Data processing method and device, equipment and storage medium

Abstract

The embodiment of the application provides a data processing method, device and equipment and a storage medium, the method comprises the following steps: receiving a claim request of a pet from an object corresponding to the pet, wherein the claim request comprises pet image data and a proof file; determining a reasonable value of the claim request according to the pet image data and the proof file; if the reasonable value is greater than a first threshold value, determining an accident loss result of the pet according to the proof file. By using the embodiment of the application, a reasonable loss event can be identified, and the accuracy of the accident loss can be improved.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to a data processing method, apparatus, device, and storage medium. Background Technology

[0002] With economic development, more and more people are keeping pets, and more and more people are purchasing pet insurance. When a pet becomes sick or has an accident, users can upload photos of their pet and supporting documents, such as diagnostic certificates, lab reports, medical records, and expense invoices, to apply for compensation from the insurance company. The claims personnel then verify the identity of the pet in the photos. Once the pet's identity is confirmed, the damage is assessed based on the information recorded in the supporting documents, and then the claim is processed. If a fake pet photo exists, identity verification may pass. If fake supporting documents exist, damage assessment may be achieved. Therefore, how to verify the authenticity of pet photos and supporting documents is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0003] This application provides a data processing method, apparatus, device, and storage medium that can identify reasonable damage assessment events, thereby improving the accuracy of accident damage assessment.

[0004] This application provides a data processing method, comprising: receiving a pet's claim request from a corresponding object, the claim request including pet image data and supporting documents; determining a reasonable value for the claim request based on the pet image data and supporting documents; and if the reasonable value is greater than a first threshold, determining the pet's accident damage assessment result based on the supporting documents. Thus, by identifying reasonable damage assessment events based on the pet image data and supporting documents carried in the claim request, the accuracy of accident damage assessment can be improved.

[0005] In some possible examples, methods for determining the reasonable value of a claim based on pet image data and supporting documents include: determining a first state characteristic of the pet based on the pet image data, and determining a second state characteristic of the pet based on the supporting documents; and determining the reasonable value of the claim based on the matching value between the first and second state characteristics. It can be understood that the larger the matching value between the first and second state characteristics, the greater the likelihood that the pet image data and supporting documents correspond to the same event. Therefore, determining the reasonable value of the claim based on the matching value between the first state characteristics determined from the pet image data and the second state characteristics determined from the supporting documents can improve the accuracy of identifying reasonable damage assessment events.

[0006] In some possible examples, methods for determining a pet's first state characteristics based on pet image data include: performing feature recognition on the pet image data to obtain multiple feature point subsets, including a nose feature point subset; obtaining pet information based on the nose feature point subset, including historical state data; and performing state recognition on the multiple feature point subsets based on the historical state data to obtain the pet's first state characteristics. In this way, state recognition can be performed on multiple feature point subsets corresponding to the pet image data based on the pet's historical state data, improving the accuracy of state characteristic recognition.

[0007] In some possible examples, a method for determining the pet's second state characteristics based on the supporting documentation includes: identifying a set of text vectors from the supporting documentation based on its file type; and determining the pet's second state characteristics based on the text vectors. In this way, state characteristic identification can be performed by identifying the text vector set from the supporting documentation based on its file type, thus improving the accuracy of state characteristic identification.

[0008] In some possible examples, the text vector set includes a monetary dataset, and the pet information includes insurance information. The method for determining the pet's accident damage assessment result based on supporting documents includes: finding the assessment rules based on the insurance information; and assessing the monetary dataset based on the assessment rules to obtain the pet's accident damage assessment result. Thus, when the reasonable value of the claim request exceeds a first threshold, assessing the monetary dataset based on the assessment rules, without considering other information, can improve the efficiency of damage assessment.

[0009] In some possible examples, after determining a reasonable value for the claim based on pet image data and supporting documents, the process may also include: if the reasonable value is less than a first threshold, sending a retransmission prompt or canceling the claim prompt to the object corresponding to the pet. It can be understood that if the reasonable value is less than the first threshold, the pet accident corresponding to the claim is determined to be a fabricated pet accident, and the damage assessment step is no longer performed, thus improving the efficiency of accident damage assessment.

[0010] In some possible examples, after determining the accident damage assessment result for the pet based on supporting documents, the process may include: sending a notification message about the accident damage assessment result to the relevant party associated with the pet; and if an objection request is received from the relevant party, sending a verification request for the accident damage assessment result to the claims verification party based on the pet's image data and supporting documents. In this way, if the relevant party is dissatisfied with the accident damage assessment result, they can send a verification request to the claims verification party before filing a claim, thereby increasing the diversity of data processing and improving user satisfaction.

[0011] One embodiment of this application provides a data processing apparatus, including:

[0012] Storage unit, used to store the first threshold;

[0013] The communication unit is used to receive a claim request from the object corresponding to the pet. The claim request includes pet image data and supporting documents.

[0014] The processing unit is used to determine the reasonable value of the claim request based on the pet image data and supporting documents; if the reasonable value is greater than the first threshold, the accident damage assessment result of the pet is determined based on the supporting documents.

[0015] One aspect of this application provides a computer device, including a memory and a processor connected to the memory. The memory stores a computer program, and the processor invokes the computer program to cause the computer device to execute the method provided in one aspect of this application.

[0016] One aspect of this application provides a computer-readable storage medium. This computer-readable storage medium stores a computer program adapted to be loaded and executed by a processor, causing a computer device having a processor to perform the method provided in one aspect of this application.

[0017] According to one aspect of this application, a computer program product is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method provided in the above aspect.

[0018] In this embodiment, after receiving a claim request from the object corresponding to the pet, a reasonable value for the claim request is determined based on the pet image data and supporting documents in the claim request. If the reasonable value is greater than a first threshold, it indicates that the pet accident corresponding to the claim request is a genuine pet accident. Then, the accident damage assessment result for the pet is determined based on the supporting documents, which can identify reasonable damage assessment events and improve the accuracy of accident damage assessment. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0020] Figure 1 This is a schematic diagram of a network architecture provided in an embodiment of this application;

[0021] Figure 2A schematic diagram illustrating a data processing method provided in an embodiment of this application;

[0022] Figure 3 This is a flowchart illustrating a data processing method proposed in an embodiment of this application;

[0023] Figure 4 A flowchart illustrating a state feature recognition method provided in an embodiment of this application;

[0024] Figure 5 A schematic diagram illustrating a communication method provided in an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0026] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0027] This application does not limit the type of pet, and may include common pets such as cats, dogs, birds, and turtles, as well as less common pets such as chickens, pigs, snakes, lizards, geckos, and lions. In the embodiments of this application, pet information may include identifying information such as the pet's name and nose print, and may also include pet attributes such as breed, age, sex, skin color, and fur color. Pet information may also include characteristics such as dietary habits, daily routines, exercise preferences, and health status, or may include historical medical records, vaccination records, and insurance information, etc., without limitation.

[0028] The medical records are used to document the information for each visit. This information may include the type of visit, current medical history, past medical history, physical examination data, and diagnostic information. It may also include the hospital visited and follow-up information; there are no specific limitations. The vaccination records document the pet's vaccination history, including the type of vaccine administered, the number of doses given, and whether any adverse reactions occurred. The insurance information describes the pet's purchased insurance details, such as the insurance name, insurance number, insurance company, insured party, beneficiary, coverage, terms and conditions, premium amount, and claims instructions; there are no specific limitations.

[0029] This application does not limit the object corresponding to the pet; it can be the pet's owner or caretaker, etc. The number of objects corresponding to a pet can be one, two, or more. In the embodiments of this application, object information can include object attributes. These object attributes can include basic information such as the object's identification information (e.g., name, identity identifier, account identifier, etc.), age, gender, occupation, and address. They can also include the object's social data, such as social relationships online or in real life. Object attributes may also include tags for the object, such as hobbies and behavioral habits.

[0030] Object information can also be categorized by time of occurrence into real-time data and historical data. Real-time data can include relevant data from the user's current terminal, such as currently searched keywords and browsed content. Historical data can include the object's previous shopping records and browsing history from other user terminals. Historical data can be used to obtain the object's preference characteristics; for example, determining a user's preferred pet hospital based on their frequently visited clinics. Thus, whether to send a corresponding push notification can be determined based on whether the data characteristics of the real-time scenario match the object's preference characteristics.

[0031] Pet information and / or object information can be uploaded to a server for storage or stored on a blockchain. The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer. Thus, by distributing data storage through the blockchain, data security can be ensured while enabling data sharing between different platforms.

[0032] This application does not limit the scenarios for storing pet information and / or object information. It can be used for scenarios such as pets visiting hospitals, receiving vaccinations, grooming, undergoing physical examinations, handling hospitalization procedures, applying for insurance, and obtaining identity information. It is understood that after storing pet and object information, information about the pet can be pushed to the target based on this information, improving the accuracy of the pushed pet information.

[0033] It should be noted that the specific implementation of this application may involve data of users, enterprises, institutions, etc. When the above embodiments of this application are applied to specific products or technologies, permission or consent from users, enterprises, institutions, etc. is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0034] 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, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0035] Please see Figure 1 , Figure 1 This is a schematic diagram of a network architecture provided in an embodiment of this application. Figure 1 As shown, this network architecture may include a server 10d and a user terminal cluster. The user terminal cluster may include one or more user terminals; the number of user terminals is not limited here. Figure 1 As shown, the user terminal cluster may specifically include user terminal 10a, user terminal 10b, and user terminal 10c, etc.

[0036] Among them, server 10d can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0037] User terminals 10a, 10b, and 10c can all include: smartphones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices (such as smartwatches and smart bracelets), smart voice interaction devices, smart home appliances (such as smart TVs), and in-vehicle devices, as well as other electronic devices with video / image playback functions.

[0038] like Figure 1As shown, user terminals 10a, 10b, and 10c can each connect to server 10d via a network, enabling data interaction between each user terminal and server 10d. For example, a first object may send its object information and pet information to server 10d via user terminal 10a, while a second object may send a claim request to server 10d via user terminal 10b. Alternatively, server 10d may send accident assessment results to the insured via user terminal 10a.

[0039] In this embodiment, the insured object can be an insurance company or an entity that has entered into a guarantee agreement with the pet. The following example uses user terminal 10a as the computer device used by the pet and server 10d as the server corresponding to the insured object. Please refer to [link / reference]. Figure 2 , Figure 2 This is a schematic diagram illustrating a data processing method provided in an embodiment of this application. Figure 2 As shown, the pet can send a claim request to the server 10d via user terminal 10a. The server 10d can then determine the pet's accident damage assessment based on information in the claim request, such as pet image data and supporting documents.

[0040] In this embodiment, the claim request is used to instruct the insured to assess the damage caused to the pet in an accident, obtain the accident damage assessment result, and then make a claim based on the reimbursement amount in the accident damage assessment result. This application does not limit the type of accident caused to the pet or the accident damage assessment result. The accident damage assessment result may include the accident type and reimbursement level corresponding to the incident and report, as well as the reimbursement amount. For example, if the accident type is illness, the reimbursement amount in the accident damage assessment result can be the illness reimbursement amount. If the accident type is death or loss, the reimbursement amount in the accident damage assessment result can be the compensation for mental distress corresponding to death or loss. If the accident type is damage to a third party's property, the reimbursement amount in the accident damage assessment result can be the compensation for property damage.

[0041] The data processing method provided in the embodiments of this application is described in detail below. Please refer to... Figure 3 , Figure 3 This is a flowchart illustrating a data processing method provided in an embodiment of this application. The method can be executed by a data processing device or a computer device, which can be a server (e.g., Figure 1 The corresponding embodiment is a server 10d), or a user terminal (e.g., Figure 1 The user terminal in the user terminal cluster shown can be any user terminal, or a computer program (including program code). Figure 3 Let's take server execution as an example, such as... Figure 3As shown, the method includes the following steps S301 to S303, wherein:

[0042] S301: Receive the pet's claim request from the object corresponding to the pet.

[0043] The claim request is used to instruct the insured to assess the damage caused to the pet in an accident, obtaining the assessment result. The claim request may include pet image data. Pet image data may include full-body images or partial images of the pet. Partial images may be facial images or nose print images. Pet image data can be images taken at the time the claim request is sent or before. It is understood that the pet image data at the time of the claim request can reflect the pet's current state. This pet image data can be a single image or multiple images, or a set of images in the form of videos or short videos, etc., without limitation.

[0044] Claims can also include supporting documents to prove the content of the claim, which can include text, images, audio, video, etc. It should be understood that different types of insurance require different types of supporting documents. For example, medical insurance claims require diagnostic certificates, laboratory reports, medical records, and expense invoices. Third-party liability claims require proof of the accident, on-site photos, and property invoices. Therefore, providing prompts for document types on the document upload page can help users upload the relevant data, thereby increasing the success rate of claim submissions. If the pet image data is taken at the time of the accident, it can be used as supporting documentation, helping to improve the accuracy of accident damage assessment.

[0045] Claim requests can carry time information, which records the time the claim request was submitted. If the insured has a pre-defined processing time, for example, 3 days for Category I accidents, 5 days for Category II accidents, and 10 days for Category III accidents, performing the loss assessment process within the time period corresponding to the time the claim request was submitted and the processing time can improve the efficiency of claim processing. Claim requests may also include remarks about the pet, such as a description of the accident corresponding to the claim request, to supplement information not disclosed in supporting documents, etc. This application does not limit the content of the claim request.

[0046] S302: Determine the reasonable value of the claim based on the pet image data and supporting documents in the claim request.

[0047] In this embodiment, a reasonable value for a claim request is used to determine whether the pet accident corresponding to the claim request is genuine. In this embodiment, claim requests with a reasonable value greater than a first threshold are determined to be genuine pet accidents. Otherwise, they are determined to be fabricated pet accidents. This application does not limit the size of the first threshold; it can be a fixed value, such as 90%. Alternatively, it can be determined based on the clarity of the pet image data, the completeness of the supporting documents, etc.

[0048] This application does not limit the method for determining the reasonable value of a claim, but can identify the authenticity of pet image data and supporting documents. For example, it analyzes the image texture in the pet image data and / or the official seal image in the supporting documents to obtain abnormal texture features; then it determines the size of the image area corresponding to the abnormal texture features. If the image area is less than or equal to a threshold (e.g., 0, 2% of the entire image, etc.), then it can be determined that the pet image data and / or supporting documents are not abnormal. Another example is to search for a pet image based on the supporting documents and determine whether the pet image in the claim and the pet image data in the claim correspond to the same pet; if so, then it can be determined that the pet image data and supporting documents are not abnormal.

[0049] In some possible examples, step S302 may include the following steps: determining a first state characteristic of the pet based on pet image data, determining a second state characteristic of the pet based on supporting documents, and determining a reasonable value for the claim based on the matching value between the first state characteristic and the second state characteristic.

[0050] The first state feature is determined from pet image data, and the second state feature is determined from supporting documentation. State features can include emotional state features, physiological state features, limb state features, etc., and are not limited here. State features of the same dimension can be compared to obtain matching sub-values ​​for each dimension. These matching sub-values ​​are then weighted to obtain the matching value between the first and second state features. This application does not limit the method for determining a reasonable value for the matching value; a mapping relationship between the matching value and the reasonable value can be pre-set, for example, a larger matching value corresponds to a larger reasonable value.

[0051] It is understandable that, in this example, the larger the matching value between the first state feature and the second state feature, the greater the likelihood that the pet image data and the supporting documents correspond to the same event. Therefore, determining the reasonable value of the claim request based on the matching value between the first state feature determined by the pet image data and the second state feature determined by the supporting documents can improve the accuracy of identifying reasonable damage assessment events.

[0052] This application does not limit the method for determining the first state feature and the second state feature. The first state feature is described below. In some possible examples, the method for determining the first state feature of a pet based on pet image data may include the following steps: performing feature recognition on the pet image data to obtain multiple feature point subsets; obtaining pet information based on the nose feature point subset; and performing state recognition on the multiple feature point subsets based on historical state data in the pet information to obtain the first state feature of the pet.

[0053] The multiple feature point subsets include feature point subsets corresponding to each of multiple body parts. These subsets can include nose feature point subsets, eye feature point subsets, mouth feature point subsets, contour feature point subsets, limb feature point subsets, etc. Contour feature point subsets can include feature points corresponding to facial contours, or they can include feature points corresponding to body contours. Feature recognition methods can employ algorithms such as Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), wavelet transform, and Haar-like algorithms. After extracting feature points from the pet image, the image can be divided according to the region where the body part is located, resulting in feature point subsets for each body part. Alternatively, feature points from the same body part can be grouped into a single feature point subset using clustering.

[0054] Pet nose print features can be used for identification. Pet information can be referred to above or below, and will not be repeated here. Pet information can be retrieved from a pre-stored database based on the identification information obtained from a subset of nose feature points. Alternatively, it can be searched online based on at least one image of the pet's nose corresponding to a subset of nose feature points. Pet information can include historical state data, which can be state data corresponding to different events (e.g., illness, vaccination, sleeping, etc.), including emotional state, physical state, and physiological state. This state data can be associated with state features. Therefore, the first state feature of the pet can be obtained by matching the feature points corresponding to the state features in the historical state data with multiple feature point subsets.

[0055] It is understandable that a subset of a pet's feature points can be used to determine the pet's state. In this example, state recognition is performed on multiple subsets of the pet's feature points based on historical state data from the pet's information, improving the accuracy of state feature recognition.

[0056] In some possible examples, a method for determining the second state characteristics of a pet based on a certificate may include the following steps: identifying a set of text vectors from the certificate based on the certificate's file type; and determining the pet's second state characteristics based on the text vectors.

[0057] The supporting documents can be of various file types, including text, images, audio, and video. A text vector set can include multiple text vectors, each consisting of text attributes and specific numerical or textual values. If the supporting document is a medical record, the text attributes can include the disease and diagnosis, anatomical location, medications, surgeries, imaging examinations, and laboratory tests from the main body of the medical record. If the supporting document is an invoice, the text attributes can include the invoice header, the recipient of reimbursement, and the invoice amount. If the supporting document is an accident report, the text attributes can include the event name, cause, result, and time of the event.

[0058] It's understandable that the text attributes in the text vector set are accident-related content. Different text recognition methods apply to different file types. For text, keyword extraction algorithms based on statistical features or word graph models can be used to extract key text from the evidence document. For images, image recognition text algorithms can be used for text extraction. For audio, speech and semantic recognition algorithms can be used for text extraction. Video can be split into audio and image data for separate text extraction. Therefore, first identifying the text vector set from the evidence document based on its file type, and then determining the pet's second state features based on the text vector set, can improve the accuracy of state feature recognition.

[0059] In some possible examples, a method for determining the second state features of a pet based on a set of text vectors may include the following steps: dividing the set of text vectors to obtain a set of state text vectors and an incident text vector set; determining the probabilistic state of the pet based on the incident text vector set; determining the shallow state of the pet based on the state text vector set; and determining the second state features of the pet based on the probabilistic state and the shallow state.

[0060] Here, the state text vector refers to the text vector that directly describes the pet's state. The accident text vector set refers to the text vector that describes the accident that occurred to the pet. Therefore, the pet's state can be determined based on the state text vector, and this state is called the shallow state. The possible states of the pet can be determined based on the accident text vector set, and this state is called the probabilistic state. The second state feature determined by the probabilistic state and the shallow state can be understood as the state for feature fusion or the deep, comprehensive state. It can be understood that determining the second state feature based on the states determined by the accident text vector set and the state text vector set respectively can improve the accuracy of state feature recognition.

[0061] The above-mentioned state feature recognition methods can employ models, specifically feature recognition models and feature learning models, to improve recognition accuracy. Feature recognition networks can include deep residual networks (ResNet), which address the degradation problem of deep networks through residual learning, allowing for the training of even deeper networks. Feature learning networks can include recurrent neural networks (RNNs), used to predict the state information of the next time step based on previous state information. Specifically, this can include long short-term memory networks (LSTM), which can address long-term dependency issues.

[0062] The following example uses the state text vector and the accident text vector set to determine the second state features. Please refer to [the example]. Figure 4 , Figure 4 This is a flowchart illustrating a state feature recognition method provided in an embodiment of this application. Figure 4 As shown, the pet's accident text vector set and state text vector set are input into the feature recognition network to obtain the pet's probabilistic state and shallow state. Then, the probabilistic state and shallow state are input into the feature learning network to obtain the pet's second state features.

[0063] S303: If the reasonable value is greater than the first threshold, the accident damage assessment result for the pet shall be determined based on the supporting documentation.

[0064] The first threshold can be referred to in step S302, and the accident assessment result can be referred to the foregoing or the following description, which will not be repeated here. This application does not limit the method for determining the accident assessment result. The accident type, reimbursement level, and expense amount can be determined based on the supporting documents. Then, the reimbursement amount of the expense amount is determined according to the claim calculation rules corresponding to the reimbursement level.

[0065] In some possible examples, the text vector set includes a monetary dataset, and the pet information includes insurance information. The method for determining the pet's accident damage assessment result based on the supporting documents may include the following steps: finding the damage assessment rules based on the insurance information; and assessing the monetary dataset based on the damage assessment rules to obtain the pet's accident damage assessment result.

[0066] The insurance information can be found above or below and will not be repeated here. The monetary dataset includes various monetary data, which may include treatment costs, food costs, etc. It is understood that the pet insurance policy defines loss assessment rules or related content. These loss assessment rules are used to indicate the method of loss assessment for accidents to obtain the loss assessment result. For example, the loss assessment rules stipulate a reimbursement rate of 60% for designated hospitals and 40% for non-designated hospitals; the maximum compensation for third-party personal injury or property damage is 150,000 yuan, and the maximum compensation for pet death due to accidents, diseases, or epidemics is 1,500 yuan. In this example, when the reasonable value of the claim request exceeds the first threshold, the monetary dataset is assessed based on the loss assessment rules without considering other information, thus improving the efficiency of loss assessment.

[0067] exist Figure 3 In the method shown, after receiving a claim request from the object corresponding to the pet, a reasonable value for the claim request is determined based on the pet image data and supporting documents in the claim request. If the reasonable value is greater than a first threshold, it can be indicated that the pet accident corresponding to the claim request is a genuine pet accident. Then, the pet's accident damage assessment result is determined based on the supporting documents. In this way, identifying reasonable damage assessment events based on the pet image data and supporting documents carried in the claim request helps improve the accuracy of accident damage assessment.

[0068] In some possible examples, after step S302, the following steps are also included: if the reasonable value is less than the first threshold, a retransmission prompt or a cancellation of the claim prompt is sent to the object corresponding to the pet.

[0069] The retransmission prompt instructs the recipient of the pet to retransmit pet image data and / or supporting documents, or to supplement with new supporting materials, allowing the recipient to upload authentic data or provide additional documentation. The claim cancellation prompt informs the recipient that their claim request has failed review, allowing them to upload authentic data, provide additional supporting materials, or cancel the claim. It can be understood that if the reasonable value is less than the first threshold, the pet accident corresponding to the claim request is determined to be a fabricated pet accident, and the damage assessment step is no longer performed, thus improving the efficiency of accident damage assessment.

[0070] In some possible examples, after step S303, the following steps may also be included: sending a notification message about the accident assessment result to the object corresponding to the pet; if an objection request is received from the object corresponding to the pet, sending a verification request for the accident assessment result to the claim verification object based on the pet image data and supporting documents.

[0071] The system includes a notification message indicating the accident damage assessment result, and a request for objection indicating the pet's owner's dissatisfaction with the assessment. The claims verification entity can be a designated reviewer, staff from a third-party arbitration institution, or another computer device (user terminal or server). The verification request instructs the verification entity to check the accident damage assessment result based on the pet's image data and supporting documents. After verification, the verified accident damage assessment result can be sent to the pet's owner to indicate whether it matches the result obtained in step S303. Thus, if the pet's owner is dissatisfied with the accident damage assessment result, they can send a verification request to the claims verification entity before filing a claim, increasing the diversity of data processing and improving user satisfaction.

[0072] For example, please refer to Figure 5 , Figure 5 This is a schematic diagram illustrating a communication method provided in an embodiment of this application. Figure 5 As shown, the pet's corresponding object 101 receives a notification message from the server 10d regarding the accident assessment result via user terminal 10a, causing the accident assessment result page 500 on user terminal 10a to display the accident type, reimbursement level, and reimbursement amount. If object 101 clicks the objection function component 501, an objection request is sent to the server 10d. If object 101 clicks the confirmation function component 502, the server 10d can proceed to the next step, such as claims processing. After receiving the objection request, the server 10d can execute... Figure 5 Not shown, a verification request for the accident assessment results is sent to the claimant based on pet image data and supporting documents.

[0073] The methods of the embodiments of this application have been described in detail above, and the apparatus of the embodiments of this application is provided below.

[0074] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application. Figure 6 As shown, the data processing device includes a storage unit 601, a communication unit 602, and a processing unit 603, wherein:

[0075] Storage unit 601 is used to store the first threshold;

[0076] Communication unit 602 is used to receive a claim request from the object corresponding to the pet, the claim request including pet image data and supporting documents;

[0077] The processing unit 603 is used to determine the reasonable value of the claim based on the pet image data and supporting documents; if the reasonable value is greater than the first threshold, the accident damage assessment result of the pet is determined based on the supporting documents.

[0078] In some possible examples, the processing unit 603 is specifically used to determine a first state feature of the pet based on pet image data, determine a second state feature of the pet based on supporting documents, and determine a reasonable value for the claim based on the matching value between the first state feature and the second state feature.

[0079] In some possible examples, the processing unit 603 is specifically used to perform feature recognition on pet image data to obtain multiple feature point subsets, including a nose feature point subset; obtain pet information based on the nose feature point subset, including historical state data; and perform state recognition on the multiple feature point subsets based on the historical state data to obtain the pet's first state features.

[0080] In some possible examples, the processing unit 603 is specifically used to identify a set of text vectors from the proof document based on the document type; and to determine the second state features of the pet based on the set of text vectors.

[0081] In some possible examples, the text vector set includes a monetary dataset, the pet information includes insurance information, and the processing unit 603 is specifically used to find the loss assessment rules based on the insurance information; based on the loss assessment rules, the monetary dataset is assessed to obtain the pet's accident loss assessment result.

[0082] In some possible examples, the communication unit 602 is also used to send a retransmission prompt or a cancellation of claim prompt to the object corresponding to the pet if the reasonable value is less than a first threshold.

[0083] In some possible examples, the communication unit 602 is also used to send a notification message about the accident assessment result to the object corresponding to the pet; if an objection request is received from the object corresponding to the pet, a verification request for the accident assessment result is sent to the claim verification object based on the pet image data and supporting documents.

[0084] Please refer to Figure 7 , Figure 7 This is a schematic diagram of a computer device provided in an embodiment of this application. The computer device 700 includes a processor 701, a communication interface 702, and a memory 703. The processor 701, the communication interface 702, and the memory 703 can be interconnected via a bus 705 or other means. Figure 6 The related functions implemented by the processing unit 603 shown can be implemented by one or more processors 701. Figure 6 The functions implemented by the communication unit 602 shown can be realized through the communication interface 702. Figure 6 The functions implemented by the storage unit 601 shown can be implemented by the memory 703.

[0085] The processor 701 includes one or more processors, such as one or more central processing units (CPUs). When the processor 701 is a CPU, the CPU can be a single-core CPU or a multi-core CPU. In this embodiment, the processor 701 is used to control the computer device 700. Figure 3 The example shown.

[0086] The communication interface 702 is used to enable communication with other devices. For example, if the computer device 700 is a user terminal, the communication interface 702 can enable communication between the user terminal and devices such as servers; if the computer device 700 is a server, the communication interface 702 can enable communication between the server and devices such as user terminals.

[0087] The memory 703 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), and is used to store related instructions and data.

[0088] In this embodiment, memory 703 stores computer program 704, which includes program instructions. Processor 701 is configured to invoke the program instructions. The program includes instructions for performing the following steps:

[0089] Receive the pet's claim request from the corresponding object. The claim request includes pet image data and supporting documents.

[0090] The reasonable value of the claim is determined based on pet image data and supporting documents;

[0091] If the reasonable value is greater than the first threshold, the accident damage assessment result for the pet will be determined based on the supporting documentation.

[0092] In some possible examples, in determining the reasonable value of a claim based on pet image data and supporting documentation, the instructions in the procedure are specifically used to perform the following operations:

[0093] The first state characteristics of the pet are determined based on pet image data, and the second state characteristics of the pet are determined based on supporting documents.

[0094] The reasonable value of the claim request is determined based on the matching value between the first state feature and the second state feature.

[0095] In some possible examples, in determining the pet's initial state characteristics based on pet image data, the instructions in the program are specifically used to perform the following operations:

[0096] Feature recognition is performed on pet image data to obtain multiple feature point subsets, including a nose feature point subset.

[0097] Pet information is obtained based on a subset of nasal feature points. This pet information includes historical status data.

[0098] Based on historical state data, state recognition is performed on multiple feature point subsets to obtain the pet's first state features.

[0099] In some possible examples, in determining the second-state characteristics of a pet based on supporting documentation, the instructions in the program are specifically used to perform the following operations:

[0100] Identify the text vector set from the proof document based on its file type;

[0101] The second state features of the pet are determined based on the text vector set.

[0102] In some possible examples, the text vector set includes a monetary dataset, and the pet information includes insurance information. Specifically, the instructions in the program are used to perform the following operations in determining the pet's accident damage assessment based on supporting documentation:

[0103] Find the loss assessment rules based on the insurance information;

[0104] Based on the damage assessment rules, the monetary dataset is used to assess the damage and obtain the damage assessment results for pet accidents.

[0105] In some possible examples, after determining a reasonable value for the claim based on pet image data and supporting documents, the instructions in the procedure are also used to perform the following operations:

[0106] If the reasonable value is less than the first threshold, a retransmission prompt or a cancellation of the claim prompt will be sent to the object corresponding to the pet.

[0107] In some possible examples, after determining the pet's accident damage assessment based on supporting documentation, the instructions in the procedure are also used to perform the following operations:

[0108] Send a notification message with the accident damage assessment results to the relevant party associated with the pet;

[0109] If an objection request is received from the object corresponding to the pet, a verification request for the accident assessment result is sent to the claim verification object based on the pet's image data and supporting documents.

[0110] This application also provides a computer-readable storage medium storing a computer program executed by the aforementioned data processing device. The computer program includes program instructions, which, when executed by a processor, enable the execution of the aforementioned data processing device. Figure 3 The description of the data processing method in the corresponding embodiments is already provided and will not be repeated here. Similarly, the beneficial effects of using the same method will not be repeated here. For technical details not disclosed in the computer-readable storage medium embodiments related to this application, please refer to the description of the method embodiments of this application. As an example, program instructions can be deployed and executed on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network. These multiple computing devices distributed across multiple locations and interconnected via a communication network can constitute a blockchain system.

[0111] Furthermore, it should be noted that this application also provides a computer program product or computer program, which may include computer instructions, which may be stored in a computer-readable storage medium. The processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor may execute the computer instructions, causing the computer device to perform the aforementioned actions. Figure 3 The description of the data processing method in the corresponding embodiments is already provided and will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated. For technical details not disclosed in the computer program products or computer program embodiments related to this application, please refer to the description of the method embodiments of this application.

[0112] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0113] The steps in the method of this application embodiment can be adjusted, combined, or deleted according to actual needs.

[0114] The modules in the device of this application embodiment can be merged, divided, and deleted according to actual needs.

[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, ROM, RAM, EPROM, or CD-ROM, etc.

[0116] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A data processing method, characterized in that, include: Receive the pet's claim request from the object corresponding to the pet, the claim request including pet image data and supporting documents; The reasonable value of the claim is determined based on the pet image data and the supporting documents; If the reasonable value is greater than the first threshold, the accident damage assessment result of the pet is determined according to the supporting documents; The step of determining a reasonable value for the claim based on the pet image data and the supporting documents includes: The first state characteristics of the pet are determined based on the pet image data, and the second state characteristics of the pet are determined based on the supporting documents; The reasonable value of the claim request is determined based on the matching value between the first state feature and the second state feature; Determining the first state characteristics of the pet based on the pet image data includes: The pet image data is subjected to feature recognition to obtain multiple feature point subsets, including a nose feature point subset. Pet information of the pet is obtained based on the subset of nasal feature points, and the pet information includes historical status data; Based on the historical state data, state recognition is performed on the subset of multiple feature points to obtain the first state feature of the pet; The determination of the pet's second state characteristics based on the supporting documentation includes: Based on the file type of the proof document, identify a set of text vectors from the proof document; The second state features of the pet are determined based on the text vector set.

2. The method as described in claim 1, characterized in that, The text vector set includes a monetary dataset, the pet information includes insurance information, and determining the pet's accident assessment result based on the supporting documents includes: Based on the insurance information, find the loss assessment rules; Based on the aforementioned damage assessment rules, the damage assessment is performed on the monetary dataset to obtain the accident damage assessment result for the pet.

3. The method as described in claim 1 or 2, characterized in that, After determining the reasonable value of the claim based on the pet image data and the supporting documents, the process further includes: If the reasonable value is less than the first threshold, a retransmission prompt or a cancellation of claim prompt will be sent to the object corresponding to the pet.

4. The method as described in claim 1 or 2, characterized in that, After determining the accident damage assessment result for the pet based on the aforementioned supporting documents, the process further includes: Send a notification message with the accident damage assessment result to the object corresponding to the pet; If an objection request is received from the object corresponding to the pet, a verification request for the accident assessment result is sent to the claims verification object based on the pet image data and the supporting documents.

5. A data processing apparatus, characterized in that, Includes units for performing the method as described in any one of claims 1 to 4.

6. A computer device, characterized in that, It includes a memory and a processor; the memory is connected to the processor, the memory is used to store a computer program, and the processor is used to invoke the computer program so that the computer device performs the method of any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded and executed by a processor to cause a computer device having the processor to perform the method of any one of claims 1 to 4.

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