Agricultural credit risk assessment method and device, equipment and storage medium

Abstract

The invention relates to the technical field of financial science and technology, in particular to an agricultural credit risk assessment method and device, equipment and a storage medium, and the method comprises the steps: determining a target agricultural production region corresponding to a target agricultural user; acquiring original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculating current crop growth indexes corresponding to the growth stages one by one based on the original remote sensing spectral data; obtaining growth index data based on the current growth indexes of the crops and historical healthy growth indexes of the crops in one-to-one correspondence with the current growth indexes of the crops; and collecting geographic index data corresponding to the target agricultural production area, and obtaining a target risk assessment result based on the growth index data, the geographic index data and a preset risk assessment model. According to the invention, the accuracy of agricultural credit risk assessment is improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to an agricultural credit risk assessment method, device, equipment and storage medium. Background Art

[0002] In the agricultural sector, credit is one of the important driving forces for agricultural development. Agricultural credit can not only help agricultural users purchase agricultural production materials such as seeds, fertilizers, and pesticides, but also support agricultural users in land improvement, equipment renewal, agricultural technology training and other activities, thereby promoting the improvement of agricultural production and the increase of agricultural users' income. When agricultural users apply for agricultural credit, banks need to conduct credit risk assessments on agricultural users to ensure the safety and effective use of credit funds.

[0003] At present, the way banks conduct credit risk assessment on users is to obtain indicator data such as the financial status and collateral materials of agricultural users who apply for credit, such as income data, asset data, credit record data, etc., and then determine the credit risk level of agricultural users based on the indicator data such as the financial status and collateral materials of agricultural users.

[0004] However, the above-mentioned financial status, collateral and other indicator data are not only easily subject to unilateral control by agricultural users, but agricultural production is also affected by factors such as seasonality, climate change, and natural disasters. If the credit risk level of agricultural users is directly assessed based on static historical indicator data such as the financial status of agricultural users and collateral materials, the accuracy of agricultural credit risk assessment will be poor. Summary of the invention

[0005] In order to improve the accuracy of agricultural credit risk assessment, the present application provides an agricultural credit risk assessment method, device, equipment and storage medium.

[0006] In a first aspect, the present application provides an agricultural credit risk assessment method, comprising:

[0007] Determine the target agricultural production areas corresponding to the target agricultural users;

[0008] Collecting original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data;

[0009] Obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop;

[0010] The geographical indicator data corresponding to the target agricultural production area is collected, and a target risk assessment result is obtained based on the growth indicator data, the geographical indicator data and a preset risk assessment model.

[0011] Through the above implementation, growth indicator data and geographical indicator data related to agricultural use are collected. The growth indicator data and geographical indicator data can be used to easily judge the current growth conditions and future growth expectations of crops in the target agricultural production area. The growth indicator data and geographical indicator data are analyzed through a risk assessment model to determine the target risk assessment results. During the risk assessment process, the collected data is not subject to the unilateral control of agricultural users, and growth indicator data and geographical indicator data related to crop growth are collected to facilitate the determination of the impact of factors such as seasonality, climate change, and natural disasters on agricultural production, thereby improving the accuracy of agricultural credit risk assessment.

[0012] Preferably, the step of calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data includes:

[0013] Calibrate the original remote sensing spectrum data corresponding to each growth stage to obtain calibration data corresponding to each growth stage;

[0014] Correcting the calibration data corresponding to each of the growth stages to obtain target remote sensing spectrum data corresponding to each of the growth stages;

[0015] The current growth index of the crop corresponding to each growth stage is calculated based on the target remote sensing spectral data corresponding to each growth stage.

[0016] Through the above implementation, the original remote sensing spectral data can be preprocessed by calibrating and correcting the original remote sensing spectral data, so as to facilitate the subsequent agricultural credit risk assessment based on the preprocessed original remote sensing spectral data, thereby further improving the accuracy of agricultural credit risk assessment.

[0017] Preferably, the target remote sensing spectrum data includes infrared band data and red light band data; and the step of calculating the current growth index of the crop corresponding to each growth stage based on the target remote sensing spectrum data corresponding to each growth stage includes:

[0018] Calculate the difference between the infrared band data and the red light band data corresponding to each growth stage to obtain a target difference;

[0019] Calculate the sum of the infrared band data and the red light band data corresponding to each growth stage to obtain a target sum value;

[0020] Based on the target difference value corresponding to each growth stage and the target sum value, a current growth index of the crop corresponding to each growth stage is obtained.

[0021] Through the above implementation, the corresponding current crop growth index is calculated according to the infrared band data and the red light band data, so as to provide a data basis for the subsequent calculation of growth index data.

[0022] Preferably, the growth index data is obtained based on the crop historical health growth index corresponding to each crop current growth index, including:

[0023] The difference between the current growth index of each crop and the historical healthy growth index of the crop corresponding to the current growth index of each crop is calculated respectively to obtain growth index data.

[0024] Through the above implementation, by calculating the difference with the corresponding historical healthy growth index of the crop, it is convenient to calculate the growth index data.

[0025] Preferably, the geographical indicator data include: soil type data, climate data and topographic data.

[0026] Through the above implementation, the content of public geographic indicator data and the enrichment of public geographic indicator data will facilitate the improvement of the accuracy of agricultural credit risk assessment.

[0027] Preferably, obtaining the target risk assessment result based on the growth indicator data, the geographical indicator data and a preset risk assessment model includes:

[0028] Based on a preset risk assessment model, the growth indicator data and the geographical indicator data are processed respectively to obtain a growth assessment classification and a geographical assessment classification;

[0029] A target risk assessment result is obtained based on the growth assessment classification and the geographic assessment classification.

[0030] Through the above implementation, the growth assessment classification and the geographical assessment classification are calculated respectively, and the target risk assessment results are calculated by combining the growth assessment classification and the geographical assessment classification, which fully considers the factors affecting the target risk assessment results, thereby facilitating improving the accuracy of the calculated target risk assessment results.

[0031] In a second aspect, the present application provides an agricultural credit risk assessment device, comprising:

[0032] A region determination module, used to determine the target agricultural production region corresponding to the target agricultural user;

[0033] An index calculation module, used to collect original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculate the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data;

[0034] A data calculation module, used for obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop;

[0035] The result evaluation module is used to collect the geographical indicator data corresponding to the target agricultural production area, and obtain the target risk evaluation result based on the growth indicator data, the geographical indicator data and a preset risk evaluation model.

[0036] Through the above implementation, growth indicator data and geographical indicator data related to agricultural use are collected. The growth indicator data and geographical indicator data can be used to easily judge the current growth conditions and future growth expectations of crops in the target agricultural production area. The growth indicator data and geographical indicator data are analyzed through a risk assessment model to determine the target risk assessment results. During the risk assessment process, the collected data is not subject to the unilateral control of agricultural users, and growth indicator data and geographical indicator data related to crop growth are collected to facilitate the determination of the impact of factors such as seasonality, climate change, and natural disasters on agricultural production, thereby improving the accuracy of agricultural credit risk assessment.

[0037] In a third aspect, the present application provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method when executing the computer program.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above method when executed by a processor.

[0039] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0040] The above-mentioned agricultural credit risk assessment method, device, equipment and storage medium determine the target agricultural production area corresponding to the target agricultural user; collect the original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculate the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data; obtain growth index data based on the current growth index of each crop and the historical health growth index of the crop corresponding to the current growth index of each crop; collect the geographical indicator data corresponding to the target agricultural production area, and obtain the target risk assessment result based on the growth index data, the geographical indicator data and a preset risk assessment model. Through the above implementation, growth indicator data and geographical indicator data related to agricultural use are collected. The growth indicator data and geographical indicator data can be used to easily judge the current growth conditions and future growth expectations of crops in the target agricultural production area. The growth indicator data and geographical indicator data are analyzed through a risk assessment model to determine the target risk assessment results. During the risk assessment process, the collected data is not subject to the unilateral control of agricultural users, and growth indicator data and geographical indicator data related to crop growth are collected to facilitate the determination of the impact of factors such as seasonality, climate change, and natural disasters on agricultural production, thereby improving the accuracy of agricultural credit risk assessment.

[0041] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A flow chart of an agricultural credit risk assessment method provided in an embodiment of the present application;

[0044] Figure 2 This is a schematic diagram of the structure of an agricultural credit risk assessment device provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application;

[0046] Figure 4 This is a diagram of the internal structure of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0049] In this article, the term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the related objects before and after are in an "or" relationship.

[0050] Embodiment 1

[0051] Figure 1 A flow chart of an agricultural credit risk assessment method provided in Example 1 of this application, refer to Figure 1 The method may be performed by a device for performing the method, and the device may be implemented by software and / or hardware. The method includes:

[0052] S110. Determine the target agricultural production area corresponding to the target agricultural user.

[0053] Among them, the target agricultural users are users who apply for agricultural credit loans from the bank. With the customer's authorization, the bank system determines the target agricultural production area corresponding to the target agricultural users based on the application information provided by the target agricultural users when applying for agricultural credit loans from the bank. The target agricultural production area is the geographical area where the target agricultural users conduct agricultural production. By way of example, the target agricultural production area is the town or city where the target agricultural users conduct agricultural production.

[0054] Specifically, the banking system determines the target agricultural production area corresponding to the target agricultural user based on the application information provided by the target agricultural user when applying for an agricultural credit loan from the bank.

[0055] S120, collecting original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data.

[0056] Among them, in agricultural production, in order to facilitate understanding of the distribution, growth status and other information of agricultural crops, the agricultural department uses remote sensing technology to obtain remote sensing spectral data of crops at different growth stages of preset crops; the bank system can connect data with relevant agricultural departments, so as to collect remote sensing spectral data corresponding to the crops currently growing in the target agricultural production area at different growth stages, as the original remote sensing spectral data corresponding to each growth stage of the target agricultural production area; in this embodiment, the crop can be one or more of soybeans, corn and rice. In this embodiment, after the sowing of the crop is completed, a cycle of 15 days is taken as a growth stage of the crop. Each growth stage of the crop can collect one-to-one corresponding original remote sensing spectral data. Taking the original remote sensing spectral data collected in one growth stage as an example, the original remote sensing spectral data can be processed and calculated accordingly to calculate the current growth index of the crop corresponding to the growth stage. The current growth index of the crop is used to characterize the surface crop coverage and growth of the crop in the growth stage.

[0057] Specifically, the banking system obtains the original remote sensing spectral data corresponding to crops at different growth stages from relevant agricultural departments, and then processes and calculates the original remote sensing spectral data corresponding to each growth stage accordingly, so as to obtain the current growth index of crops corresponding to each growth stage.

[0058] S130, obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop.

[0059] Among them, each growth stage of the crop has a corresponding current crop growth index, and the crop history data stored by the agricultural department also records the growth index of each growth stage of the crop under healthy growth in history, and the index is recorded as the crop historical healthy growth index; that is, each growth stage of the crop not only has a corresponding current crop growth index, but also a corresponding historical healthy growth index of the crop; taking one of the growth stages as an example, by processing the current crop growth index and the historical healthy growth index of the crop corresponding to the growth stage, the growth index corresponding to the growth stage can be calculated, and the growth index corresponding to each growth stage can constitute the growth index data, which is used to characterize whether the crop grows normally and whether it is earlier than pests and diseases, so as to characterize whether the growth process of the crop meets expectations. The growth index data is the first indicator used in this embodiment to evaluate agricultural credit risk.

[0060] Specifically, the current growth index of the crop and the historical healthy growth index of the crop corresponding to each growth stage of the crop are obtained, and then the current growth index of the crop and the historical healthy growth index of the crop corresponding to each growth stage of the crop are calculated respectively, so as to obtain the growth index corresponding to each growth stage, and the growth index data are obtained by combining the growth indicators.

[0061] S140, collecting geographical indicator data corresponding to the target agricultural production area, and obtaining a target risk assessment result based on the growth indicator data, the geographical indicator data and a preset risk assessment model.

[0062] The geographic indicator data is the geographic data corresponding to the target agricultural production area at each growth stage of the above-mentioned crops, and the geographic indicator data is the second indicator used in this embodiment to assess agricultural credit risk. The risk assessment model is a pre-trained assessment model, which is used to process the growth indicator data and the geographic indicator data to obtain a target risk assessment result, which is used to characterize the estimated agricultural credit risk level of the target agricultural user.

[0063] Specifically, geographical indicator data corresponding to the target agricultural production area are collected, and then the growth indicator data and the geographical indicator data are input into a preset risk assessment model for processing to obtain the target risk assessment result.

[0064] It should be noted that, in this embodiment, the target agricultural production area corresponding to the target agricultural user is determined; the original remote sensing spectral data corresponding to each growth stage of the target agricultural production area is collected, and the current crop growth index corresponding to each growth stage is calculated based on the original remote sensing spectral data; the growth index data is obtained based on the current growth index of each crop and the historical healthy growth index of each crop corresponding to the current growth index of each crop; the geographical indicator data corresponding to the target agricultural production area is collected, and the target risk assessment result is obtained based on the growth indicator data, the geographical indicator data and the preset risk assessment model. Through the above implementation, the growth indicator data and geographical indicator data related to agricultural use are collected, and the current growth situation and future growth expectation of crops in the target agricultural production area can be judged by the growth indicator data and the geographical indicator data. The growth indicator data and the geographical indicator data are analyzed by the risk assessment model, so as to determine the target risk assessment result. In the risk assessment process, the collected data is not unilaterally controlled by the agricultural user, and the growth indicator data and the geographical indicator data related to crop growth are collected, so as to determine the impact of seasonality, climate change, natural disasters and other factors on agricultural production, so as to improve the accuracy of agricultural credit risk assessment.

[0065] Embodiment 2

[0066] A method for assessing agricultural credit risk is provided in Example 2 of the present application. The method optimizes the "calculating the current growth index of crops corresponding to each growth stage based on the original remote sensing spectral data" in Example 1. It should be noted that for the parts not described in detail in this embodiment, reference may be made to the descriptions of other embodiments. The method includes:

[0067] S210. Determine the target agricultural production area corresponding to the target agricultural user.

[0068] S221. Collect original remote sensing spectral data corresponding to each growth stage of the target agricultural production area.

[0069] S222, calibrating the original remote sensing spectral data corresponding to each of the growth stages to obtain calibration data corresponding to each of the growth stages.

[0070] It should be noted that the original remote sensing spectral data is the light band data of the corresponding crop. The light band cannot be directly used to calculate the current growth index of the crop. The original remote sensing spectral data must be calibrated first. Calibration is to convert the original remote sensing spectral data into a radiation brightness value or reflectance value with physical meaning. The original remote sensing spectral data can be converted into a radiation brightness value or reflectance value with physical meaning through a corresponding conversion calculation formula, and the result obtained after converting the original remote sensing spectral data through the corresponding conversion calculation formula is recorded as calibration data.

[0071] Specifically, taking the original remote sensing spectrum data corresponding to one growth stage as an example, the original remote sensing spectrum data is processed by a corresponding conversion calculation formula to obtain corresponding calibration data.

[0072] S223, correcting the calibration data corresponding to each of the growth stages to obtain target remote sensing spectrum data corresponding to each of the growth stages.

[0073] It should be noted that the data in the calibration data is data affected by the atmosphere during the remote sensing process. In order to obtain data reflecting the true radiation brightness value or reflectance value, the calibration data needs to be corrected. This embodiment uses a preset correction model to correct the calibration data, and the target remote sensing spectrum data is the data obtained after the calibration data is corrected. By correcting the calibration data, data reflecting the true radiation brightness value or reflectance value can be obtained, which is convenient for improving the accuracy of the subsequent calculated current growth index of the crop.

[0074] Specifically, taking one of the growth stages as an example, the calibration data corresponding to the growth stage is input into a preset correction model for processing, thereby obtaining target remote sensing spectral data.

[0075] S224, calculating the current growth index of the crop corresponding to each growth stage based on the target remote sensing spectral data corresponding to each growth stage.

[0076] The corresponding target remote sensing spectral data can be calculated for each growth stage, and further calculation of the target remote sensing spectral data can calculate the current growth index of the crop corresponding to each growth stage.

[0077] Specifically, taking one of the growth stages as an example, further calculation is performed on the target remote sensing spectral data corresponding to the growth stage, so as to obtain the current growth index of the crop corresponding to the growth stage.

[0078] S230, obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop.

[0079] S240, collecting geographical indicator data corresponding to the target agricultural production area, and obtaining a target risk assessment result based on the growth indicator data, the geographical indicator data and a preset risk assessment model.

[0080] Embodiment 3

[0081] A method for assessing agricultural credit risk is provided in Example 3 of the present application. The method optimizes the "target remote sensing spectral data" and "calculating the current growth index of crops corresponding to each growth stage based on the target remote sensing spectral data corresponding to each growth stage" in Example 2; it should be noted that for the parts not described in detail in this embodiment, reference can be made to the descriptions of other embodiments. The method includes:

[0082] S310. Determine the target agricultural production area corresponding to the target agricultural user.

[0083] S321. Collect original remote sensing spectral data corresponding to each growth stage of the target agricultural production area.

[0084] S322, calibrating the original remote sensing spectral data corresponding to each of the growth stages to obtain calibration data corresponding to each of the growth stages.

[0085] S323, correcting the calibration data corresponding to each of the growth stages to obtain target remote sensing spectrum data corresponding to each of the growth stages.

[0086] The target remote sensing spectrum data includes infrared band data and red light band data.

[0087] It should be noted that the infrared band data NIR in this embodiment is specifically the infrared band emissivity value corresponding to the target agricultural production area at the corresponding growth stage, and the red light band data Red in this embodiment is specifically the red band emissivity value corresponding to the target agricultural production area at the corresponding growth stage.

[0088] S3241. Calculate the difference between the infrared band data and the red light band data corresponding to each growth stage to obtain a target difference.

[0089] Taking one of the growth stages as an example, the target difference is the difference between the infrared band data NIR and the red band data Red corresponding to the growth stage, that is, NIR-Red.

[0090] S3242. Calculate the sum of the infrared band data and the red light band data corresponding to each growth stage to obtain a target sum value.

[0091] Taking one of the growth stages as an example, the target sum value is the sum of the infrared band data NIR and the red light band data Red corresponding to the growth stage, that is, NIR+Red.

[0092] S3243. Obtain the current growth index of the crop corresponding to each growth stage based on the target difference value corresponding to each growth stage and the target sum value.

[0093] In this embodiment, taking one of the growth stages as an example, the current crop growth index NDVI is the ratio of the target difference value to the target sum value of the corresponding growth stage, that is, NDVI=(NIR-Red) / (NIR+Red).

[0094] It should be noted that the NDVI value range is usually between -1 and +1. The higher the value, the denser the crop cover, and the lower the value, the sparser the crop or the land is covered by non-vegetation cover such as water bodies and buildings. Generally speaking:

[0095] NDVI>0: indicates an area covered with crops, and the higher the value, the more lush the crops;

[0096] NDVI < 0: indicates areas without crop cover, which may be water bodies or bare land;

[0097] NDVI=0: Indicates that the land surface is bare rocks, buildings, etc.

[0098] S330, obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop.

[0099] S340, collecting geographical indicator data corresponding to the target agricultural production area, and obtaining a target risk assessment result based on the growth indicator data, the geographical indicator data and a preset risk assessment model.

[0100] Embodiment 4

[0101] A method for assessing agricultural credit risk is provided in Example 2 of the present application. The method optimizes the "obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop" in Example 1; it should be noted that for the parts not described in detail in this example, reference can be made to the descriptions of other examples. The method includes:

[0102] S410: Determine the target agricultural production area corresponding to the target agricultural user.

[0103] S420, collecting original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data.

[0104] S430, respectively calculating the difference between the current growth index of each crop and the historical healthy growth index of the crop corresponding to the current growth index of each crop, to obtain growth index data.

[0105] Among them, taking one of the growth stages as an example, the growth stage has a corresponding current crop growth index and a historical healthy growth index of the crop. The growth index corresponding to the growth stage is the difference between the current crop growth index and the historical healthy growth index of the crop corresponding to the growth stage; the growth index data is a collection of growth indicators corresponding to each growth stage.

[0106] S440, collecting geographical indicator data corresponding to the target agricultural production area, and obtaining a target risk assessment result based on the growth indicator data, the geographical indicator data and a preset risk assessment model.

[0107] Embodiment 5

[0108] A method for assessing agricultural credit risk is provided in Example 5 of the present application. The method optimizes the "geographical indicator data" in Example 1. It should be noted that for the parts not described in detail in this embodiment, reference may be made to the descriptions of other embodiments. The method includes:

[0109] S510: Determine the target agricultural production area corresponding to the target agricultural user.

[0110] S520, collecting original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data.

[0111] S530: Obtain growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop.

[0112] S540, collecting geographical indicator data corresponding to the target agricultural production area, and obtaining a target risk assessment result based on the growth indicator data, the geographical indicator data and a preset risk assessment model.

[0113] The geographical indicator data include soil type data, climate data and topographic data.

[0114] It should be noted that soil type data is used to characterize the soil type corresponding to the target agricultural production area, which includes but is not limited to sandy soil, loam, clay, chestnut soil, saline-alkali soil and paddy soil; climate data is used for the climate corresponding to the target agricultural production area, such as subtropical humid climate; topography data is used to characterize the topography corresponding to the target agricultural production area, such as plain landform, basin landform, etc.

[0115] Embodiment 6

[0116] A method for assessing agricultural credit risk is provided in Example 6 of the present application. The method optimizes the "obtaining a target risk assessment result based on the growth indicator data, the geographic indicator data and the preset risk assessment model" in Example 1. It should be noted that for the parts not described in detail in this example, reference may be made to the descriptions of other examples. The method includes:

[0117] S610: Determine the target agricultural production area corresponding to the target agricultural user.

[0118] S620, collecting original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data.

[0119] S630: Obtain growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop.

[0120] S641. Collect geographical indicator data corresponding to the target agricultural production area.

[0121] S642. Based on a preset risk assessment model, the growth indicator data and the geographic indicator data are processed separately to obtain a growth assessment classification and a geographic assessment classification.

[0122] Among them, the risk assessment model is used to process the growth indicator data input therein, and output the growth assessment classification corresponding to the growth indicator data, the growth assessment classification is used to characterize the categories of the growth trend quality level of crops in the target agricultural production area, and exemplarily, the growth assessment classifications include: excellent, medium, and poor; the risk assessment model is also used to process the geographical indicator data input therein, and output the geographical assessment classification corresponding to the geographical indicator data, the geographical assessment classification is used to characterize the categories of the future growth trend quality level of crops in the corresponding target agricultural production area, and exemplarily, the geographical assessment classifications include: excellent, medium, and poor.

[0123] Specifically, the growth indicator data is input into a preset risk assessment model for processing, thereby obtaining a growth assessment classification corresponding to the growth indicator data; the geographical indicator data is also input into a preset risk assessment model for processing, thereby obtaining a geographical assessment classification corresponding to the geographical indicator data.

[0124] S643. Obtain a target risk assessment result based on the growth assessment classification and the geographic assessment classification.

[0125] Among them, the target risk assessment result is the assessment classification with a lower classification level between the growth assessment classification and the geographic assessment classification. For example, assuming that the growth assessment classification is "excellent" and the geographic assessment classification is "medium", "medium" will be used as the target risk assessment result.

[0126] Specifically, the growth assessment classification and the assessment classification with a lower classification level in the geographical assessment classification are used as the target risk assessment result.

[0127] It should be noted that the above agricultural credit risk assessment method has a more comprehensive and objective data source, a more accurate and timely assessment capability, and can improve the accuracy and efficiency of agricultural credit, reduce credit risk, and promote the sustainable development of the agricultural industry compared to traditional methods. The invention can be widely used in various agricultural credit institutions, including banks, rural credit cooperatives, etc., to help them more accurately assess the credit risk of agricultural projects, reduce credit risks and losses, and promote the sustainable development of the agricultural industry.

[0128] Traditional agricultural credit assessment methods often rely on farmers' historical data and claims, and lack real-time monitoring methods. The above agricultural credit risk assessment method monitors crop growth in real time, provides objective data support, reduces information asymmetry, and improves loan accuracy.

[0129] By monitoring the NDVI index of crops, abnormal crop growth or disasters can be discovered in a timely manner, helping lending institutions predict possible income fluctuations, reduce loan risks, and avoid repayment risks caused by poor crop yields.

[0130] The above agricultural credit risk assessment method can help lending institutions better understand the production status of farmers and provide farmers with more appropriate credit support, thereby promoting the sustainable development of agricultural production. By reducing loan risks, farmers can more easily obtain loan support, thereby improving agricultural production efficiency and farmers' income levels.

[0131] The above-mentioned agricultural credit risk assessment method combines remote sensing technology, data analysis and financial business, promotes the integrated development of science and technology and finance, and injects new vitality and innovation into the field of agricultural finance.

[0132] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0133] Embodiment 7

[0134] Based on the same inventive concept, this embodiment also provides an agricultural credit risk assessment device for implementing the agricultural credit risk assessment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more agricultural credit risk assessment device embodiments provided below can refer to the limitations of the agricultural credit risk assessment method above, and will not be repeated here.

[0135] In this embodiment, Figure 2 As shown, an agricultural credit risk assessment device is provided, comprising:

[0136] A region determination module, used to determine the target agricultural production region corresponding to the target agricultural user;

[0137] An index calculation module, used to collect original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculate the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data;

[0138] A data calculation module, used for obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop;

[0139] The result evaluation module is used to collect the geographical indicator data corresponding to the target agricultural production area, and obtain the target risk evaluation result based on the growth indicator data, the geographical indicator data and a preset risk evaluation model.

[0140] Each module in the above agricultural credit risk assessment device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0141] It should be noted that, in this embodiment, the target agricultural production area corresponding to the target agricultural user is determined; the original remote sensing spectral data corresponding to each growth stage of the target agricultural production area is collected, and the current crop growth index corresponding to each growth stage is calculated based on the original remote sensing spectral data; the growth index data is obtained based on the current growth index of each crop and the historical healthy growth index of each crop corresponding to the current growth index of each crop; the geographical indicator data corresponding to the target agricultural production area is collected, and the target risk assessment result is obtained based on the growth indicator data, the geographical indicator data and the preset risk assessment model. Through the above implementation, the growth indicator data and geographical indicator data related to agricultural use are collected, and the current growth situation and future growth expectation of crops in the target agricultural production area can be judged by the growth indicator data and the geographical indicator data. The growth indicator data and the geographical indicator data are analyzed by the risk assessment model, so as to determine the target risk assessment result. In the risk assessment process, the collected data is not unilaterally controlled by the agricultural user, and the growth indicator data and the geographical indicator data related to crop growth are collected, so as to determine the impact of seasonality, climate change, natural disasters and other factors on agricultural production, so as to improve the accuracy of agricultural credit risk assessment.

[0142] Embodiment 8

[0143] In this embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and 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 database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an agricultural credit risk assessment method is implemented.

[0144] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0145] Embodiment 9

[0146] In this embodiment, a computer readable storage medium is provided. Figure 4 As shown, a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0147] Embodiment 10

[0148] In this embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present disclosure may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present disclosure may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0151] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] The above-described embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the attached claims.

Claims

1. A method for assessing agricultural credit risk, characterized in that: include: Determine the target agricultural production areas corresponding to the target agricultural users; Collecting original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data; Obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop; The geographical indicator data corresponding to the target agricultural production area is collected, and a target risk assessment result is obtained based on the growth indicator data, the geographical indicator data and a preset risk assessment model.

2. The method according to claim 1, characterized in that The calculating the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data comprises: Calibrate the original remote sensing spectrum data corresponding to each growth stage to obtain calibration data corresponding to each growth stage; Correcting the calibration data corresponding to each of the growth stages to obtain target remote sensing spectrum data corresponding to each of the growth stages; The current growth index of the crop corresponding to each growth stage is calculated based on the target remote sensing spectral data corresponding to each growth stage.

3. The method according to claim 2, characterized in that The target remote sensing spectrum data includes infrared band data and red light band data; the current growth index of the crop corresponding to each growth stage is calculated based on the target remote sensing spectrum data corresponding to each growth stage, including: Calculate the difference between the infrared band data and the red light band data corresponding to each growth stage to obtain a target difference; Calculate the sum of the infrared band data and the red light band data corresponding to each growth stage to obtain a target sum value; Based on the target difference value corresponding to each growth stage and the target sum value, a current growth index of the crop corresponding to each growth stage is obtained.

4. The method according to claim 1, characterized in that: The growth index data is obtained based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop, including: The difference between the current growth index of each crop and the historical healthy growth index of the crop corresponding to the current growth index of each crop is calculated respectively to obtain growth index data.

5. The method according to claim 1, characterized in that The geographical indicator data include: soil type data, climate data and topography data.

6. The method according to claim 1, characterized in that The target risk assessment result is obtained based on the growth indicator data, the geographical indicator data and a preset risk assessment model, including: Based on a preset risk assessment model, the growth indicator data and the geographical indicator data are processed respectively to obtain a growth assessment classification and a geographical assessment classification; A target risk assessment result is obtained based on the growth assessment classification and the geographic assessment classification.

7. An agricultural credit risk assessment device, characterized in that: The device comprises: A region determination module, used to determine the target agricultural production region corresponding to the target agricultural user; An index calculation module, used to collect original remote sensing spectral data corresponding to each growth stage of the target agricultural production area, and calculate the current growth index of the crop corresponding to each growth stage based on the original remote sensing spectral data; A data calculation module, used for obtaining growth index data based on the current growth index of each crop and the crop historical health growth index corresponding to the current growth index of each crop; The result evaluation module is used to collect the geographical indicator data corresponding to the target agricultural production area, and obtain the target risk evaluation result based on the growth indicator data, the geographical indicator data and a preset risk evaluation model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images