Insurance business loss assessment method, device, server and storage medium

Abstract

The present application relates to the field of financial technology and discloses a method, device, server and storage medium for loss assessment of insurance business. The method receives a business loss assessment image corresponding to a target insurance business to be assessed, and determines the object attributes of the target object in each business loss assessment image based on the image features of the business loss assessment image; obtains an image label and first loss assessment information corresponding to each business loss assessment image according to the object attribute; annotates the business loss assessment image according to the image label to obtain a business annotated image, and preprocesses the business annotated image to obtain a preprocessed image; obtains an image compression strategy for the preprocessed image according to at least label information corresponding to the image label to compress the preprocessed image; transmits at least the compressed target compressed image to a service terminal, receives second loss assessment information obtained by the service terminal, and obtains insurance loss assessment information based on the first loss assessment information and the second loss assessment information.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a method, device, server, and storage medium for loss assessment of insurance business. Background Art

[0002] As agricultural insurance expands in scale, its operations face a paradox between efficiency and risk management. This is particularly evident in information asymmetry and low claims processing efficiency, which severely restrict the effectiveness of agricultural insurance in terms of economic compensation, financing, and social management.

[0003] Currently, when it comes to claims caused by natural disasters, traditional agricultural insurance often uses random field sampling to conduct inspections and damage assessments after a natural disaster occurs during the crop growth period. However, if a natural disaster in a certain area causes a large area of ​​the insured object to be affected, the traditional inspection and damage assessment model often has problems such as information asymmetry, low claims efficiency, and many claims disputes. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to provide a method, device, server and storage medium for loss assessment of insurance business, aiming to realize claim assessment of insurance business efficiently and accurately.

[0005] In a first aspect, the present application provides a method for loss assessment of insurance business, the method comprising:

[0006] receiving business damage assessment images corresponding to target insurance businesses to be assessed, and determining object attributes of target objects in each of the business damage assessment images based on image features of the business damage assessment images, the object attributes including the object type of the target object and the degree of damage to the target object;

[0007] Obtaining image labels and first damage assessment information corresponding to each of the business damage assessment images according to the object type and the damage degree;

[0008] Annotating the business damage assessment image according to the image label to obtain a business annotated image, and preprocessing the business annotated image to obtain a preprocessed image, wherein the preprocessed image has label information corresponding to the image label;

[0009] At least obtaining an image compression strategy for the preprocessed image according to the tag information, and compressing the preprocessed image according to the image compression strategy to obtain a target compressed image;

[0010] at least transmitting the target compressed image to a service terminal so that the service terminal displays the target compressed image on a preset interface;

[0011] The second damage assessment information obtained by the service terminal is received, and the insurance loss assessment information of the target insurance business is obtained based on the first damage assessment information and the second damage assessment information.

[0012] In a second aspect, the present application provides a device for loss assessment of insurance business, comprising:

[0013] An image receiving module is configured to receive business damage assessment images corresponding to target insurance businesses to be assessed, and determine object attributes of target objects in each of the business damage assessment images based on image features of the business damage assessment images, wherein the object attributes include an object type of the target object and a degree of damage to the target object;

[0014] An image analysis module, configured to obtain an image label and first damage assessment information corresponding to each of the business damage assessment images according to the object type and the damage degree;

[0015] an image processing module, which annotates the business damage assessment image according to the image label to obtain a business annotated image, and preprocesses the business annotated image to obtain a preprocessed image, wherein the preprocessed image has label information corresponding to the image label;

[0016] an image compression module, configured to obtain an image compression strategy for the preprocessed image at least according to the tag information, and compress the preprocessed image according to the image compression strategy to obtain a target compressed image;

[0017] An image transmission module, configured to transmit at least the target compressed image to a service terminal, so that the service terminal displays the target compressed image on a preset interface;

[0018] The damage assessment module receives the second damage assessment information obtained by the service terminal, and obtains the insurance damage assessment information of the target insurance business based on the first damage assessment information and the second damage assessment information.

[0019] In a third aspect, the present application provides a server, including a processor and a memory;

[0020] The memory is used to store computer programs;

[0021] The processor is used to execute the computer program and implement the aforementioned insurance business loss assessment method when executing the computer program.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which, when executed by one or more processors, enables the one or more processors to execute the steps of the aforementioned insurance business loss assessment method.

[0023] The embodiments of the present application provide a method, apparatus, device, and storage medium for loss assessment of insurance business. The method comprises receiving a business loss assessment image corresponding to a target insurance business to be assessed, and determining object attributes of a target object in each business loss assessment image based on image features of the business loss assessment image, wherein the object attributes include the object type of the target object and the degree of damage to the target object; obtaining image labels and first loss assessment information corresponding to each business loss assessment image based on the object type and the degree of damage; annotating the business loss assessment image based on the image label to obtain a business annotated image, and preprocessing the business annotated image to obtain a preprocessed image; obtaining an image compression strategy for the preprocessed image based on at least label information corresponding to the image label of the preprocessed image, and compressing the preprocessed image based on the image compression strategy to obtain a target compressed image; transmitting at least the target compressed image to a service terminal so that the service terminal displays the target compressed image on a preset interface; receiving second loss assessment information obtained by the service terminal, and obtaining insurance loss assessment information of the target insurance business based on the first loss assessment information and the second loss assessment information.

[0024] Image analysis is used to obtain the first loss assessment information corresponding to the target object in the business loss assessment image and the second loss assessment information obtained by the auditor through the service terminal to assess the target object in the business loss assessment image, and a comprehensive assessment is performed, so that the target insurance business can be accurately and efficiently assessed relatively efficiently and quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 A schematic diagram of the structure of a loss assessment system for insurance business provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the steps of a method for loss assessment of insurance business provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of a scenario for labeling an image using a method for loss assessment of insurance business provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of the structure of a damage assessment device for insurance business provided in an embodiment of the present application;

[0030] Figure 5 A schematic block diagram of the structure of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0032] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0033] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] As agricultural insurance expands in scale, its operations face a paradox between efficiency and risk management. This is particularly evident in information asymmetry and low claims processing efficiency, which severely restrict the effectiveness of agricultural insurance in terms of economic compensation, financing, and social management.

[0035] Currently, when it comes to claims caused by natural disasters, traditional agricultural insurance often uses random field sampling to conduct inspections and damage assessments after a natural disaster occurs during the crop growth period. However, if a natural disaster in a certain area causes a large area of ​​the insured object to be affected, the traditional inspection and damage assessment model often has problems such as information asymmetry, low claims efficiency, and many claims disputes.

[0036] Based on this, the present application provides a method, device, server and storage medium for loss assessment of insurance business, aiming to realize the claim assessment of insurance business efficiently and accurately.

[0037] The following embodiments of the present application are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0038] See also Figure 1 , Figure 1 A loss assessment system for insurance business is provided in an embodiment of the present application.

[0039] like Figure 1As shown, the insurance loss assessment system 100 includes a client terminal 10, a server 20, and a service terminal 30. The client terminal 10 is installed with an insurance loss assessment client program, and the service terminal 30 is installed with an insurance loss assessment service program. Typical insurance services include, but are not limited to, vehicle insurance, agricultural insurance, and property insurance. The client terminal 10 and the service terminal 30 may include, but are not limited to, smartphones, tablets, laptops, intelligent voice interaction devices, smart home appliances, in-vehicle terminals, and smart wearable devices.

[0040] Server 20 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0041] For ease of explanation, in the embodiments of the present application, the insurance business purchased by the user is taken as an example of agricultural insurance.

[0042] After the user purchases agricultural insurance, if the insured crops are damaged, and the insurance claim terms are met, the user can operate the client terminal 10 to upload crop damage images (also called business damage assessment images) to the server 20, so that the server 20 can conduct a preliminary assessment of the damaged crops in the received image, obtain a preliminary damage assessment (also called first damage assessment information), and transmit the corresponding image to the corresponding service terminal 30, so that the reviewer can further assess the degree of damage to the current insured crops by referring to the image displayed by the service terminal 30, and obtain a secondary damage assessment (also called second damage assessment information), so as to conduct a comprehensive assessment based on the preliminary damage assessment and the secondary damage assessment, and obtain the final damage assessment of the current insured crops, so as to make a claim based on the final damage assessment.

[0043] Furthermore, the images uploaded by the client terminal 10 may have different image quality issues, for example, some images are not clear enough, and some images are too large. Therefore, in order to improve the image quality, the server 20 needs to process the images after receiving the images uploaded by the client terminal 10, and then transmit the processed images to the corresponding service terminal 30, so that the reviewer can further evaluate the degree of damage to the currently insured crops by checking the images displayed on the service terminal 30.

[0044] Please refer to Figure 2 , Figure 2 A schematic flow chart of the steps of a method for loss assessment of insurance business provided in an embodiment of the present application.

[0045] like Figure 2 As shown, the loss assessment method for the insurance business includes steps S101 to S106.

[0046] Step S101: Receive a business damage assessment image corresponding to a target insurance business to be assessed, and determine the object attributes of the target object in each business damage assessment image based on the image features of the business damage assessment image, wherein the object attributes include the object type of the target object and the degree of damage to the target object.

[0047] For example, in the embodiment of the present application, the damage assessment method for insurance business is applied to the aforementioned damage assessment system 100 for insurance business, and is executed by the server 20 in the damage assessment system for insurance business as an example for description.

[0048] After purchasing agricultural insurance, if the insured crops are damaged and the insurance claim terms are met, the user can use client terminal 10 to upload a damage assessment image to server 20. The damage assessment image includes at least one target object, which is the insured crop. After receiving the damage assessment image transmitted by client terminal 10, server 20 extracts image features from the damage assessment image using a preset feature extraction model or feature extraction algorithm and analyzes the acquired image features. Image features include, but are not limited to, color features, texture features, and shape features.

[0049] Different target objects in the business damage assessment image have different image features, and the same target object has different corresponding image features under different degrees of damage. Therefore, after extracting the image features of the business damage assessment image, the acquired image features can be analyzed and the object attributes of the target objects in each business damage assessment image can be obtained according to the analysis results. The object attributes include the object type of the target object and the degree of damage of the target object.

[0050] In some embodiments, determining the object attributes of the target object in each of the business damage assessment images based on the image features of the business damage assessment images includes:

[0051] Determining an object identification area where the target object is located from the business damage assessment image, and extracting image features of the object identification area;

[0052] The target object in the business damage assessment image is analyzed based on the image features to obtain object attributes of the target object.

[0053] Optionally, determining the object identification area where the target object is located from the business damage assessment image includes: selecting the target object in the business damage assessment image using a labeling box through an image labeling model, and determining the object identification area where the target object is located in the business damage assessment image based on the labeling box.

[0054] Optionally, determining the object identification area where the target object is located from the business damage assessment image includes: identifying the object contour of the target object in the business damage assessment image, and determining the object identification area where the target object is located in the business damage assessment image based on the object contour, wherein the object identification area is larger than the area where the object contour is located.

[0055] like Figure 3 As shown, for example, in order to improve the efficiency of image feature extraction, an object identification area with a target object is selected from a business damage assessment image, and the image features of the object identification area are extracted, so that the feature extraction of the corresponding business damage assessment image can be achieved at a faster rate.

[0056] Specifically, the target object 201 in the business damage assessment image 200 is annotated with an annotation box using a trained image annotation model. For example, the target object in the business damage assessment image 200 is selected with a dotted annotation box. After completing the annotation of the target object 201, the minimum area 200a including all the annotation boxes is segmented from the business damage assessment image 200, and this area is used as the object identification area where the target object is located.

[0057] Alternatively, the business damage assessment image is grayscale processed and binarized to obtain a binarized damage assessment image. Then, a preset contour recognition algorithm is used to identify the object contour of the target object in the binarized business damage assessment image (binarized damage assessment image), and a minimum area including all object contours is segmented from the business damage assessment image, and this area is used as the object recognition area where the target object is located. The object recognition area is larger than the area where the object contour is located. The contour recognition algorithm includes, but is not limited to, a connectivity-based contour recognition algorithm and an edge detection-based contour recognition algorithm.

[0058] After identifying the object identification area where the target object is located from the business damage assessment image, the image features of the object identification area are extracted, so that the feature extraction of the corresponding business damage assessment image can be achieved at a faster rate.

[0059] It can be understood that the method of extracting the image features of the object recognition area can be to extract the image features in the object recognition area through a preset feature extraction algorithm, or to cut out the object recognition area from the business damage assessment image and input the object recognition area as input information into a preset feature extraction model, thereby extracting the image features of the object recognition area, and using the image features as the image features of the business damage assessment image, and finally realizing the feature extraction of the corresponding business damage assessment image at a faster level.

[0060] After the image features are acquired, the acquired image features are analyzed so that the object attributes of the target object in the corresponding business damage assessment image can be acquired based on the image features. The object attributes include the object type of the target object and the degree of damage of the target object.

[0061] For example, after extracting image feature A from business damage assessment image A and analyzing it, it is determined that business damage assessment image A includes Class 1 and Class 2 target objects. Class 1 target objects are undamaged, while Class 2 target objects are severely damaged. The damaged area is approximately 50 square meters, accounting for 49% of the total insured area.

[0062] Image feature B of the business damage assessment image B is extracted, and after analyzing image feature B, it is found that the business damage assessment image B includes 1 type of target objects and 3 types of target objects. Among them, the type 1 target objects are not damaged, and the type B target objects are seriously damaged. The damaged area is about 100 square meters, accounting for 98% of the total insured area.

[0063] Step S102: Obtain the image label and first damage assessment information corresponding to each of the business damage assessment images according to the object type and the damage degree.

[0064] Exemplarily, server 20 stores a first association between damage degree and image tags. After obtaining the damage degree of the target object in each business damage assessment image, server 20 determines an image tag appropriate for the damage degree based on the damage degree and the first association. This image tag is used to characterize the damage degree of the target object in the corresponding business damage assessment image. For example, image tags may include a first type of tag indicating that the target object is undamaged or slightly damaged, and a second type of tag indicating that the target object is severely damaged.

[0065] At the same time, the server 20 also stores a damage assessment function, which is related to the object type of the target object and the degree of damage of the target object. After obtaining the object type of the target object and the degree of damage of the target object, the first damage assessment information corresponding to each business damage image uploaded by the client terminal 10 can be obtained through the preset damage assessment function.

[0066] Step S103: annotating the business damage assessment image according to the image label to obtain a business annotated image, and preprocessing the business annotated image to obtain a preprocessed image, wherein the preprocessed image has label information corresponding to the image label.

[0067] For example, in order to enhance the reliability of the first damage assessment information obtained by the server 20, the server 20 also needs to send the received business damage assessment image to the corresponding service terminal 30 so that the user can perform a secondary assessment at the service terminal 30.

[0068] The server 30 labels each business damage assessment image according to the acquired image label, and preprocesses the labeled image to obtain a preprocessed image, and the obtained preprocessed image has label information corresponding to the image label. The preprocessing of the image includes but is not limited to at least one of image compression and image noise reduction.

[0069] The pre-processed image obtained by compressing the business annotated image can effectively reduce the time required to transmit the image from the service area 20 to the service terminal 30, thereby reducing image transmission time and data traffic consumption, and ultimately improving the insurance processing speed.

[0070] The preprocessed image obtained by denoising the service annotated image can effectively improve the image quality, thereby improving the image effect displayed on the service terminal 30 .

[0071] Optionally, preprocessing the business-annotated image to obtain a preprocessed image includes:

[0072] The business annotated image is compressed using a preset image processing model to obtain a preprocessed image. The image processing model includes an image coding network, a quantization network, and an image decoding network. The image coding network is used to convert the business annotated image into a corresponding image feature vector. The quantization network is used to discretize the image feature vector output by the image coding network. The image coding network is used to restore the discretized image feature vector to obtain the preprocessed image. The image coding network includes at least a residual network or a U-Net network. The network structures of the image coding network and the image decoding network are symmetrical.

[0073] For example, a deep convolutional neural network is used as the image encoding network to extract features from the original business annotated image and compress the information to obtain an image feature vector. The image encoding network can be based on a residual network (ResNet), U-Net, or other efficient architectures to gradually reduce the spatial dimension of the feature map extracted from the original business annotated image while preserving important image details.

[0074] The image coding network adds the eigenvalues ​​corresponding to the image feature vector obtained from the business annotated image to the quantization layer, which is used to convert the continuous eigenvalues ​​into discrete representations through a preset quantization strategy, where the quantization strategy includes uniform quantization, vector quantization (VQ) or nearest neighbor quantization.

[0075] The image decoding network is symmetrical with the image encoding network in structure, and is used to restore the quantized eigenvalues ​​to an image close to the original image (business annotated image), thereby reconstructing a high-quality image with as few bits as possible to obtain a preprocessed image.

[0076] Optionally, the image processing model uses a combination of mean square error (MSE) and structural similarity index (SSIM) as the loss function to ensure that the reconstructed image (preprocessed image) is close to the original image (business annotated image) at the pixel level and maintains structural similarity.

[0077] Step S104: obtaining an image compression strategy for the pre-processed image at least according to the tag information, and compressing the pre-processed image according to the image compression strategy to obtain a target compressed image.

[0078] For example, to balance the efficiency of image transmission and the reliability of image-based damage assessment and analysis, the image compression strategy for the target compressed image to be transmitted to the service terminal 30 is determined based on the tag information. For example, if the tag information indicates that the damage to the target object in the pre-processed image is severe, then to ensure that the image received by the service terminal 30 can show the damaged details of the target object, the pre-processed image should be compressed using a lower image compression ratio. This ensures that the damaged details of the target object in the resulting target compressed image are clearer, facilitating reviewers' verification and damage assessment of the damage to the target object.

[0079] When it is known from the label information that the target object in the preprocessed image is not damaged or is less damaged, the preprocessed image has little auxiliary effect on the damage assessment of the target object. A higher image compression rate should be sampled to compress the preprocessed image, which can effectively reduce the file size of the image and speed up the image transmission.

[0080] In some embodiments, obtaining the image compression strategy for the pre-processed image at least based on the tag information includes:

[0081] determining the damage degree of the target object in the pre-processed image based on the tag information, and obtaining device performance parameters and network environment parameters of a service terminal in communication with the server;

[0082] An image compression strategy for the pre-processed image is determined according to at least one of the device performance parameter, the network environment parameter, and the damage degree.

[0083] Optionally, determining the image compression strategy of the pre-processed image according to at least one of the device performance parameter, the network environment parameter, and the damage degree includes:

[0084] When the device performance parameter meets the preset performance requirement, the network environment parameter meets the preset data transmission requirement, and the damage degree of the target object is Class I damage, determining that the image compression strategy of the pre-processed image is the first compression strategy;

[0085] When the device performance parameters do not meet the preset performance requirements, or the network environment parameters do not meet the preset data transmission requirements, or the damage degree of the target object is Class II damage, the image compression strategy of the preprocessed image is determined to be the second compression strategy, wherein the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is less than the image compression rate of the first compression strategy.

[0086] Optionally, determining the image compression strategy of the pre-processed image according to at least one of the device performance parameter, the network environment parameter, and the damage degree includes:

[0087] When the device performance parameter meets the preset performance requirement and the damage degree of the target object is a type of damage, determining that the image compression strategy of the pre-processed image is a first compression strategy;

[0088] When the device performance parameters do not meet the preset performance requirements or the damage degree of the target object is Class II damage, the image compression strategy of the preprocessed image is determined to be the second compression strategy, wherein the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is less than the image compression rate of the first compression strategy.

[0089] Optionally, determining the image compression strategy of the pre-processed image according to at least one of the device performance parameter, the network environment parameter, and the damage degree includes:

[0090] When the network environment parameters meet the preset data transmission requirements and the damage degree of the target object is Class I damage, determining that the image compression strategy of the pre-processed image is a first compression strategy;

[0091] When the network environment parameters do not meet the preset data transmission requirements or the damage degree of the target object is Class II damage, the image compression strategy of the preprocessed image is determined to be the second compression strategy, wherein the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is less than the image compression rate of the first compression strategy.

[0092] Optionally, determining the image compression strategy of the pre-processed image according to at least one of the device performance parameter, the network environment parameter, and the damage degree includes:

[0093] When the damage degree of the target object is Class I damage, the image compression strategy of the preprocessed image is determined to be the first compression strategy; when the damage degree of the target object is Class II damage, the image compression strategy of the preprocessed image is determined to be the second compression strategy, wherein the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is less than the image compression rate of the first compression strategy.

[0094] Exemplarily, the device performance parameters include at least one of CPU performance and GPU performance. The network environment parameters include at least network bandwidth.

[0095] If it is detected that the network environment parameters of the service terminal 30 cannot meet the preset data transmission requirements, indicating that the network bandwidth of the service terminal 30 is less than the preset network bandwidth value, then the image compression strategy of the pre-processed image is determined to be the second compression strategy, that is, the pre-processed image is compressed using the second image compression rate to reduce the image file size and speed up the transmission speed.

[0096] On the contrary, if it is detected that the network environment parameters of the service terminal 30 can meet the preset data transmission requirements, indicating that the network bandwidth of the service terminal 30 is greater than or equal to the preset network bandwidth value, then the image compression strategy of the preprocessed image is determined to be the first compression strategy, and a lower image compression rate can be selected to provide a higher quality picture.

[0097] Alternatively, if it is detected that the device performance parameters of the service terminal 30 cannot meet the preset device performance parameters, indicating that the service terminal 30 has weak image processing capabilities, then the image compression strategy of the pre-processed image is determined to be the second compression strategy, that is, the pre-processed image is compressed using the second image compression rate to reduce the image file size and speed up the transmission speed so that the compressed image can be loaded and displayed faster.

[0098] On the contrary, if it is detected that the device performance parameters of the service terminal 30 meet the preset device performance parameters, indicating that the service terminal 30 has a strong image processing capability, the image compression strategy of the pre-processed image is determined to be the first compression strategy, and a lower image compression rate can be selected to provide a higher quality picture.

[0099] Alternatively, if the damage level of the target object in the pre-processed image is Class II damage, indicating that the damage level of the target object in the pre-processed image is relatively severe, in this case, to ensure that the damaged details of the target object can be seen in the image received by the service terminal 30, the pre-processed image should be compressed using a lower image compression ratio. This ensures that the damaged details of the target object in the resulting compressed image are clearer, facilitating the reviewer's review and assessment of the damage level of the target object. In other words, the image compression strategy for the pre-processed image is determined to be the second compression strategy.

[0100] That is, if it is detected that the network environment parameters of the service terminal 30 meet the preset data transmission requirements, the device performance parameters meet the preset device performance parameters, and the damage degree of the target object in the pre-processed image is Class I damage, it indicates that the service terminal 30 has a strong image processing capability and has sufficient bandwidth performance, and the damage degree of the target object in the pre-processed image is relatively light or undamaged, and the image compression strategy of the pre-processed image is determined to be the first compression strategy, and a lower image compression rate can be selected to provide a higher quality picture.

[0101] Step S105: at least transmitting the target compressed image to the service terminal, so that the service terminal displays the target compressed image on a preset interface.

[0102] Exemplarily, after obtaining the target compressed image, the target compressed image is transmitted to the service terminal 30 so that the target compressed image can be displayed on the preset interface of the service terminal 30, so that the reviewer can read the corresponding target compressed image and conduct a worker review and assessment of the damage to the target object based on the image to obtain a second damage assessment information of the damage to the target object.

[0103] Alternatively, after obtaining the target compressed image, the target compressed image and the first damage assessment information are transmitted to the service terminal 30 so that the target compressed image can be displayed on the preset interface of the service terminal 30, so that the reviewer can read the corresponding target compressed image and the first damage assessment information, and then conduct a worker review and assessment of the damage to the target object based on the image to obtain the second damage assessment information of the damage to the target object.

[0104] Step S106: receiving the second damage assessment information acquired by the service terminal, and obtaining the insurance damage assessment information of the target insurance business based on the first damage assessment information and the second damage assessment information.

[0105] Exemplarily, after the reviewer views the target compressed image or the target compressed image and the first damage assessment information through the service terminal 30, he obtains the second damage assessment information of the target object, and sends the second damage assessment information to the server 20, so that the server 20 is provided with a damage assessment program or algorithm, which obtains the final damage assessment information of the target object through analysis based on the first damage assessment information and the second damage assessment information through the damage assessment program or algorithm, that is, obtains the insurance loss information of the target insurance business.

[0106] See also Figure 4 , Figure 4 A schematic block diagram of the structure of a damage assessment device for insurance business provided in an embodiment of the present application.

[0107] like Figure 4 As shown, the insurance business damage assessment device 300 includes an image receiving module 301, an image analysis module 302, an image processing module 303, an image compression module 304, an image transmission module 305, and a damage assessment module 306. The image receiving module 301 is configured to receive damage assessment images corresponding to target insurance businesses to be assessed and, based on the image features of the damage assessment images, determine the object attributes of the target objects in each of the damage assessment images. The object attributes include the object type and damage degree of the target objects. The image analysis module 302 is configured to obtain image labels and first damage assessment information corresponding to each of the damage assessment images based on the object type and damage degree. The image processing module 303 is configured to annotate the damage assessment images based on the image labels to obtain an annotated image, and to preprocess the annotated image using a preset image processing model to obtain a preprocessed image. The image compression module 304 is configured to obtain an image compression strategy for the preprocessed image and compress the preprocessed image according to the strategy to obtain a target compressed image. The image transmission module 305 is configured to transmit at least the target compressed image to the service terminal, so that the service terminal displays the target compressed image on a preset interface. The damage assessment module 306 receives the second damage assessment information obtained by the service terminal and obtains insurance loss information for the target insurance business based on the first and second damage assessment information.

[0108] It can be understood that the insurance business damage assessment device 300 can be applied to a computer device and used to execute the steps of the insurance business damage assessment method provided in any embodiment of the present application.

[0109] It should be noted that, those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working process of the damage assessment device 300 for insurance business described above can refer to the corresponding process in the aforementioned embodiment of the damage assessment method for insurance business, and will not be repeated here.

[0110] In some embodiments, determining the object attributes of the target object in each of the business damage assessment images based on the image features of the business damage assessment images includes:

[0111] Determining an object identification area where the target object is located from the business damage assessment image, and extracting image features of the object identification area;

[0112] The target object in the business damage assessment image is analyzed based on the image features to obtain object attributes of the target object.

[0113] In some embodiments, determining the object identification area where the target object is located from the business damage assessment image includes:

[0114] Identify the object contour of the target object in the business damage assessment image, and determine the object identification area where the target object is located in the business damage assessment image based on the object contour, wherein the object identification area is larger than the area where the object contour is located.

[0115] In some embodiments, the preprocessing of the business annotated image to obtain a preprocessed image includes: using a preset image processing model to compress the business annotated image to obtain a preprocessed image, the image processing model includes an image coding network, a quantization network and an image decoding network, wherein the image coding network is used to convert the business annotated image into a corresponding image feature vector, the quantization network is used to discretize the image feature vector output by the image coding network, and the image coding network is used to restore the discretized image feature vector to obtain the preprocessed image, the image coding network includes at least a residual network or a U-Net network, and the network structures of the image coding network and the image decoding network are symmetrical.

[0116] In some embodiments, obtaining the image compression strategy for the pre-processed image at least based on the tag information includes:

[0117] determining the damage degree of the target object in the pre-processed image based on the tag information, and obtaining device performance parameters and network environment parameters of a service terminal in communication with the server;

[0118] An image compression strategy for the pre-processed image is determined according to at least one of the device performance parameter, the network environment parameter, and the damage degree.

[0119] In some embodiments, determining the image compression strategy for the pre-processed image based on at least one of the device performance parameter, the network environment parameter, and the damage degree includes:

[0120] When the device performance parameter meets the preset performance requirement, the network environment parameter meets the preset data transmission requirement, and the damage degree of the target object is Class I damage, determining that the image compression strategy of the pre-processed image is the first compression strategy;

[0121] When the device performance parameters do not meet the preset performance requirements, or the network environment parameters do not meet the preset data transmission requirements, or the damage degree of the target object is Class II damage, the image compression strategy of the preprocessed image is determined to be the second compression strategy, wherein the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is greater than the image compression rate of the first compression strategy.

[0122] In some embodiments, the network environment parameters include at least network bandwidth, and / or the device performance parameters include at least one of CPU performance and GPU performance. Figure 5 , Figure 5 A schematic diagram of the structure of a server provided in an embodiment of the present application.

[0123] like Figure 5 As shown, the server 20 includes a processor 21 and a memory 22 , and the processor 21 and the memory 22 are connected via a bus 23 , such as an I 2 C (Inter-integrated Circuit) bus.

[0124] Specifically, the processor 21 is used to provide computing and control capabilities to support the operation of the entire server 20. The processor 21 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0125] Specifically, the memory 22 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0126] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the embodiment of the present application, and does not constitute a limitation on the computer device to which the embodiment of the present application is applied. The specific server may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] The processor 21 is configured to execute a computer program stored in the memory, and implement any one of the insurance business loss assessment methods provided in the embodiments of the present application when executing the computer program.

[0128] In some embodiments, the processor 21 is configured to run a computer program stored in the memory and implement the following steps when executing the computer program:

[0129] receiving business damage assessment images corresponding to target insurance businesses to be assessed, and determining object attributes of target objects in each of the business damage assessment images based on image features of the business damage assessment images, the object attributes including the object type of the target object and the degree of damage to the target object;

[0130] Obtaining image labels and first damage assessment information corresponding to each of the business damage assessment images according to the object type and the damage degree;

[0131] Annotating the business damage assessment image according to the image label to obtain a business annotated image, and preprocessing the business annotated image to obtain a preprocessed image, wherein the preprocessed image has label information corresponding to the image label;

[0132] At least obtaining an image compression strategy for the preprocessed image according to the tag information, and compressing the preprocessed image according to the image compression strategy to obtain a target compressed image;

[0133] at least transmitting the target compressed image to a service terminal so that the service terminal displays the target compressed image on a preset interface;

[0134] The second damage assessment information obtained by the service terminal is received, and the insurance loss assessment information of the target insurance business is obtained based on the first damage assessment information and the second damage assessment information.

[0135] In some embodiments, determining the object attributes of the target object in each of the business damage assessment images based on the image features of the business damage assessment images includes:

[0136] Determining an object identification area where the target object is located from the business damage assessment image, and extracting image features of the object identification area;

[0137] The target object in the business damage assessment image is analyzed based on the image features to obtain object attributes of the target object.

[0138] In some embodiments, determining the object identification area where the target object is located from the business damage assessment image includes:

[0139] Identify the object contour of the target object in the business damage assessment image, and determine the object identification area where the target object is located in the business damage assessment image based on the object contour, wherein the object identification area is larger than the area where the object contour is located.

[0140] In some embodiments, the preprocessing of the business annotated image to obtain a preprocessed image includes: using a preset image processing model to compress the business annotated image to obtain a preprocessed image, the image processing model includes an image coding network, a quantization network and an image decoding network, wherein the image coding network is used to convert the business annotated image into a corresponding image feature vector, the quantization network is used to discretize the image feature vector output by the image coding network, and the image coding network is used to restore the discretized image feature vector to obtain the preprocessed image, the image coding network includes at least a residual network or a U-Net network, and the network structures of the image coding network and the image decoding network are symmetrical.

[0141] In some embodiments, obtaining the image compression strategy for the pre-processed image at least based on the tag information includes:

[0142] determining the damage degree of the target object in the pre-processed image based on the tag information, and obtaining device performance parameters and network environment parameters of a service terminal in communication with the server;

[0143] An image compression strategy for the pre-processed image is determined according to at least one of the device performance parameter, the network environment parameter, and the damage degree.

[0144] In some embodiments, determining the image compression strategy for the pre-processed image based on at least one of the device performance parameter, the network environment parameter, and the damage degree includes:

[0145] When the device performance parameter meets the preset performance requirement, the network environment parameter meets the preset data transmission requirement, and the damage degree of the target object is Class I damage, determining that the image compression strategy of the pre-processed image is the first compression strategy;

[0146] When the device performance parameters do not meet the preset performance requirements, or the network environment parameters do not meet the preset data transmission requirements, or the damage degree of the target object is Class II damage, the image compression strategy of the preprocessed image is determined to be the second compression strategy, wherein the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is greater than the image compression rate of the first compression strategy.

[0147] In some embodiments, the network environment parameters include at least network bandwidth, and / or the device performance parameters include at least one of CPU performance and GPU performance.

[0148] It should be noted that, those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working process of the steps of the processor in the server 20 calling the program to execute the damage assessment method for insurance business described above can refer to the corresponding process in the aforementioned embodiment of the damage assessment method for insurance business, and will not be repeated here.

[0149] An embodiment of the present application also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of a loss assessment method for insurance business as provided in any embodiment of the present application specification.

[0150] The storage medium may be an internal storage unit of the server in the aforementioned embodiment, such as a hard disk or memory of the server. The storage medium may also be an external storage device of a computer device, such as a plug-in hard disk equipped on the server, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0151] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0152] It should be understood that the term "and / or" used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0153] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments. The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for loss assessment of insurance business, characterized in that: The method comprises: receiving business damage assessment images corresponding to target insurance businesses to be assessed, and determining object attributes of target objects in each of the business damage assessment images based on image features of the business damage assessment images, the object attributes including the object type of the target object and the degree of damage to the target object; Obtaining image labels and first damage assessment information corresponding to each of the business damage assessment images according to the object type and the damage degree; Annotating the business damage assessment image according to the image label to obtain a business annotated image, and preprocessing the business annotated image to obtain a preprocessed image, wherein the preprocessed image has label information corresponding to the image label; At least obtaining an image compression strategy for the preprocessed image according to the tag information, and compressing the preprocessed image according to the image compression strategy to obtain a target compressed image; at least transmitting the target compressed image to a service terminal so that the service terminal displays the target compressed image on a preset interface; receiving the second damage assessment information acquired by the service terminal, and obtaining insurance loss assessment information of the target insurance business based on the first damage assessment information and the second damage assessment information; Wherein, obtaining the image compression strategy of the pre-processed image at least according to the tag information includes: determining the damage degree of the target object in the pre-processed image based on the tag information, and obtaining device performance parameters and network environment parameters of the service terminal connected to the server for communication; When the device performance parameters meet the preset performance requirements, the network environment parameters meet the preset data transmission requirements, and the damage degree of the target object is Class I damage, the image compression strategy of the pre-processed image is determined to be the first compression strategy; when the device performance parameters do not meet the preset performance requirements, or the network environment parameters do not meet the preset data transmission requirements, or the damage degree of the target object is Class II damage, the image compression strategy of the pre-processed image is determined to be the second compression strategy, the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is greater than the image compression rate of the first compression strategy.

2. The method according to claim 1, characterized in that The determining the object attributes of the target objects in each of the business damage assessment images based on the image features of the business damage assessment images includes: Determining an object identification area where the target object is located from the business damage assessment image, and extracting image features of the object identification area; The target object in the business damage assessment image is analyzed based on the image features to obtain object attributes of the target object.

3. The method according to claim 2, characterized in that The determining, from the business damage assessment image, an object identification area where the target object is located, includes: Identify the object contour of the target object in the business damage assessment image, and determine the object identification area where the target object is located in the business damage assessment image based on the object contour, wherein the object identification area is larger than the area where the object contour is located.

4. The method according to claim 1, wherein The preprocessing of the business annotated image to obtain a preprocessed image includes: The business annotated image is compressed using a preset image processing model to obtain a preprocessed image. The image processing model includes an image coding network, a quantization network, and an image decoding network. The image coding network is used to convert the business annotated image into a corresponding image feature vector. The quantization network is used to discretize the image feature vector output by the image coding network. The image coding network is used to restore the discretized image feature vector to obtain the preprocessed image. The image coding network includes at least a residual network or a U-Net network. The network structures of the image coding network and the image decoding network are symmetrical.

5. The method according to claim 1, wherein The network environment parameters include at least network bandwidth, and / or the device performance parameters include at least one of CPU performance and GPU performance.

6. A device for loss assessment of insurance business, characterized in that: The device comprises: an image receiving module, configured to receive business damage assessment images corresponding to target insurance businesses to be assessed, and determine object attributes of target objects in each of the business damage assessment images based on image features of the business damage assessment images, wherein the object attributes include an object type of the target object and a degree of damage to the target object; An image analysis module, configured to obtain an image label and first damage assessment information corresponding to each of the business damage assessment images according to the object type and the damage degree; an image processing module, which annotates the business damage assessment image according to the image label to obtain a business annotated image, and preprocesses the business annotated image to obtain a preprocessed image, wherein the preprocessed image has label information corresponding to the image label; an image compression module, configured to obtain an image compression strategy for the preprocessed image at least according to the tag information, and compress the preprocessed image according to the image compression strategy to obtain a target compressed image; An image transmission module, configured to transmit at least the target compressed image to a service terminal, so that the service terminal displays the target compressed image on a preset interface; a damage assessment module, receiving the second damage assessment information obtained by the service terminal, and obtaining insurance damage assessment information of the target insurance business based on the first damage assessment information and the second damage assessment information; Wherein, obtaining the image compression strategy of the pre-processed image at least according to the tag information includes: determining the damage degree of the target object in the pre-processed image based on the tag information, and obtaining device performance parameters and network environment parameters of the service terminal connected to the server for communication; When the device performance parameters meet the preset performance requirements, the network environment parameters meet the preset data transmission requirements, and the damage degree of the target object is Class I damage, the image compression strategy of the pre-processed image is determined to be the first compression strategy; when the device performance parameters do not meet the preset performance requirements, or the network environment parameters do not meet the preset data transmission requirements, or the damage degree of the target object is Class II damage, the image compression strategy of the pre-processed image is determined to be the second compression strategy, the damage degree of the target object corresponding to Class II damage is less than the damage degree of the target object corresponding to Class I damage, and the image compression rate of the second compression strategy is greater than the image compression rate of the first compression strategy.

7. A server, characterized in that: Including processor and memory; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the loss assessment method for insurance business according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that When the computer-readable storage medium is executed by one or more processors, the one or more processors are caused to execute the steps of the method for loss assessment of insurance business according to any one of claims 1 to 5.

Images