Agricultural insurance processing method and device, equipment and storage medium

Abstract

The invention relates to the technical field of image processing, and discloses an agricultural insurance processing method, device and equipment and a storage medium, and the method comprises the steps: obtaining the target attribute information of a target agricultural land parcel where an agricultural insurance object is located from a preset agricultural land parcel information base, and the target attribute information comprises the position, the type and the area; performing compliance verification according to the type and the area; under the condition that compliance verification is passed, a high-resolution image of the agricultural insurance object is collected according to the position, and the object is verified according to the high-resolution image; underwriting is carried out under the condition that the label verification is passed; collecting a remote sensing image sequence of the agricultural insurance object according to the position, and performing health risk early warning according to the sequence; and acquiring a disaster damage high-resolution image and a disaster damage remote sensing image of the agricultural insurance object according to the position, and generating a disaster damage claim settlement suggestion according to the disaster damage high-resolution image and the disaster damage remote sensing image. The method can be applied to agricultural insurance processing scenes in the field of financial insurance, and the efficiency and accuracy of agricultural insurance processing can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and in particular to an agricultural insurance processing method, device, equipment and storage medium. BACKGROUND

[0002] In the field of finance and insurance, agricultural insurance not only provides risk protection for agricultural production and ensures the stability of farmers' income, but also promotes agricultural investment and financial innovation, and drives the development of modern agriculture, thereby effectively helping the development of rural economy and social stability, and having important significance for enhancing national food security and promoting sustainable agriculture.

[0003] At present, the processing of agricultural insurance still relies on manual investigation and manual assessment. For example, in the underwriting process, underwriters need to personally visit the agricultural insurance target site, understand the actual situation and conduct risk assessment; in the claim process, claim adjusters also need to verify the damage situation and assess the loss. This not only consumes time and effort, but also affects the efficiency of agricultural insurance processing, and is easily disturbed by human subjective factors, resulting in errors in data or non-uniform assessment standards, affecting the accuracy of insurance processing.

[0004] Therefore, how to improve the efficiency and accuracy of agricultural insurance processing has become a technical problem to be solved. SUMMARY

[0005] The present application provides an agricultural insurance processing method, device, computer equipment and storage medium, which aims to improve the efficiency and accuracy of agricultural insurance processing.

[0006] In a first aspect, an agricultural insurance processing method is provided, comprising:

[0007] In the underwriting link, target attribute information of a target agricultural plot where an agricultural insurance target is located is acquired from a preset agricultural plot information library, wherein the target attribute information includes location, type and area;

[0008] According to the type and the area, compliance verification is performed on the agricultural insurance target;

[0009] In the case that the agricultural insurance target passes the compliance verification, a high-resolution image of the agricultural insurance target is collected according to the location, and the agricultural insurance target is verified according to the high-resolution image;

[0010] In the case that the agricultural insurance target verification passes, the agricultural insurance target is underwritten;

[0011] In the underwriting link, a remote sensing image sequence of the agricultural insurance target is collected according to the location, and a health risk early warning of the agricultural insurance target is performed according to the remote sensing image sequence;

[0012] In the claim settlement link, disaster loss high-resolution images and disaster loss remote sensing images of the agricultural insurance subject are collected according to the position, and disaster loss claim settlement suggestions are generated according to the disaster loss high-resolution images and the disaster loss remote sensing images.

[0013] In a second aspect, an agricultural insurance processing apparatus is provided, comprising:

[0014] An acquisition module is configured to acquire, in the underwriting link, target attribute information of a target agricultural plot in which the agricultural insurance subject is located from a preset agricultural plot information library, wherein the target attribute information comprises a position, a type, and an area.

[0015] A verification module is configured to perform compliance verification on the agricultural insurance subject according to the type and the area.

[0016] An inspection module is configured to, if the agricultural insurance subject passes the compliance verification, collect high-resolution images of the agricultural insurance subject according to the position, and perform inspection on the agricultural insurance subject according to the high-resolution images.

[0017] An underwriting module is configured to, if the agricultural insurance subject passes the inspection, underwrite the agricultural insurance subject.

[0018] An early warning module is configured to, in the insurance link, collect a remote sensing image sequence of the agricultural insurance subject according to the position, and perform health risk early warning on the agricultural insurance subject according to the remote sensing image sequence.

[0019] A claim settlement module is configured to, in the claim settlement link, collect disaster loss high-resolution images and disaster loss remote sensing images of the agricultural insurance subject according to the position, and generate disaster loss claim settlement suggestions according to the disaster loss high-resolution images and the disaster loss remote sensing images.

[0020] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the agricultural insurance processing method when executing the computer program.

[0021] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program implements the steps of the agricultural insurance processing method when executed by a processor.

[0022] In the above-mentioned agricultural insurance processing method, device, computer equipment, and storage medium, during the underwriting stage, target attribute information of the target agricultural plot where the agricultural insurance object is located is obtained from a pre-set agricultural plot information database. This target attribute information includes location, type, and area. Based on the type and area, compliance verification of the agricultural insurance object is performed. If the agricultural insurance object passes the compliance verification, a high-resolution image of the agricultural insurance object is acquired based on its location, and the agricultural insurance object is verified based on the high-resolution image. If the agricultural insurance object passes the verification, insurance is provided. During the insurance process, a remote sensing image sequence of the agricultural insurance object is acquired based on its location, and a health risk warning is issued based on the remote sensing image sequence. During the claims process, high-resolution images and remote sensing images of the disaster damage of the agricultural insurance object are acquired based on its location, and disaster damage claims suggestions are generated based on these images. In this invention, during the underwriting stage, complete attribute information of the target agricultural land plot is quickly obtained from a pre-set land information database, ensuring that the agricultural insurance target is accurately linked to its location. This improves the accuracy of compliance verification, reduces underwriting errors, and the application of high-resolution images enhances verification efficiency, achieving automation and intelligence in compliance verification and target assessment. This significantly improves the accuracy and efficiency of underwriting and effectively mitigates underwriting risks. During the insurance process, remote sensing image sequences provide real-time health monitoring and dynamic risk warnings for the agricultural insurance target, helping to promptly identify potential problems and significantly improving the intelligence and precision of supervision. In the claims process, the application of high-resolution disaster damage images and remote sensing images enables high-precision assessment of disaster damage and quickly generates reasonable claims recommendations based on the assessment results, thereby improving the fairness and efficiency of claims settlement. Thus, the entire process of agricultural insurance, from underwriting and supervision to claims settlement, is automated and intelligently managed, reducing human interference and greatly improving the efficiency and accuracy of agricultural insurance processing. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of an agricultural insurance processing method according to an embodiment of the present invention;

[0025] Figure 2 This is another schematic diagram of the agricultural insurance processing method in one embodiment of the present invention;

[0026] Figure 3is an example diagram of a preset agricultural plot information base in an embodiment of the present application;

[0027] Figure 4 is a structural schematic diagram of an agricultural insurance processing device in an embodiment of the present application;

[0028] Figure 5 is a structural schematic diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0030] The agricultural insurance processing method provided by the embodiment of the application can be applied in a server. In the underwriting link, the server can acquire target attribute information of a target agricultural plot where an agricultural insurance subject is located from a preset agricultural plot information library, wherein the target attribute information includes a location, a type, and an area; the agricultural insurance subject is subjected to compliance verification according to the type and the area; in the case that the agricultural insurance subject passes the compliance verification, a high-resolution image of the agricultural insurance subject is collected according to the location, and the agricultural insurance subject is subjected to subject verification according to the high-resolution image; in the case that the subject verification of the agricultural insurance subject passes, the agricultural insurance subject is underwritten; in the insurance link, a remote sensing image sequence of the agricultural insurance subject is collected according to the location, and the agricultural insurance subject is subjected to health risk early warning according to the remote sensing image sequence; in the claim settlement link, a disaster loss high-resolution image and a disaster loss remote sensing image of the agricultural insurance subject are collected according to the location, and a disaster loss claim settlement suggestion is generated according to the disaster loss high-resolution image and the disaster loss remote sensing image. In the application, in the underwriting link, the complete attribute information of the target agricultural plot is quickly acquired from the preset plot information library, the agricultural insurance subject is accurately linked with the plot where the agricultural insurance subject is located, the accuracy of the compliance verification is improved, the underwriting error is reduced, the application of the high-resolution image improves the subject verification efficiency, the automation and the intelligentization of the compliance verification and the subject verification are realized, the precision and the efficiency of the underwriting are significantly improved, and the underwriting risk is effectively avoided; in the insurance link, the remote sensing image sequence provides real-time health monitoring and dynamic risk early warning for the agricultural insurance subject, helps to discover potential problems in time, and significantly improves the intelligentization and the precision level of the supervision in the insurance link; in the claim settlement link, the application of the disaster loss high-resolution image and the remote sensing image realizes high-precision evaluation of the disaster loss, and a reasonable claim settlement suggestion is quickly generated according to the evaluation result, thereby improving the fairness and the efficiency of the claim settlement. Therefore, the whole-process automation and the intelligentization management of the agricultural insurance from the underwriting, the supervision to the claim settlement are realized, the human factor interference is reduced, and the efficiency and the accuracy of the agricultural insurance processing are greatly improved. The server can be implemented by an independent server or a server cluster composed of multiple servers. The application will be described in detail through specific embodiments.

[0031] Referring to Figure 1 as shown, Figure 1 The agricultural insurance processing method provided by the embodiment of the application can be applied in a server. In the underwriting link, the server can acquire target attribute information of a target agricultural plot where an agricultural insurance subject is located from a preset agricultural plot information library, wherein the target attribute information includes a location, a type, and an area; the agricultural insurance subject is subjected to compliance verification according to the type and the area; in the case that the agricultural insurance subject passes the compliance verification, a high-resolution image of the agricultural insurance subject is collected according to the location, and the agricultural insurance subject is subjected to subject verification according to the high-resolution image; in the case that the subject verification of the agricultural insurance subject passes, the agricultural insurance subject is underwritten; in the insurance link, a remote sensing image sequence of the agricultural insurance subject is collected according to the location, and the agricultural insurance subject is subjected to health risk early warning according to the remote sensing image sequence; in the claim settlement link, a disaster loss high-resolution image and a disaster loss remote sensing image of the agricultural insurance subject are collected according to the location, and a disaster loss claim settlement suggestion is generated according to the disaster loss high-resolution image and the disaster loss remote sensing image. In the application, in the underwriting link, the complete attribute information of the target agricultural plot is quickly acquired from the preset plot information library, the agricultural insurance subject is accurately linked with the plot where the agricultural insurance subject is located, the accuracy of the compliance verification is improved, the underwriting error is reduced, the application of the high-resolution image improves the subject verification efficiency, the automation and the intelligentization of the compliance verification and the subject verification are realized, the precision and the efficiency of the underwriting are significantly improved, and the underwriting risk is effectively avoided; in the insurance link, the remote sensing image sequence provides real-time health monitoring and dynamic risk early warning for the agricultural insurance subject, helps to discover potential problems in time, and significantly improves the intelligentization and the precision level of the supervision in the insurance link; in the claim settlement link, the application of the disaster loss high-resolution image and the remote sensing image realizes high-precision evaluation of the disaster loss, and a reasonable claim settlement suggestion is quickly generated according to the evaluation result, thereby improving the fairness and the efficiency of the claim settlement. Therefore, the whole-process automation and the intelligentization management of the agricultural insurance from the underwriting, the supervision to the claim settlement are realized, the human factor interference is reduced, and the efficiency and the accuracy of the agricultural insurance processing are greatly improved. The server can be implemented by an independent server or a server cluster composed of multiple servers. The application will be described in detail through specific embodiments.

[0032] S10: In the underwriting link, target attribute information of a target agricultural plot where an agricultural insurance subject is located is acquired from a preset agricultural plot information library, wherein the target attribute information includes a location, a type, and an area.

[0033] The agricultural insurance processing method provided by the application can be applied to an agricultural insurance processing scene. Through intelligent tools such as unmanned aerial vehicles and remote sensing satellites, combined with image recognition technology, the agricultural insurance is automatically and intelligently managed in the whole process from pre-insurance underwriting, mid-insurance supervision to post-insurance claim settlement. The agricultural insurance processing efficiency is improved, the accuracy of agricultural insurance processing is greatly improved, and the interference of human factors is reduced, so as to promote the digitalization and intelligent development of agricultural insurance.

[0034] The server can communicate with the remote sensing satellite through the network, and communicate with the unmanned aerial vehicle through the network.

[0035] The agricultural insurance processing method provided by the embodiment of the application will be described in detail below.

[0036] Please refer to Figure 2 Before step S10 of some embodiments, the following steps can also be included.

[0037] S100: Obtain high-resolution remote sensing images of an agricultural area;

[0038] S200: Perform tile segmentation processing on the high-resolution remote sensing images to obtain tiles;

[0039] S300: Perform plot identification processing on the tiles to obtain agricultural plots and detail information of the agricultural plots;

[0040] S400: Perform coding processing on the agricultural plots to obtain codes of the agricultural plots;

[0041] S500: Obtain attribute information of the agricultural plots based on the detail information;

[0042] S600: Construct a mapping relationship between the codes and the attribute information of the agricultural plots to construct a preset agricultural plot information database.

[0043] For step S100, the high-resolution remote sensing images of the agricultural area are captured by the remote sensing satellite, and the high-resolution remote sensing images of the agricultural area can clearly reflect the details of the ground of the agricultural area.

[0044] For step S200, the high-resolution remote sensing images of the agricultural area can be segmented into multiple tiles according to the boundaries of towns or villages.

[0045] For step S300, the image recognition algorithm can be used, such as using a semantic segmentation network (such as DeepLab series) or an instance segmentation network (such as Mask R-CNN, YOLO), etc. The agricultural plots can be efficiently and accurately extracted from each tile, and the detail information of the agricultural plots can be determined, so as to improve the plot identification accuracy.

[0046] The semantic segmentation network or the instance segmentation network is applied to the field identification task after being trained by using specific first training data, such as sample field block images and real type labels thereof.

[0047] For step S400, the agricultural field can be encoded to obtain an encoding of the agricultural field, which is an identifier of the agricultural field and is unique to avoid duplication and confusion.

[0048] For step S500, attribute information of the agricultural field can be obtained based on the detail information of the agricultural field. For example, the location of the agricultural field can be determined according to the boundary in the detail information, wherein the location of the agricultural field can be administrative division information of the agricultural field (such as a city, a county, a township or a town, a village, etc. where the agricultural field is located), or specific latitude and longitude information of the agricultural field; the area of the agricultural field can be calculated according to the boundary in the detail information, and the type, location and area of the agricultural field constitute the attribute information of the agricultural field.

[0049] For step S600, the encoding of the agricultural field can be associated with the attribute information to obtain a mapping relationship between the encoding of the agricultural field and the attribute information, which is a one-to-one correspondence. The boundary of the agricultural field, the encoding of the agricultural field and the mapping relationship are fused with the map of the agricultural area to construct an agricultural field information library (defined as a preset agricultural field information library), so that the map of the agricultural area can visually display the boundary of the agricultural field and the encoding of the agricultural field, and clicking on the encoding of a certain agricultural field can display the attribute information of the agricultural field. In addition, the preset agricultural field information library is editable, and supports users to modify or adjust the boundary or attribute information of the agricultural field on the map of the agricultural area. Figure 3 As shown in Figure 3 is an example diagram of the preset agricultural field information library. In this way, the agricultural field is visually displayed in detail, which not only improves the accessibility and transparency of the agricultural field and its attribute information, but also provides effective and reasonable data support for subsequent underwriting, supervision and claim settlement.

[0050] In this way, in the underwriting link, the insurance information of the agricultural insurance subject is first received, wherein the insurance information mainly includes the insured area, the insured variety, the premium, the insurance amount and the like of the agricultural insurance subject. Then, the target attribute information of the target agricultural field where the agricultural insurance subject is located can be quickly obtained from the preset agricultural field information library.

[0051] In step S10 of some embodiments, target attribute information of a target agricultural plot in which the agricultural insurance subject is located is obtained from the preset agricultural plot information library. The target attribute information can be the code of the target agricultural plot. The code of the target agricultural plot is compared with the preset agricultural plot information library to determine the target attribute information based on the mapping relationship between the codes and the attribute information of the agricultural plots in the preset agricultural plot information library.

[0052] Specifically, the code of the target agricultural plot can be queried first. The code of the target agricultural plot is compared with the preset agricultural plot information library to find the code of the agricultural plot in the preset agricultural plot information library that is consistent with the code of the target agricultural plot. Thus, the attribute information corresponding to the code of the agricultural plot that is consistent with the code of the target agricultural plot is determined based on the mapping relationship between the codes and the attribute information of the agricultural plots. The determined attribute information is the target attribute information.

[0053] Therefore, based on the preset agricultural plot information library, the agricultural insurance subject can be intelligently and accurately linked to the target agricultural plot in which it is located, and errors caused by manual operation are avoided.

[0054] S20: The agricultural insurance subject is subjected to compliance verification according to the type, the underwriting state, and the area.

[0055] The underwriting link covers compliance verification and subject verification.

[0056] After the target attribute information is obtained, the agricultural insurance subject is subjected to compliance verification according to the type and the area of the target agricultural plot in which the agricultural insurance subject is located in the target attribute information, so as to reduce the underwriting risk.

[0057] In step S20 of some embodiments, it can be judged whether the type allows the agricultural insurance subject to be underwritten. In the case where the type allows the agricultural insurance subject to be underwritten, the underwriting state of the target agricultural plot is queried according to the code of the target agricultural plot. In the case where the underwriting state is not underwritten, the area is compared with the insured area of the agricultural insurance subject, and in the case where the area is greater than or equal to the insured area, it is determined that the agricultural insurance subject passes the compliance verification. Alternatively, in the case where the underwriting state is underwriting period, the remaining area except the underwritten area in the area is compared with the insured area of the agricultural insurance subject, and in the case where the remaining area is greater than or equal to the insured area, it is determined that the agricultural insurance subject passes the compliance verification.

[0058] Specifically, it can be determined whether the type of the target agricultural plot allows the agricultural insurance subject to be insured, that is, whether the agricultural insurance subject is consistent with the type of the target agricultural plot where the agricultural insurance subject is located. For example, assuming that the target agricultural plot is a paddy field and the agricultural insurance subject is fruit (such as apples, oranges, etc.), the paddy field is inconsistent with the fruit and the paddy field is not allowed to insure the fruit; assuming that the target agricultural plot is a fish pond and the agricultural insurance subject is a food crop (such as rice, wheat, etc.), the fish pond is consistent with the breeding subject and is inconsistent with the planting subject (such as food crops), and the fish pond is prohibited to insure the food crops.

[0059] In the case where the type of the target agricultural plot does not allow the agricultural insurance subject to be insured, the agricultural insurance subject is not insured.

[0060] In this way, the situation that the agricultural insurance subject is inconsistent with the target agricultural plot where the agricultural insurance subject is located can be effectively avoided, so that the insurance error can be effectively avoided.

[0061] In the case where the type of the target agricultural plot allows the agricultural insurance subject to be insured, the existing agricultural insurance policy can be queried according to the coding of the target agricultural plot to confirm the insurance state of the target agricultural plot.

[0062] If the insurance state of the target agricultural plot is not insured, the area of the target agricultural plot is compared with the insured area. In the case where the area of the target agricultural plot is greater than or equal to the insured area, it is determined that the agricultural insurance subject passes the compliance check; in the case where the area of the target agricultural plot is less than the insured area, it is determined that the agricultural insurance subject does not pass the compliance check. In the case where the agricultural insurance subject does not pass the compliance check, the agricultural insurance subject is not insured, so that the risk brought by the false insured area can be effectively prevented.

[0063] If the insurance state of the target agricultural plot is the insured period, the remaining area except the insured area in the area of the target agricultural plot is compared with the insured area. In the case where the remaining area is greater than or equal to the insured area, it is determined that the agricultural insurance subject passes the compliance check; in the case where the remaining area is less than the insured area, it is determined that the agricultural insurance subject does not pass the compliance check. In the case where the agricultural insurance subject does not pass the compliance check, the agricultural insurance subject is not insured, so that the risk brought by the repeated insurance can be effectively prevented.

[0064] Through the above reasonable compliance check, the accuracy and efficiency of the insurance can be improved, and the insurance risk can be effectively reduced.

[0065] S30: In the case where the agricultural insurance subject passes the compliance check, a high-resolution image of the agricultural insurance subject is collected according to the position, and the agricultural insurance subject is verified according to the high-resolution image.

[0066] To further improve the accuracy of underwriting, when the agricultural insurance subject passes the compliance check, the UAV is planned to fly along a flight route according to the location of the target agricultural plot, so as to control the UAV to fly to the location of the target agricultural plot, take a high-resolution image of the agricultural insurance subject at close range, and then verify the agricultural insurance subject based on the high-resolution image of the agricultural insurance subject. Thus, the automation and efficiency of verification are improved by using the UAV.

[0067] In step S30 of some embodiments, the agricultural insurance subject is verified based on the high-resolution image. The high-resolution image can be subjected to crop identification processing to obtain the crop variety corresponding to the agricultural insurance subject. It is determined whether the crop variety is consistent with the insured variety of the agricultural insurance subject. If the crop variety is consistent with the insured variety, it is determined that the verification of the agricultural insurance subject is passed.

[0068] Specifically, the high-resolution image can be subjected to crop identification processing by using an image recognition algorithm, such as a deep residual network (e.g., ResNet), a regional convolutional neural network (e.g., Faster R-CNN), or a Vision Transformer. The residual network, the regional convolutional neural network, or the Vision Transformer is applied to the crop identification task after being trained with specific second training data, such as sample crop images and their true crop variety labels, to improve the accuracy of crop identification and thereby improve the verification accuracy.

[0069] After identifying the crop variety corresponding to the agricultural insurance subject, it is determined whether the crop variety is consistent with the insured variety of the agricultural insurance subject. If the crop variety is consistent with the insured variety, it is determined that the verification of the agricultural insurance subject is passed. If the crop variety is not consistent with the insured variety, it is determined that the verification of the agricultural insurance subject is not passed. In this way, the risk caused by false reporting of the insured variety can be effectively prevented.

[0070] Through the intelligent verification described above, the accuracy of underwriting can be further improved, and the underwriting risk can be reduced.

[0071] S40: In the case where the verification of the agricultural insurance subject is passed, the agricultural insurance subject is underwritten.

[0072] In the case where the verification of the agricultural insurance subject is not passed, the agricultural insurance subject is not underwritten.

[0073] Thus, in the underwriting link, the compliance check and verification are automated and intelligent, which can reduce manual intervention, significantly improve the accuracy and efficiency of underwriting, and effectively avoid underwriting risks.

[0074] S50: In the insurance-keeping link, a remote sensing image sequence of the agricultural insurance target is collected according to the location, and a health risk warning of the agricultural insurance target is performed according to the remote sensing image sequence.

[0075] After the agricultural insurance target is underwritten, in the insurance-keeping link, a remote sensing image sequence of the agricultural insurance target is collected by a remote sensing satellite according to the location of the target agricultural plot, for example, the remote sensing satellite is controlled to periodically take remote sensing images of the agricultural insurance target, such as taking remote sensing images of the agricultural insurance target once every preset period of one week, half a month or one month, to obtain a plurality of continuous remote sensing images, which constitute the remote sensing image sequence. Thus, a health risk warning of the agricultural insurance target is performed according to the remote sensing image sequence of the agricultural insurance target, so as to early discover abnormal health problems of the agricultural insurance target and issue a warning, so that corresponding intervention measures can be taken in time to reduce the risk of agricultural insurance.

[0076] In step S50 of some embodiments, the health risk warning of the agricultural insurance target according to the remote sensing image sequence can be a vigor identification processing of the agricultural insurance target according to the remote sensing image sequence to obtain a vigor condition of the agricultural insurance target, or a pest and disease identification processing of the agricultural insurance target according to the remote sensing image sequence to obtain a pest and disease condition of the agricultural insurance target, and the health risk warning of the agricultural insurance target is performed according to the vigor condition or the pest and disease condition.

[0077] Specifically, the spatial and temporal features in the remote sensing image sequence can be fully mined by an image recognition algorithm, for example, a recurrent neural network (RNN) or a long short-term memory network (LSTM), and the vigor of the agricultural insurance target, such as the density, coverage, leaf size, fruit change, etc. of the plant, is analyzed accordingly, so as to classify the vigor and output the vigor condition (such as good, general or poor, etc.) of the agricultural insurance target. The recurrent neural network (RNN) or the long short-term memory network (LSTM) is applied to the vigor identification task only after being trained by specific third training data, such as sample crop remote sensing images and their real vigor classification labels, so as to improve the accuracy and efficiency of the vigor identification.

[0078] The image recognition algorithm, such as a convolutional neural network (CNN), a support vector machine (SVM) or a random forest, can also be used to extract typical pest and disease features such as leaf discoloration, spots and wilting from each remote sensing image of the remote sensing image sequence, and then classify the typical pest and disease features in combination with the shape, color, texture, etc. of the typical pest and disease features, to output the pest and disease condition (such as whether there is a pest and disease, or a probability value of the existence of a pest and disease, etc.) of the agricultural insurance target.

[0079] Among them, the convolutional neural network (CNN), support vector machine (SVM) or random forest is trained after using specific fourth training data such as sample crop remote sensing image and its pest and disease classification label, and then applied to pest and disease identification task, which can improve the accuracy and efficiency of pest and disease identification.

[0080] If the growth condition of the agricultural insurance subject is poor or there is pest and disease, it is confirmed that the agricultural insurance subject has a health risk, and a health risk warning is issued, for example:

[0081] “Health risk warning notice:

[0082] Agricultural insurance subject: wheat

[0083] Warning date: x year x month x day

[0084] Warning level: high risk

[0085] Warning details: according to the recent remote sensing monitoring results, your agricultural insurance subject-wheat (wheat field code: xxx) is currently facing a health risk. Compared with the normal growth cycle, the current growth rate is significantly lagging behind, the growth is uneven, and the initial signs of wheat powdery mildew have been found, and the local area is obviously diseased. Therefore, the health risk is high risk.

[0086] Suggestion: Strengthen water and fertilizer management to ensure that wheat gets enough nutrients and water to help it recover growth; for powdery mildew, use disease-resistant varieties or spray special drugs.”

[0087] In this way, through remote sensing satellite monitoring and image recognition technology, the growth of agricultural insurance subjects and pest and disease risks can be monitored in real time and accurately, and timely health risk warnings can be issued, which not only reduces the risk of agricultural insurance, but also provides more accurate agricultural technology support and disaster prevention suggestions for agricultural insurance policyholders. Therefore, the intelligent and accurate degree of supervision in the insurance is improved.

[0088] S60: In the claim settlement link, collect high-resolution images and disaster remote sensing images of the agricultural insurance subject according to the location, and generate disaster claim settlement suggestions according to the high-resolution images and disaster remote sensing images.

[0089] When the agricultural insurance subject appears disaster, enter the claim settlement link. In the claim settlement link, according to the location of the target agricultural plot, the disaster high-resolution images of the agricultural insurance subject are taken by the unmanned aerial vehicle at close range, and the disaster remote sensing images of the whole agricultural insurance subject are taken by the remote sensing satellite, so as to generate disaster claim settlement suggestions according to the disaster high-resolution images and disaster remote sensing images, improve the accuracy and efficiency of claim settlement.

[0090] In step S60 of some embodiments, a disaster loss claim suggestion is generated according to the disaster loss high-resolution image and the disaster loss remote sensing image. The disaster loss claim suggestion can be a disaster loss grade evaluation of the agricultural insurance subject according to the disaster loss high-resolution image and the disaster loss remote sensing image, to obtain a disaster loss grade of the agricultural insurance subject; and the disaster loss claim suggestion is generated according to the disaster loss grade.

[0091] Specifically, the disaster loss high-resolution image and the disaster loss remote sensing image can be respectively input into two input channels of a disaster loss evaluation model based on U-Net (a variant of convolutional neural network). One input channel extracts local scale features from the disaster loss high-resolution image, and the other input channel extracts global scale features from the disaster loss remote sensing image. The local scale features and the global scale features are fused to obtain fused features that can comprehensively reflect the disaster loss area, so that the disaster loss evaluation model based on U-Net can better understand the specific information of the disaster loss area, and thus output a disaster loss segmentation map based on the fused features. Each pixel point in the disaster loss segmentation map has a label. The label 0 represents no damage, the label 1 represents slight damage, and the label 2 represents serious damage.

[0092] Then, a first area ratio of the pixel points with the label 1 in the disaster loss segmentation map is calculated, and a second area ratio of the pixel points with the label 2 in the disaster loss segmentation map is counted. If the first area ratio exceeds 50%, the disaster loss grade is slight; if the second area ratio exceeds 50%, the disaster loss grade is serious; and if the second area is greater than the first area ratio and less than 50%, the disaster loss grade is moderate.

[0093] The disaster loss evaluation model based on U-Net is applied to the disaster loss evaluation task only after being trained with specific fifth training data, such as sample disaster loss images and sample disaster loss remote sensing images and their corresponding actual disaster loss segmentation maps, which helps to improve the accuracy and efficiency of the disaster loss grade evaluation.

[0094] In this way, the disaster loss high-resolution image clearly reflecting the details of the disaster loss is collected by the unmanned aerial vehicle, the disaster loss remote sensing image reflecting the overall situation of the disaster loss is collected by the remote sensing satellite, and the pixel-level disaster loss area segmentation of the disaster loss high-resolution image and the disaster loss remote sensing image is performed by the disaster loss evaluation model based on U-Net, which can effectively and accurately evaluate the disaster loss grade (such as slight, moderate, and serious) and provide a reliable basis for the claim settlement of agricultural insurance.

[0095] Finally, a disaster loss claim suggestion is generated according to the disaster loss grade. For example, according to the characteristics of different disaster loss grades, the compensation ratio and the compensation method are flexibly adjusted. For example, for slight damage, it is suggested to provide fast claim settlement and a small amount of compensation; for moderate damage, it is suggested to provide moderate compensation and support for the recovery of farmers; and for serious disaster loss, it is suggested to provide as comprehensive compensation and post-disaster recovery support as possible. In this way, the claim settlement accuracy and efficiency are improved.

[0096] As can be seen, in the above scheme, a comprehensive and accurate plot information library is constructed in advance by remote sensing satellites and image recognition technology, so that in the underwriting link, through the plot information library and the unmanned aerial vehicle, the automatic compliance verification and the mark checking of the agricultural insurance subject are realized, the underwriting efficiency is improved, and it is ensured that the agricultural insurance subject meets the underwriting requirements; in the insurance link, through remote sensing satellites and image recognition technology, the growth situation and the pest and disease risk of the agricultural insurance subject are monitored in real time and accurately, and timely health risk warning is issued, the intelligent degree and the precision degree of the supervision in the insurance are improved; in the claim settlement link, through remote sensing satellites, unmanned aerial vehicles and image recognition technology, high-precision disaster loss assessment is realized, and reasonable claim settlement suggestions are quickly generated accordingly, and the claim settlement efficiency is improved. Therefore, the above scheme not only improves the efficiency, accuracy and fairness of agricultural insurance, but also effectively reduces the management cost, improves the satisfaction of the insured party, and provides more timely protection and support for the insured party, which has obvious social and economic benefits.

[0097] The agricultural insurance processing method provided in this embodiment of the invention includes the following steps: In the underwriting stage, target attribute information of the target agricultural plot where the agricultural insurance object is located is obtained from a preset agricultural plot information database. This target attribute information includes location, type, and area. Based on the type and area, compliance verification of the agricultural insurance object is performed. If the agricultural insurance object passes the compliance verification, a high-resolution image of the agricultural insurance object is acquired based on its location, and the agricultural insurance object is verified based on the high-resolution image. If the agricultural insurance object passes the verification, insurance is provided. In the insurance process, a remote sensing image sequence of the agricultural insurance object is acquired based on its location, and a health risk warning is issued based on the remote sensing image sequence. In the claims stage, high-resolution images and remote sensing images of the disaster damage of the agricultural insurance object are acquired based on its location, and a disaster damage claims suggestion is generated based on the high-resolution images and remote sensing images of the disaster damage. In this invention, during the underwriting stage, complete attribute information of the target agricultural land plot is quickly obtained from a pre-set land information database, ensuring that the agricultural insurance target is accurately linked to its location. This improves the accuracy of compliance verification, reduces underwriting errors, and the application of high-resolution images enhances verification efficiency, automating and intelligentizing compliance verification and target assessment. This significantly improves the accuracy and efficiency of underwriting and effectively mitigates underwriting risks. During the insurance process, remote sensing image sequences provide real-time health monitoring and dynamic risk warnings for the agricultural insurance target, helping to promptly identify potential problems and significantly improving the intelligence and precision of supervision. In the claims process, the application of high-resolution disaster damage images and remote sensing images enables high-precision disaster damage assessment and quickly generates reasonable claims recommendations based on the assessment results, thereby improving the fairness and efficiency of claims settlement. Thus, the entire process of agricultural insurance, from underwriting and supervision to claims settlement, is automated and intelligently managed, reducing human interference and greatly improving the efficiency and accuracy of agricultural insurance processing.

[0098] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0099] It should be noted that the software tools or components not belonging to our company that appear in the embodiments of this invention are merely illustrative examples and do not represent actual use.

[0100] In one embodiment, an agricultural insurance processing device is provided, which corresponds one-to-one with the agricultural insurance processing method described in the above embodiments. For example... Figure 4 As shown, the agricultural insurance processing device includes an acquisition module 101, a verification module 102, a label verification module 103, an underwriting module 104, an early warning module 105, and a claims module 106. Detailed descriptions of each functional module are as follows:

[0101] The acquisition module 101 is configured to acquire target attribute information of a target agricultural plot in which an agricultural insurance subject is located from a preset agricultural plot information library in an underwriting link, where the target attribute information includes a location, a type, and an area.

[0102] The verification module 102 is configured to perform compliance verification on the agricultural insurance subject according to the type and the area.

[0103] The verification module 103 is configured to, when the agricultural insurance subject passes the compliance verification, collect a high-resolution image of the agricultural insurance subject according to the location, and perform verification on the agricultural insurance subject according to the high-resolution image.

[0104] The underwriting module 104 is configured to, when the verification on the agricultural insurance subject passes, underwrite the agricultural insurance subject.

[0105] The early warning module 105 is configured to, in an in-force link, collect a remote sensing image sequence of the agricultural insurance subject according to the location, and perform health risk early warning on the agricultural insurance subject according to the remote sensing image sequence.

[0106] The claim settlement module 106 is configured to, in a claim settlement link, collect a disaster loss high-resolution image and a disaster loss remote sensing image of the agricultural insurance subject according to the location, and generate a disaster loss claim settlement suggestion according to the disaster loss high-resolution image and the disaster loss remote sensing image.

[0107] In an embodiment, the verification module 103 is specifically configured to:

[0108] perform crop identification processing on the high-resolution image to obtain a crop variety corresponding to the agricultural insurance subject;

[0109] determine whether the crop variety is consistent with an insured crop variety of the agricultural insurance subject;

[0110] when the crop variety is consistent with the insured crop variety, determine that the verification on the agricultural insurance subject passes.

[0111] In an embodiment, the early warning module 105 is specifically configured to:

[0112] perform vigor identification processing on the agricultural insurance subject according to the remote sensing image sequence to obtain a vigor condition of the agricultural insurance subject; or

[0113] perform pest and disease identification processing on the agricultural insurance subject according to the remote sensing image sequence to obtain a pest and disease condition of the agricultural insurance subject;

[0114] perform health risk early warning on the agricultural insurance subject according to the vigor condition or the pest and disease condition.

[0115] In an embodiment, the claim settlement module 106 is specifically configured to:

[0116] perform disaster damage level assessment on the agricultural insurance subject according to the disaster damage high-resolution image and the disaster damage remote sensing image, to obtain a disaster damage level of the agricultural insurance subject;

[0117] generate the disaster damage claim settlement suggestion according to the disaster damage level.

[0118] In an embodiment, the agricultural insurance processing apparatus further comprises a construction module configured to:

[0119] obtain a high-resolution remote sensing image of an agricultural area;

[0120] perform tile segmentation processing on the high-resolution remote sensing image to obtain a tile;

[0121] perform plot identification processing on the tile to obtain an agricultural plot and detail information of the agricultural plot;

[0122] perform coding processing on the agricultural plot to obtain a code of the agricultural plot;

[0123] obtain attribute information of the agricultural plot based on the detail information;

[0124] construct a mapping relationship between the code and the attribute information of the agricultural plot, to construct a preset agricultural plot information database.

[0125] In an embodiment, the obtaining module 101 is specifically configured to:

[0126] obtain a code of the target agricultural plot;

[0127] compare the code of the target agricultural plot with the preset agricultural plot information database, to determine the target attribute information based on the mapping relationship between the code and the attribute information of the agricultural plot in the preset agricultural plot information database.

[0128] In an embodiment, the verification module 102 is specifically configured to:

[0129] determine whether the type allows to underwrite the agricultural insurance subject;

[0130] in a case where the type allows to underwrite the agricultural insurance subject, query an underwriting state of the target agricultural plot according to the code of the target agricultural plot;

[0131] in a case where the underwriting state is ununderwritten, compare the area with an insured area of the agricultural insurance subject, and in a case where the area is greater than or equal to the insured area, determine that the agricultural insurance subject passes the compliance verification; or,

[0132] In a case where the underwriting state is an underwriting period, a remaining area in the area except for an underwritten area is compared with an insured area of the agricultural insurance subject, and in a case where the remaining area is greater than or equal to the insured area, it is determined that the agricultural insurance subject passes the compliance check.

[0133] The present application provides an agricultural insurance processing device. In the present application, in the underwriting link, the complete attribute information of the target agricultural plot is quickly obtained from the preset plot information library to ensure that the agricultural insurance subject is accurately linked with the plot where it is located, thereby improving the accuracy of the compliance check, reducing underwriting errors, and at the same time, the application of high-resolution images improves the efficiency of the subject verification, realizes the automation and intelligentization of the compliance check and the subject verification, significantly improves the precision and efficiency of the underwriting, and effectively avoids the underwriting risk; in the insurance link, the remote sensing image sequence provides real-time health monitoring and dynamic risk early warning for the agricultural insurance subject, helps to discover potential problems in time, and significantly improves the intelligentization and precision level of the supervision in the insurance link; in the claim settlement link, the application of the disaster loss high-resolution image and the remote sensing image realizes high-precision evaluation of the disaster loss, and generates reasonable claim settlement suggestions according to the evaluation results, thereby improving the fairness and efficiency of the claim settlement. Thus, the whole process automation and intelligent management of the agricultural insurance from underwriting, supervision to claim settlement are realized, the human factor interference is reduced, and the efficiency and accuracy of the agricultural insurance processing are greatly improved.

[0134] The specific limitations of the agricultural insurance processing device can be referred to the limitations of the agricultural insurance processing method in the above, which will not be repeated here. Each module in the above agricultural insurance processing device can be realized by software, hardware and their combination in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0135] In one embodiment, a computer device is provided, which can be a server, and its internal structure diagram can be as shown in Figure 5 The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with the external client through the network connection. The computer program is executed by the processor to realize the functions or steps of the server side of the agricultural insurance processing method.

[0136] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

[0137] In the underwriting link, target attribute information of a target agricultural land parcel where the agricultural insurance subject is located is acquired from a preset agricultural land parcel information library, wherein the target attribute information comprises a location, a type, and an area;

[0138] According to the type and the area, compliance verification is performed on the agricultural insurance subject;

[0139] In the case where the agricultural insurance subject passes the compliance verification, a high-resolution image of the agricultural insurance subject is collected according to the location, and the agricultural insurance subject is verified according to the high-resolution image;

[0140] In the case where the agricultural insurance subject verification passes, the agricultural insurance subject is underwritten;

[0141] In the claim settlement link, a remote sensing image sequence of the agricultural insurance subject is collected according to the location, and a health risk early warning is performed on the agricultural insurance subject according to the remote sensing image sequence;

[0142] In the claim settlement link, a disaster loss high-resolution image and a disaster loss remote sensing image of the agricultural insurance subject are collected according to the location, and a disaster loss claim settlement suggestion is generated according to the disaster loss high-resolution image and the disaster loss remote sensing image.

[0143] In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0144] In the underwriting link, target attribute information of a target agricultural land parcel where the agricultural insurance subject is located is acquired from a preset agricultural land parcel information library, wherein the target attribute information comprises a location, a type, and an area;

[0145] According to the type and the area, compliance verification is performed on the agricultural insurance subject;

[0146] In the case where the agricultural insurance subject passes the compliance verification, a high-resolution image of the agricultural insurance subject is collected according to the location, and the agricultural insurance subject is verified according to the high-resolution image;

[0147] In the case where the agricultural insurance subject verification passes, the agricultural insurance subject is underwritten;

[0148] In the insurance link, a remote sensing image sequence of the agricultural insurance target is collected according to the position, and a health risk early warning of the agricultural insurance target is performed according to the remote sensing image sequence.

[0149] In the claim settlement link, a disaster loss high-resolution image and a disaster loss remote sensing image of the agricultural insurance target are collected according to the position, and a disaster loss claim settlement suggestion is generated according to the disaster loss high-resolution image and the disaster loss remote sensing image.

[0150] It should be noted that the functions or steps that can be achieved by the computer readable storage medium or the computer device described above can correspond to the related description of the server side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0151] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0152] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is exemplified. In actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0153] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An agricultural insurance processing method characterized by comprising: The method comprises the following steps: In the underwriting link, target attribute information of a target agricultural plot where an agricultural insurance subject is located is acquired from a preset agricultural plot information library, wherein the target attribute information comprises a location, a type and an area; According to the type and the area, compliance verification is performed on the agricultural insurance subject; In the case where the agricultural insurance subject passes the compliance verification, a high-resolution image of the agricultural insurance subject is collected according to the location, and the agricultural insurance subject is verified according to the high-resolution image; In the case where the agricultural insurance subject verification passes, the agricultural insurance subject is underwritten; In the insurance link, a remote sensing image sequence of the agricultural insurance subject is collected according to the location, and a health risk early warning is performed on the agricultural insurance subject according to the remote sensing image sequence; In the claim settlement link, a disaster loss high-resolution image and a disaster loss remote sensing image of the agricultural insurance subject are collected according to the location, and a disaster loss claim settlement suggestion is generated according to the disaster loss high-resolution image and the disaster loss remote sensing image.

2. The agricultural insurance processing method according to claim 1, characterized by, The verification of the agricultural insurance subject according to the high-resolution image comprises the following steps: crop identification processing is performed on the high-resolution image to obtain a crop variety corresponding to the agricultural insurance subject; it is judged whether the crop variety is consistent with the insured variety of the agricultural insurance subject; in the case where the crop variety is consistent with the insured variety, it is determined that the agricultural insurance subject verification passes.

3. The agricultural insurance processing method according to claim 1, characterized by, The health risk early warning of the agricultural insurance subject according to the remote sensing image comprises the following steps: a growth vigor condition of the agricultural insurance subject is obtained by performing growth vigor identification processing on the agricultural insurance subject according to the remote sensing image sequence; or a pest and disease condition of the agricultural insurance subject is obtained by performing pest and disease identification processing on the agricultural insurance subject according to the remote sensing image sequence; a health risk early warning is performed on the agricultural insurance subject according to the growth vigor condition or the pest and disease condition.

4. The agricultural insurance processing method according to claim 1, characterized by, The generation of the disaster loss claim settlement suggestion according to the disaster loss high-resolution image and the disaster loss remote sensing image comprises the following steps: a disaster loss grade of the agricultural insurance subject is obtained by performing disaster loss grade evaluation on the agricultural insurance subject according to the disaster loss high-resolution image and the disaster loss remote sensing image; the disaster loss claim settlement suggestion is generated according to the disaster loss grade.

5. The agricultural insurance processing method of claim 1, wherein, Before the acquisition of the target attribute information of the target agricultural plot where the agricultural insurance subject is located from the preset agricultural plot information library, the following steps are further included: a high-resolution remote sensing image of an agricultural region is acquired; tile segmentation processing is performed on the high-resolution remote sensing image to obtain a tile; plot identification processing is performed on the tile to obtain an agricultural plot and detail information of the agricultural plot; coding processing is performed on the agricultural plot to obtain a code of the agricultural plot; attribute information of the agricultural plot is acquired based on the detail information; a mapping relationship between the code and the attribute information of the agricultural plot is constructed to construct a preset agricultural plot information library.

6. The agricultural insurance processing method according to claim 5, characterized by, The acquisition of the target attribute information of the target agricultural plot where the agricultural insurance subject is located from the preset agricultural plot information library comprises the following steps: the code of the target agricultural plot is acquired; The code of the target agricultural plot is compared with a preset agricultural plot information base to determine the target attribute information based on a mapping relationship between the codes and attribute information of agricultural plots in the preset agricultural plot information base.

7. The agricultural insurance processing method according to claim 6, characterized by, The compliance verification of the agricultural insurance subject according to the type and the area includes: determining whether the type allows to underwrite the agricultural insurance subject; in the case that the type allows to underwrite the agricultural insurance subject, querying the underwriting state of the target agricultural plot according to the code of the target agricultural plot; in the case that the underwriting state is ununderwritten, comparing the area with the insured area of the agricultural insurance subject, and in the case that the area is greater than or equal to the insured area, determining that the agricultural insurance subject passes the compliance verification; or, in the case that the underwriting state is underwritten, comparing the remaining area except the underwritten area in the area with the insured area of the agricultural insurance subject, and in the case that the remaining area is greater than or equal to the insured area, determining that the agricultural insurance subject passes the compliance verification.

8. An agricultural insurance processing apparatus characterized by comprising: includes: an acquisition module, configured to acquire target attribute information of a target agricultural plot where an agricultural insurance subject is located from a preset agricultural plot information base in an underwriting link, wherein the target attribute information includes location, type and area; a verification module, configured to perform compliance verification of the agricultural insurance subject according to the type and the area; an inspection module, configured to acquire a high-resolution image of the agricultural insurance subject according to the location in the case that the agricultural insurance subject passes the compliance verification, and perform inspection of the agricultural insurance subject according to the high-resolution image; an underwriting module, configured to underwrite the agricultural insurance subject in the case that the agricultural insurance subject passes the inspection; an early warning module, configured to acquire a remote sensing image sequence of the agricultural insurance subject according to the location in a maintenance link, and perform health risk early warning of the agricultural insurance subject according to the remote sensing image sequence; a claim settlement module, configured to acquire a disaster loss high-resolution image and a disaster loss remote sensing image of the agricultural insurance subject according to the location in a claim settlement link, and generate a disaster loss claim settlement suggestion according to the disaster loss high-resolution image and the disaster loss remote sensing image.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the agricultural insurance processing method in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the agricultural insurance processing method in any one of claims 1 to 7.

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