of the maintenance service point to obtain the location information of the maintenance service point. The repair service provider screening module is used to screen a preset repair service provider database based on the location information of the repair service point, obtain target recommended repair service providers, and recommend the target recommended repair service providers to the target reporting object; The appointment form generation module is used to respond to the repair confirmation request from the target reporting object to the target recommended repairer, and to make a repair appointment based on the target reporting object and the target recommended repairer to obtain a target appointment form.

[0006] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned vehicle insurance claim reporting and repair appointment method.

[0007] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-mentioned vehicle insurance claim reporting and repair appointment method.

[0008] The aforementioned solution, implemented using the car insurance claim reporting and repair appointment method, device, computer equipment, and storage medium, can be applied to car insurance claim reporting and repair appointment scenarios in fintech. This solution first acquires images of repair service points submitted by the target claimant for their car insurance claim, eliminating the need for users to manually input address information, reducing operational costs and avoiding input errors. Next, it performs location recognition on the repair service point images, automatically extracting accurate location information and improving positioning efficiency and accuracy. Furthermore, based on the repair service point location information, it filters a pre-set repair service provider database to obtain target recommended repair services, which are then recommended to the target claimant, achieving rational allocation and proximity-based scheduling of repair resources. Finally, in response to the target claimant's repair confirmation request to the target recommended repair service provider, a repair appointment is made based on the target claimant and the target recommended repair service provider, resulting in a target appointment form. This completes the fully automated closed loop from claim reporting to appointment, reducing manual intervention and significantly improving the overall efficiency and reliability of car insurance claim repair appointments. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1This is a schematic diagram of an application environment for the vehicle insurance claim reporting and repair appointment method in one embodiment of this application; Figure 2 This is a flowchart illustrating a method for reporting car insurance claims and making repair appointments in one embodiment of this application; Figure 3 yes Figure 2 A schematic diagram of a specific implementation method for step S20; Figure 4 yes Figure 2 A schematic diagram of a specific implementation method for step S30; Figure 5 yes Figure 4 A flowchart illustrating a specific implementation of step S32; Figure 6 yes Figure 4 A flowchart illustrating a specific implementation of step S33; Figure 7 yes Figure 2 A schematic diagram of a specific implementation of step S40; Figure 8 This is a schematic diagram of the structure of a vehicle insurance claim reporting and repair appointment device according to one embodiment of this application; Figure 9 This is a schematic diagram of the structure of a computer device according to one embodiment of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] First, let's analyze some of the terms used in this application: Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0013] Natural Language Processing (NLP): NLP uses computers to process, understand, and utilize human language (such as Chinese and English). It is a branch of artificial intelligence and an interdisciplinary field of computer science and linguistics, often referred to as computational linguistics. NLP includes syntactic analysis, semantic analysis, and discourse understanding. It is commonly used in machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, intent recognition, information extraction and filtering, text classification and clustering, sentiment analysis, and opinion mining. It involves data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computation.

[0014] Information extraction is a text processing technique that extracts factual information such as entities, relationships, and events from natural language text and outputs it as structured data. Information extraction is a technique for extracting specific information from text data. Text data is composed of specific units, such as sentences, paragraphs, and chapters. Text information is composed of smaller, specific units, such as characters, words, phrases, sentences, paragraphs, or combinations of these units. Extracting noun phrases, names of people, and place names from text data is an example of text information extraction. Of course, text information extraction techniques can extract information of various types.

[0015] Car insurance claim reporting and repair appointment refers to the service process where, after a vehicle accident, the insurance company guides the car owner to pre-arrange the repair time, location, and services with a partner repair shop, and then guides the vehicle to be taken to the designated or partner repair shop for repairs. This effectively shortens waiting time and rationally allocates repair resources. The car owner must truthfully describe the accident, cooperate with the damage assessment, select a repair shop, and go to the shop for repairs at the agreed time, retaining repair invoices, damage assessment reports, and other materials for subsequent claims processing.

[0016] Currently, two main methods are used to determine repair shop information: The first method involves car owners manually entering the repair shop's name or address into the insurance application. However, in practice, car owners are forced to go through a cumbersome process of "memorizing content - switching applications - manually entering / searching," which easily leads to input errors such as homophones or abbreviations, hindering subsequent dispatch and settlement. The second method uses optical character recognition (OCR) technology to analyze screenshots or map images uploaded by car owners to extract text information such as the repair shop's name and address. However, OCR technology can only output the character coordinates and text elements in the image, lacking a deep understanding of interface layout and spatial semantics. It struggles to distinguish target businesses, street names, surrounding shop signs, or interfering controls, resulting in noisy extraction results and low accuracy. Neither of these methods can accurately determine the repair shop, affecting the reliability of car insurance claims and repair appointments.

[0017] Based on this, the embodiments of this application provide a method, device, computer equipment and storage medium for making an appointment for vehicle insurance claims and repairs, in order to solve the technical problem that manual input is prone to input errors and optical character recognition is difficult to accurately extract repair shop information, which leads to the inability to accurately determine the repair shop and thus affects the reliability of vehicle insurance claims and repair appointments.

[0018] The vehicle insurance claim reporting and repair appointment method, device, computer equipment, and storage medium provided in this application embodiment are specifically described through the following embodiments. First, the vehicle insurance claim reporting and repair appointment method in this application embodiment is described.

[0019] The vehicle insurance claim reporting and repair appointment method provided in this application embodiment can be applied to, for example, Figure 1In this application environment, the client communicates with the server via a network. The server can obtain images of repair service points submitted by the target claimant for car insurance claims through the client; perform location recognition on the repair service point images to obtain the location information of the repair service points; filter the preset repair service provider database based on the repair service point location information to obtain target recommended repair providers, and recommend the target recommended repair providers to the target claimant; respond to the target claimant's repair confirmation request to the target recommended repair provider, make a repair appointment based on the target claimant and the target recommended repair provider, obtain a target appointment form, and feed the target appointment form back to the client. In this application, it can be used in the car insurance claim repair appointment scenario of fintech. This solution first obtains the images of repair service points submitted by the target claimant for car insurance claims, eliminating the need for users to manually enter address information, reducing operational costs and avoiding input errors. Then, it performs location recognition on the repair service point images, automatically extracting accurate repair service point location information, improving positioning efficiency and accuracy. Furthermore, based on the location information of repair service points, a pre-set database of repair service providers is filtered to obtain target recommended repair providers, which are then recommended to the target claimant, achieving reasonable allocation and nearby scheduling of repair resources. Finally, in response to the target claimant's request for repair confirmation from the target recommended repair provider, a repair appointment is made based on the target claimant and the target recommended repair provider, generating a target appointment form. This completes the fully automated closed loop from claim reporting to appointment, reducing manual intervention and significantly improving the overall efficiency and reliability of auto insurance claim repair appointments.

[0020] The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The following detailed description uses specific embodiments to illustrate this application.

[0021] Please see Figure 2 As shown, Figure 2 A flowchart illustrating the vehicle insurance claim reporting and repair appointment method provided in this application embodiment includes the following steps: S10. Obtain images of repair service points submitted by the target claimant in connection with the auto insurance claim; It should be noted that a car insurance claim report is a claim submitted by the car owner or policyholder to the insurance company through the insurance company's app, mini-program, or other client-side channels for accidents such as collisions and scratches. The target of the report refers to the car owner or policyholder who initiates a car insurance claim report through the insurance company's app, mini-program, or other client-side channels after a car insurance accident.

[0022] Repair service point images are pictures containing repair service location information uploaded by the target reporting party to the insurance company's application, mini-program, or other client. These repair service point images can include photos of repair shop (brand 4S store, general repair shop, fast repair chain store) signs, screenshots of map navigation interfaces, etc.

[0023] Repair service points are places that can provide damage assessment and repair services for vehicles, including brand 4S stores, comprehensive repair shops, and fast repair chain stores.

[0024] Understandably, replacing the traditional manual address input with images can reduce user operating costs and avoid positioning errors caused by unclear address descriptions or input errors such as homophones or abbreviations. At the same time, image information is more intuitive and richer, providing a high-quality data foundation for subsequent automated location recognition, which helps to reduce the user's operating threshold and operating time.

[0025] S20. Perform location recognition on the image of the maintenance service point to obtain the location information of the maintenance service point; It's important to understand that location recognition here refers to the process of using technologies such as OCR text recognition and image landmark matching to identify text (such as store names and address signs) in images and automatically extract their geographical location, thus improving positioning efficiency and accuracy. For example, Figure 3 As shown, step S20, which involves location recognition of the service point image to obtain service point location information, includes the following steps: S21. Perform category recognition on the images of the maintenance service points to obtain image category recognition information; wherein, the image category recognition information is used to characterize whether the images of the maintenance service points are valid or invalid images; S22. If the image category recognition information indicates that the image of the maintenance service point is a valid image, perform image cleaning on the image of the maintenance service point to obtain the original image; S23. Extract the location entities from the original image to obtain the location information of the maintenance service point.

[0026] For steps S21 to S24, the service point images are first classified to obtain image category recognition information that indicates whether the service point image is a valid or invalid image. This allows for early filtering out invalid data, preventing subsequent steps from wasting computational resources on invalid images and improving overall processing efficiency. If the image category recognition information indicates that the service point image is a valid image, the image is cleaned to obtain a high-quality original image. Then, entity extraction is performed on the original image to obtain the service point location information, effectively ensuring the accuracy and robustness of the location information extraction.

[0027] Specifically, models such as convolutional neural networks (e.g., ResNet, Vision Transformer) can be used to encode the input maintenance service point image layer by layer. This compresses the visual information of the maintenance service point image, such as color, texture, shape, and text, into a structured numerical vector, resulting in image embedding features that can compactly represent the semantic content of the image. Next, an image classification model is used to classify the image embedding features, calculating the probability that the feature vector belongs to either the "valid image" or "invalid image" category. The higher probability is then taken as the image category identification information.

[0028] It should be noted that the image category recognition information is used to characterize whether the image of the repair service point is a valid image or an invalid image. Invalid images may be images unrelated to the repair service point, such as casual photos or photos of vehicle damage, while valid images are images that contain information about the repair service location.

[0029] If the image category recognition information indicates that the image of the repair service point is invalid, the server rejects the image and sends a prompt message (such as "Please upload a screenshot of the map navigation") to the client to prompt the target reporting party to re-upload the image.

[0030] When the image category recognition information indicates that the maintenance service point image is a valid image, the maintenance service point image is cleaned, including but not limited to performing denoising (such as Gaussian filtering to remove noise), deblurring (such as sharpening), watermark / occlusion removal, automatic cropping, etc. The final output is a clean and high-quality original image, which provides high-quality image input for subsequent entity extraction, thereby improving the accuracy of location information extraction.

[0031] Next, the original image is localized and entity extracted using a pre-trained image-text fusion perception model to obtain the maintenance service point information. It should be noted that this image-text fusion perception model is a pre-trained multimodal large-scale model that supports information extraction from images based on prompts.

[0032] For example, the prompt words could be: Identify "location icons" in images (such as red positioning pins and blue dots on a map). Identify "text labels" in images (such as repair shop names, address text, distance prompts, etc.). Analyze the spatial mapping relationship between location icons and text labels (e.g., a location icon points to a name label).

[0033] By setting prompts based on actual application scenarios, ambiguity can be eliminated and irrelevant information (such as road condition color blocks, advertisements for nearby shops, and UI control text such as "back" and "refresh") can be removed. This enables accurate and reliable acquisition of the location information of repair service points, directly supporting subsequent business processes such as store navigation and dispatching in repair appointments, thereby improving the user experience and operational efficiency of car insurance repair appointment services.

[0034] Understandably, if the image category recognition information indicates that the image of the repair service point is a valid image, the location entity extraction of the repair service point image can be performed to obtain the location information of the repair service point, which may include the address information of the reported repair service point and the business name of the reported repair service point.

[0035] The address information of the reported repair service point is a textual description of the address of the reported repair service point, such as "No. xx, xx Road, xx District, xx City, xx Province". The business name of the reported repair service point is the textual commercial name used by the reported repair service point in business registration or system filing, such as "Zhang San Repair Shop".

[0036] S30. Based on the location information of maintenance service points, filter the preset database of repair service providers to obtain the target recommended repair service providers, and recommend the target recommended repair service providers to the target reporting object; It should be noted that the repair service provider database is a pre-established database of repair companies by the insurance company on the server side. The repair service provider database includes multiple registered repair service providers. Registered repair service providers are qualified repair companies that have been registered and filed in the insurance company's repair service provider database. Each registered repair service provider includes fields such as the name, business name, address, latitude and longitude, qualification level, cooperation status, user rating, and service radius of the 4S store / repair shop.

[0037] It's important to understand that the screening process here refers to searching the insurance company's repair service provider database for all registered repair providers within the specified range, based on the location information of the repair service point, and then identifying the most suitable target repair provider.

[0038] Among them, such as Figure 4 As shown, step S30, which involves filtering the preset repair service provider database based on the repair service point location information to obtain the target recommended repair service provider, includes the following steps: S31. Convert the address information of the reported repair service point to latitude and longitude to obtain the raw latitude and longitude data; Specifically, geocoding APIs (such as map interfaces) can be called to parse the address information of the reported repair service point into longitude and latitude values ​​in the Earth coordinate system, thereby transforming the unstructured text address into structured numerical coordinates, providing a unified and accurate data foundation for subsequent spatial distance calculations, and avoiding matching errors caused by differences in address descriptions.

[0039] S32. Based on the original latitude and longitude data, multiple registered repair contractors are screened to obtain candidate repair contractors; It's important to understand that the filtering here refers to screening registered repair contractors based on their original latitude and longitude coordinates, retaining those within a preset radius constraint. This quickly eliminates contractors that are too far away, significantly narrowing the candidate pool and improving matching efficiency. For example, Figure 5 As shown, step S32, which involves filtering multiple registered repair contractors based on the original latitude and longitude data to obtain candidate repair contractors, includes the following steps: S321. Generate repair location range data based on the original latitude and longitude data and preset radius constraints; S322. Based on the repair location range data, the registered location information of each registered repair contractor is filtered to obtain candidate location information; S323. Based on the alternative location information, multiple registered repair contractors are screened to obtain alternative repair contractors.

[0040] For steps S321 to S323, firstly, repair location range data is generated based on the original latitude and longitude data and preset radius constraints, limiting the search space to a controllable geographical area and avoiding computational redundancy caused by full traversal. Next, based on the repair location range data, the registered location information of each registered repair service provider is filtered to obtain candidate location information, quickly eliminating registered repair service providers outside the repair location range data, significantly reducing the amount of data required for subsequent matching. Finally, based on the candidate location information, the corresponding registered repair service providers are selected, resulting in candidate repair service providers, significantly improving filtering efficiency and system response speed.

[0041] It should be noted that the preset radius constraint refers to the distance threshold parameter that the insurance company sets in advance based on the actual operating conditions, which is used to limit the search range, such as 5 kilometers or 10 kilometers.

[0042] Using the original latitude and longitude coordinates as the center coordinates, combined with the preset radius constraint, a circular search area is constructed through a geofencing generation algorithm, and the output is structured repair location range data.

[0043] Next, the registration location information of each registered repair service provider is obtained. This registration location information consists of the latitude and longitude coordinates pre-registered by each registered repair service provider in the database. Based on the defined repair location range data, information within this range is retrieved from the registration location information of all registered repair service providers to obtain candidate location information. This allows for rapid initial filtering of repair service providers, directly eliminating those that do not meet the distance criteria at the location information level, significantly reducing the number of repair service providers that need to be processed subsequently and improving screening efficiency.

[0044] Finally, using the alternative location information as an index, the registered repair service providers corresponding to the alternative location information are filtered from all registered repair service providers. This allows for a reverse association with the complete records in the repair service provider database (including fields such as the name, business name, address, latitude and longitude, qualification level, cooperation status, user rating, and service radius of the 4S store / repair shop). The preliminary selected alternative repair service providers are then presented in a list format.

[0045] S33. Filter the alternative repair service providers based on the reported repair service point name to obtain the target recommended repair provider.

[0046] It's important to understand that the filtering here refers to matching the reported repair service point's name with the names of potential repair providers, retaining those that match and eliminating mismatches. This ensures that the recommended results strictly align with the user's actual desired service point brand, improving recommendation accuracy and user trust. For example, Figure 6 As shown, step S33, which involves filtering the candidate repair service providers based on the reported repair service point name to obtain the target recommended repair provider, includes the following steps: S331. Perform feature transformation on the contractor's business name of the candidate contractors to obtain the contractor's business name features; S332. Perform feature conversion on the business name of the reported repair service point to obtain the business name feature of the reported repair service point; S333. Calculate the similarity between the business characteristics of the repair contractor and the business characteristics of the reported repair service point to obtain feature similarity data; S334. Based on feature similarity data, filter the candidate repair contractors to obtain the target recommended repair contractor.

[0047] For steps S331 to S334, firstly, the business names of the candidate repair service providers are feature-transformed to obtain the repair service provider business name features. Secondly, the business names of the reported repair service points are feature-transformed to obtain the reported repair service point business name features, mapping the unstructured text business names into a unified numerical feature vector. Next, similarity calculations are performed on the repair service provider business name features and the reported repair service point business name features to obtain feature similarity data. This data can measure the degree of matching between the two at the semantic level, effectively identifying repair service providers with abbreviations, aliases, or slight differences in wording but actually pointing to the same brand. Finally, based on the feature similarity data, the candidate repair service providers are filtered to obtain the target recommended repair service provider. This ensures that the final recommendation result is highly consistent with the service point brand expected by the user, significantly improving recommendation accuracy and avoiding misrecommendations or omissions due to incomplete matching of business names.

[0048] First, the business names of potential repair service providers are obtained. These business names are the textual representations of the commercial names used by the potential repair providers in their business registration or system filings. Next, natural language processing techniques (such as Word2Vec and BERT word vector models) are used to convert the business names into high-dimensional numerical feature vectors, yielding the repair service provider's business name features. Similarly, natural language processing techniques (such as Word2Vec and BERT word vector models) are used to convert the reported repair service point's business name into a high-dimensional numerical feature vector, yielding the reported repair service point's business name features. This eliminates matching obstacles caused by differences in text length and format, ensuring that the reported service point's business name and the repair provider's business name are expressed in the same feature space. This avoids spatial misalignment problems caused by inconsistent encoding models and provides a standardized and comparable data foundation for subsequent similarity calculations.

[0049] Next, similarity calculation algorithms (such as cosine similarity, Euclidean distance, etc.) are used to calculate the similarity between the characteristics of the repair contractor's business name and the characteristics of the reported repair service point's business name to obtain feature similarity data. Feature similarity data is a quantitative score, which is usually in the range of [0,1]. The larger the value, the closer the two are semantically.

[0050] Finally, the data with the highest value is selected from all feature similarity data to obtain the maximum similarity data. Based on the maximum similarity data, the corresponding repair contractor is selected from the candidate repair contractors and recommended as the target repair contractor.

[0051] Understandably, by first defining the area based on latitude and longitude, similar alternative repair shops are initially screened. Then, by calculating the similarity at the semantic level of the business names, repair shops with subtle differences in literal expression (such as parentheses, abbreviations, and word order adjustments) but actually pointing to the same service point can be effectively identified. This overcomes the rigidity of traditional exact string matching, effectively solves the problem of converting non-standard corpora into standard entities, significantly reduces the cost of manual data cleaning, greatly improves the fault tolerance and recall rate of matching, ensures that car owners are accurately guided to the desired repair service point, and improves the appointment success rate and user satisfaction.

[0052] In other embodiments, a preset similarity threshold can be set to filter out data with feature similarity greater than or equal to the threshold, obtaining matching similarity data. Then, based on this matching similarity data, a corresponding repairman is selected from the candidate repairmen as the target recommended repairman. The number of target recommended repairmen is at least one, and when recommending them to the target reporting party, they are displayed in descending order of similarity data.

[0053] After identifying the target recommended repairman, the target recommended repairman is sent to the client's client terminal to achieve the purpose of recommending the target recommended repairman to the target reported party.

[0054] At the same time, the target complainant is shown various information about the recommended repairman, such as address, distance (straight-line distance or road network distance between the target complainant and the recommended repairman), and user ratings of the repairman.

[0055] Understandably, after the recommended repairman is sent to the client's client to recommend the repairman, if the client approves of the recommended repairman, the client will click the "confirmation control" on the client and enter the repair appointment time, thus forming a repair confirmation request. The repair confirmation request includes the repair appointment time entered by the client, which refers to the specific date and time period that the client expects to go to the repair location for vehicle repair, usually accurate to the hour.

[0056] S40. In response to the repair confirmation request from the target reporting party to the target recommended repairer, a repair appointment is made based on the target reporting party and the target recommended repairer, and a target appointment form is obtained.

[0057] Specifically, after receiving a repair confirmation request from the target reporting party for the recommended repair service provider, the server responds to the repair confirmation request by scheduling a repair appointment and generating a target appointment form.

[0058] It's important to understand that the repair appointment here refers to automatically linking the user information of the target claimant with the repair appointment time and the repairer's information to generate a complete appointment form. This helps improve the efficiency of car insurance claims repair appointments, reduce manual communication costs, and enhance user experience and claims processing time.

[0059] Among them, such as Figure 7 As shown, step S40, which involves scheduling a repair appointment based on the target reporting party and the target recommended repair provider, and obtaining a target appointment form, includes the following steps: S41. Obtain user information of the target reporting party; S42. Obtain vehicle insurance claim information; S43. Obtain the repair information of the recommended repair contractor; S44. Determine the repair appointment time based on the repair confirmation request; S45. Based on user information, car insurance claim information, repairman information, and repair appointment time, fill in the fields of the preset form template to obtain the target appointment form.

[0060] For steps S41 to S45, firstly, user information of the target claimant, vehicle insurance claim information, and repairman information of the recommended repairman are obtained. Key data required for repair appointments are comprehensively integrated from three dimensions: the claimant, accident details, and the repairman. Next, the repair appointment time is determined based on the repair confirmation request, accommodating the target claimant's personalized schedule and improving appointment flexibility. Finally, based on user information, vehicle insurance claim information, repairman information, and repair appointment time, the pre-set form template is populated with fields to obtain the target appointment form. This reduces errors and time consumption caused by manual data entry, automates and standardizes the repair appointment process, and significantly improves appointment efficiency and information accuracy.

[0061] It should be noted that the user information of the target claimant is data related to the target claimant's identity and vehicle, including but not limited to name, ID number, mobile phone number, policy number, license plate number, vehicle brand and model, and vehicle identification number. Specifically, this user information can be automatically collected from the insurance company's core business system and user center by calling the insurance company's data interface. For example, the system can automatically retrieve all of Zhang San's insurance and vehicle information from the core system using the policy number. Automatically obtaining user information avoids users manually filling in information repeatedly, reduces the error rate of data entry, and provides an accurate data foundation for subsequent automatic form filling, thus improving appointment efficiency.

[0062] Car insurance claim information refers to recorded accident details, including the time and location of the accident, accident type (rear-end collision / minor collision / single-vehicle accident, etc.), description of the accident, damaged parts and extent, on-site photos, and liability determination report. This information is proactively uploaded by the claimant when filing a car insurance claim. Automatic linking to existing claim information ensures the accuracy of accident details in the appointment form, allowing repair shops to understand the damage in advance and prepare parts and workstations, reducing the need for secondary confirmation upon arrival at the shop.

[0063] The repair service provider information is field information related to the target recommended repair service provider obtained from the repair service provider database, including but not limited to fields such as the name, business name, address, latitude and longitude, qualification level, cooperation status, user rating, and service radius of the 4S store / repair shop.

[0064] The repair appointment time is the time entered by the target reporting party when generating the repair confirmation request. It indicates the specific date and time period that the target reporting party expects to go to the repair location for vehicle repair.

[0065] The preset form template refers to a pre-defined standardized repair appointment form structure, which includes fixed fields (such as vehicle owner's name, license plate number, accident time, repair shop name, appointment time, contact number, etc.) and their arrangement format.

[0066] Furthermore, user information, car insurance claim information, repairman information, and repair appointment time are filled into the corresponding fields of the preset form template one by one to obtain the target appointment form. The target appointment form refers to the repair appointment form generated after all fields are filled in, which is complete in content and formatted in a standardized manner. It can be used by the target claimant for confirmation, the repairman for order acceptance, and the insurance company for filing.

[0067] Understandably, by using templates for automatic filling, multi-source heterogeneous data can be integrated into a standardized form, completely replacing manual filling and significantly reducing error rates and communication costs. At the same time, the unified form structure facilitates quick confirmation and transfer among insurance companies, repair shops, and users, significantly improving the overall efficiency and standardization of repair appointments.

[0068] Understandably, after the target recommended repairman is sent to the client of the target reporting object to recommend the target recommended repairman, if the target reporting object does not approve of the target recommended repairman, the target reporting object will click the "rejection control" on the client, thus forming a repair rejection request. The repair rejection request is an operation signal that the target reporting object does not accept the initially recommended target recommended repairman, such as clicking the "change to another one" button on the client.

[0069] In some embodiments, after step S30, that is, after filtering the preset repair service provider database based on the repair service point location information to obtain the target recommended repair service provider and recommending the target recommended repair service provider to the target reporting object, the method further includes: Step 1: In response to the target reporting party's request to refuse repairs to the recommended repair service provider, obtain the current location latitude and longitude of the target reporting party; Step 2: Generate current positioning range data based on the current latitude and longitude and the preset radius constraints; Step 3: Based on the current location range data, filter the registration location information of each registered repair contractor to obtain candidate location information; Step 4: Filter multiple registered repair contractors based on candidate location information to obtain candidate repair contractors, and recommend candidate repair contractors to the target reporting party.

[0070] Specifically, after receiving a repair refusal request from the target reporting party against the recommended repairman, the server responds to the repair refusal request and recommends a new repairman.

[0071] With the permission of the target reporting party, the client calls the location function to obtain the location information of the target reporting party's current location, and records it in the form of latitude and longitude coordinates. This allows for accurate perception of the target reporting party's true location, providing a basis for decision-making in subsequent secondary recommendations and avoiding blind recommendations that could lead to a decline in user experience.

[0072] Based on the current location's latitude and longitude as the center coordinates and combined with preset radius constraints, a circular search area is constructed using a geofencing generation algorithm, and the output is structured data of the current location range.

[0073] Next, the registration location information of each registered repair contractor is obtained. This registration location information consists of the latitude and longitude coordinates pre-registered by each contractor in the database. Based on the defined current location range data, information within this range is retrieved from the registration location information of all registered repair contractors to obtain candidate location information. This allows for rapid initial filtering of repair contractors, directly eliminating those that do not meet the distance criteria at the location information level, significantly reducing the number of repair contractors that need to be processed subsequently and improving screening efficiency.

[0074] Finally, using the candidate location information as an index, the registered repair service providers corresponding to the candidate location information are filtered from all registered repair service providers. This process then links back to the complete records in the repair service provider database (including fields such as the name, business name, address, latitude and longitude, qualification level, cooperation status, user rating, and service radius of the 4S store / repair shop), presenting the initially filtered candidate repair service providers in a list format. Each candidate repair service provider must have at least one such provider.

[0075] After obtaining at least one candidate repairman, the candidate repairmen can be sorted by distance (the straight-line distance or road network distance between the target reporting object and each candidate repairman) or by the repairman's user rating. The sorted repairmen are then sent to the target reporting object's client to recommend candidate repairmen to the target reporting object.

[0076] After this, the target reporting party can generate a repair confirmation request or a repair rejection request to the candidate repairman. If it is a repair confirmation request, the process will proceed to the specific implementation methods shown in steps S40, S41 to S45 above. If it is a repair rejection request, the process will proceed to the specific implementation methods shown in steps one to four above. These will not be described again here.

[0077] It should be noted that existing repair appointment systems are typically unidirectional and linear. When a customer is dissatisfied with the recommended repair shop, the appointment process immediately terminates, and the server cannot dynamically adjust based on the context. However, the car insurance claim repair appointment method provided in this application allows the server to switch to recommendation mode when the customer rejects the recommended repair shop. This proactively pushes nearby high-quality repair shops, ensuring process continuity, automatically handling traffic and converting it into new sales opportunities, effectively improving the conversion rate and satisfaction of motor vehicle repair appointments.

[0078] As can be seen, the above solution can be applied to the car insurance claim and repair appointment scenario in fintech. First, it acquires images of repair service points submitted by the claimant for car insurance claims, eliminating the need for users to manually input address information, reducing operational costs and avoiding input errors. Next, it performs location recognition on the repair service point images, automatically extracting accurate location information and improving positioning efficiency and accuracy. Furthermore, based on the repair service point location information, it filters a pre-set repair service provider database to obtain recommended repair services and recommends them to the claimant, achieving reasonable allocation and proximity-based scheduling of repair resources. Finally, in response to the claimant's repair confirmation request from the recommended repair service provider, it schedules a repair appointment based on the claimant and the recommended repair service provider, generating a target appointment form. This completes the fully automated closed loop from claim to appointment, reducing manual intervention and significantly improving the overall efficiency and reliability of car insurance claim and repair appointments.

[0079] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0080] In one embodiment, a vehicle insurance claim and repair appointment device is provided, which corresponds one-to-one with the vehicle insurance claim and repair appointment method described in the above embodiments. For example... Figure 8 As shown, the car insurance claim reporting and repair appointment device includes an image acquisition module 101, a location recognition module 102, a repair shop screening module 103, and an appointment form generation module 104. Detailed descriptions of each functional module are as follows: Image acquisition module 101 is used to acquire images of repair service points submitted by the target reporting object in connection with the car insurance claim; Location recognition module 102 is used to perform location recognition on the image of the maintenance service point to obtain the location information of the maintenance service point; The repair service provider screening module 103 is used to screen the preset repair service provider database based on the location information of the repair service point, obtain the target recommended repair service provider, and recommend the target recommended repair service provider to the target reporting object; The appointment form generation module 104 is used to respond to the repair confirmation request from the target reporting object to the target recommended repairer, and to make a repair appointment based on the target reporting object and the target recommended repairer to obtain the target appointment form.

[0081] In one embodiment, the location identification module 102 is specifically used for: The images of the maintenance service points are classified to obtain image category identification information; the image category identification information is used to characterize whether the images of the maintenance service points are valid or invalid images. If the image category recognition information indicates that the image of the maintenance service point is a valid image, the image of the maintenance service point is cleaned to obtain the original image; The original image is used to extract the location of entities and obtain the location information of the maintenance service point.

[0082] In one embodiment, the contractor screening module 103 is specifically used for: The address information of the reported repair service point is converted to latitude and longitude to obtain the raw latitude and longitude data; Based on the original latitude and longitude data, multiple registered repair contractors were screened to obtain candidate repair contractors; The candidate repair service providers are filtered based on the business name of the reported repair service point to obtain the target recommended repair service provider.

[0083] In one embodiment, the contractor screening module 103 is specifically used for: The repair location range data is generated based on the original latitude and longitude data and the preset radius constraints; Based on the repair location range data, the registered location information of each registered repair contractor is filtered to obtain candidate location information; Based on the alternative location information, multiple registered repair contractors were screened to obtain alternative repair contractors.

[0084] In one embodiment, the contractor screening module 103 is specifically used for: The characteristics of the contractor's business name of the candidate repair contractor are transformed to obtain the characteristics of the contractor's business name; The features of the reported repair service point business name are transformed to obtain the features of the reported repair service point business name; Similarity calculations were performed on the characteristics of the repair contractor's business name and the characteristics of the reported repair service point's business name to obtain feature similarity data; Based on feature similarity data, candidate repair contractors are filtered to obtain the target recommended repair contractor.

[0085] In one embodiment, the appointment form generation module 104 is specifically used for: Obtain user information of the target reporting party; Obtain car insurance claim information; Obtain the repairman information of the recommended repairman; Determine the repair appointment time based on the repair confirmation request; The target appointment form is obtained by filling in the fields of the preset form template based on user information, car insurance claim information, repair provider information, and repair appointment time.

[0086] In one embodiment, the vehicle insurance claim reporting and repair appointment device further includes: The current latitude and longitude acquisition module is used to obtain the current location latitude and longitude of the target reporting object in response to the target reporting object's request to refuse repairs to the target recommended repairer; The current positioning range generation module is used to generate current positioning range data based on the current positioning latitude and longitude and preset radius constraints; The location filtering module is used to filter the registered location information of each registered repair contractor based on the current location range data to obtain candidate location information; The repair service provider filtering module is used to filter multiple registered repair service providers based on candidate location information, obtain candidate repair service providers, and recommend candidate repair service providers to the target reporting party.

[0087] This application provides a vehicle insurance claim and repair appointment device, applicable to fintech vehicle insurance claim and repair appointment scenarios. The solution first acquires images of repair service points submitted by the claimant for their vehicle insurance claim, eliminating the need for manual address input by the user, reducing operational costs and avoiding input errors. Next, it performs location recognition on the repair service point images, automatically extracting precise location information, improving positioning efficiency and accuracy. Furthermore, based on the repair service point location information, it filters a pre-set repair service provider database to obtain recommended repair services, which are then recommended to the claimant, achieving rational allocation and proximity-based scheduling of repair resources. Finally, in response to the claimant's repair confirmation request from the recommended repair service provider, a repair appointment is made based on the claimant and the recommended repair service provider, generating a target appointment form. This completes the fully automated closed loop from claim to appointment, reducing manual intervention and significantly improving the overall efficiency and reliability of vehicle insurance claim and repair appointments.

[0088] Specific limitations regarding the vehicle insurance claim and repair appointment device can be found in the above section on the limitations of the vehicle insurance claim and repair appointment method, and will not be repeated here. Each module in the aforementioned vehicle insurance claim and repair appointment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0089] Please see Figure 9 , Figure 9 The hardware structure of a computer device according to another embodiment is illustrated. The computer device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the vehicle insurance claim reporting and repair appointment method of the embodiments of this application, including: Obtain images of repair service points submitted by the target claimant in connection with a car insurance claim; The location information of the maintenance service point is obtained by performing location identification on the image of the maintenance service point; Based on the location information of maintenance service points, a pre-set database of repair service providers is filtered to obtain target recommended repair service providers, and these recommended repair service providers are then recommended to the target reporting party. In response to the target reporting party's request for repair confirmation from the target recommended repairman, a repair appointment is made based on the target reporting party and the target recommended repairman, resulting in a target appointment form.

[0090] The 903 input / output interface is used to implement information input and output. The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0091] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Obtain images of repair service points submitted by the target claimant in connection with a car insurance claim; The location information of the maintenance service point is obtained by performing location identification on the image of the maintenance service point; Based on the location information of maintenance service points, a pre-set database of repair service providers is filtered to obtain target recommended repair service providers, and these recommended repair service providers are then recommended to the target reporting party. In response to the target reporting party's request for repair confirmation from the target recommended repairman, a repair appointment is made based on the target reporting party and the target recommended repairman, resulting in a target appointment form.

[0092] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0093] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0094] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0095] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0096] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for reporting car insurance claims and scheduling repairs, characterized in that, The method includes: Obtain images of repair service points submitted by the target claimant in connection with a car insurance claim; The location information of the maintenance service point is obtained by performing location recognition on the image of the maintenance service point; Based on the location information of the repair service points, a preset database of repair service providers is filtered to obtain target recommended repair service providers, and the target recommended repair service providers are recommended to the target reporting object; In response to the repair confirmation request from the target reporting party to the target recommended repairer, a repair appointment is made based on the target reporting party and the target recommended repairer, resulting in a target appointment form.

2. The method for reporting vehicle insurance claims and scheduling repairs according to claim 1, characterized in that, The step of performing location recognition on the image of the repair service point to obtain the location information of the repair service point includes: The images of the repair service points are subjected to category recognition to obtain image category recognition information; wherein, the image category recognition information is used to characterize whether the images of the repair service points are valid or invalid images; If the image category recognition information indicates that the maintenance service point image is a valid image, the maintenance service point image is cleaned to obtain the original image; The original image is used to extract the location of entities, thereby obtaining the location information of the maintenance service point.

3. The method for reporting vehicle insurance claims and scheduling repairs according to claim 1, characterized in that, The location information of the repair service point includes the address information and business name of the repair service point that was reported for repair; the repair contractor database includes multiple registered repair contractors. The step of filtering a pre-set database of repair service providers based on the location information of the repair service points to obtain target recommended repair providers includes: The address information of the reported repair service point is converted to latitude and longitude to obtain the raw latitude and longitude data; Based on the original latitude and longitude data, multiple registered repair contractors are screened to obtain candidate repair contractors; The candidate repair service providers are filtered based on the reported repair service point names to obtain the target recommended repair provider.

4. The method for reporting vehicle insurance claims and scheduling repairs according to claim 3, characterized in that, The process of filtering multiple registered repair contractors based on the original latitude and longitude data to obtain candidate repair contractors includes: Based on the original latitude and longitude data and the preset radius constraints, the repair location range data is generated; Based on the repair location range data, the registration location information of each of the registered repair contractors is filtered to obtain candidate location information; Based on the candidate location information, multiple registered repair contractors are screened to obtain the candidate repair contractors.

5. The method for reporting vehicle insurance claims and scheduling repairs according to claim 3, characterized in that, The process of filtering the candidate repair service providers based on the reported repair service point's business name to obtain the target recommended repair provider includes: The contractor name of the candidate repair contractor is transformed to obtain the contractor name feature; The business name of the reported repair service point is transformed to obtain the business name feature of the reported repair service point; Similarity calculations are performed on the characteristics of the repair contractor's business name and the characteristics of the reported repair service point's business name to obtain feature similarity data; Based on the feature similarity data, the candidate repair contractors are filtered to obtain the target recommended repair contractor.

6. The method for reporting vehicle insurance claims and scheduling repairs according to claim 3, characterized in that, After filtering a preset repair service provider database based on the location information of the repair service points to obtain target recommended repair providers, and recommending the target recommended repair providers to the target reporting party, the method further includes: In response to the target reporting object's request to refuse repairs to the target recommended repairer, the current location latitude and longitude of the target reporting object are obtained; The current positioning range data is generated based on the current positioning latitude and longitude and the preset radius constraint; Based on the current location range data, the registration location information of each of the registered repair contractors is filtered to obtain candidate location information; Based on the candidate location information, multiple registered repair service providers are filtered to obtain candidate repair service providers, and the candidate repair service providers are recommended to the target reporting object.

7. The method for reporting vehicle insurance claims and scheduling repairs according to any one of claims 1 to 6, characterized in that, The process of making a repair appointment based on the target reporting party and the target recommended repair provider, resulting in a target appointment form, includes: Obtain the user information of the target reporting object; Obtain the vehicle insurance claim information from the aforementioned vehicle insurance claim report; Obtain the repairer information of the target recommended repairer; The repair appointment time will be determined based on the repair confirmation request. The target appointment form is obtained by filling in the fields of the preset form template based on the user information, the car insurance claim information, the repair service provider information, and the repair appointment time.

8. A vehicle insurance claim reporting and repair appointment device, characterized in that, The device includes: The image acquisition module is used to acquire images of repair service points submitted by the target claimant in connection with a car insurance claim. The location recognition module is used to perform location recognition on the image of the maintenance service point to obtain the location information of the maintenance service point. The repair service provider screening module is used to screen a preset repair service provider database based on the location information of the repair service point, obtain target recommended repair service providers, and recommend the target recommended repair service providers to the target reporting object; The appointment form generation module is used to respond to the repair confirmation request from the target reporting object to the target recommended repairer, and to make a repair appointment based on the target reporting object and the target recommended repairer to obtain a target appointment form.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle insurance claim reporting and repair appointment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle insurance claim reporting and repair appointment method as described in any one of claims 1 to 7.

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