Post-loan risk analysis method and analysis device for agricultural loans

Abstract

The present application provides a post-loan risk analysis method and analysis device for agricultural loans. In the technical solution provided by the present application, the operation status code information and location information of the target agricultural machinery equipment concerned with the post-loan risk of agricultural loans are obtained through an Internet of Things data collection device, or the natural disaster data of the target farm concerned with the post-loan risk of agricultural loans and the management data of the target farmer when managing the target farm are obtained through the Internet of Things data collection device, and then the data obtained through the Internet of Things data collection device are analyzed according to preset rules to obtain the post-loan risk value of the target farmer, thereby improving the accuracy of the post-loan risk assessment.

Description

Technical Field

[0001] The present application relates to the field of risk management and control, and in particular to a post-loan risk analysis method and analysis device for agricultural loans. Background Art

[0002] Agricultural loans refer to the act of banks or other financial institutions taking farmers as loan objects, issuing a certain amount of loans to farmers, and asking farmers to purchase agricultural production materials, use their own labor or agricultural machinery and equipment to carry out agricultural production on farms such as cultivated land and greenhouses, and then repay the loans with funds obtained from the sale of agricultural products. In agricultural loans, farmers' normal loan repayment is the prerequisite for ensuring that the loans of banks or other financial institutions can be recovered.

[0003] At present, banks or other financial institutions generally conduct post-loan risk assessments in order to recover the loans they have issued. Among them, post-loan risk assessment refers to the various measures and methods taken by banks or other financial institutions to reduce the losses caused by risk events. For agricultural loans, post-loan risk assessment refers to the assessment of the operation of the farmer's cultivated land, greenhouses or agricultural machinery and equipment after the bank or other financial institution issues a loan to the farmer to determine the farmer's repayment ability. When it is assessed that the farmer may be at risk of not being able to repay the loan, measures such as mortgaging the farmer's other property can be taken to reduce the possibility of the loan being unable to be recovered.

[0004] However, after banks or other financial institutions issue loans to farmers, they can only conduct post-loan risk assessments through manual inspections (for example, once a month). They are unable to grasp the actual operating conditions of the farmers' cultivated land, greenhouses or agricultural machinery in real time, resulting in low accuracy in post-loan risk assessments. Summary of the invention

[0005] The embodiments of the present application provide a post-loan risk analysis method and analysis device for agricultural loans, which can improve the accuracy of post-loan risk assessment.

[0006] In a first aspect, the present application provides a post-loan risk analysis method for agricultural loans, which is applied to a post-loan risk control system. The method comprises: obtaining operation status code information of a target agricultural machinery equipment collected by an on-board diagnostic system OBD interface in a post-loan risk control system in each of M time periods, wherein the operation status code information indicates whether a fault occurs to the target agricultural machinery equipment; obtaining location information of the target agricultural machinery equipment collected by the OBD interface in each of the M time periods; processing the location information using a first preset rule to obtain location alarm information of the target agricultural machinery equipment, wherein the location alarm information is used to indicate whether the target agricultural machinery equipment is located within a preset geographical range, and the first preset A rule is set to indicate the mapping relationship between the location information of the agricultural machinery equipment collected by the OBD interface and the location alarm information; a second preset rule is used to determine the first post-loan risk value of the target farmer using the target agricultural machinery equipment based on the M operation status code information corresponding to the target agricultural machinery equipment in M ​​time periods and the M target agricultural machinery equipment location alarm information corresponding to the target agricultural machinery equipment in M ​​time periods, and the second preset rule is used to indicate the mapping relationship between the operation status code information of the agricultural machinery equipment in at least one time period, the location alarm information in at least one time period and the post-loan risk value of the farmer using the agricultural machinery equipment; and a first prompt information is output, and the first prompt information is used to indicate the first post-loan risk value.

[0007] It can be understood that since the operating status code information of the target agricultural machinery equipment corresponding to the M time periods in this embodiment is obtained through the on-board diagnostics (OBD) interface, and the location alarm information of the target agricultural machinery equipment is also obtained based on the location information of the target agricultural machinery equipment collected by the OBD interface, the real-time and accuracy of the data of the target agricultural machinery equipment can be guaranteed first.

[0008] In addition, since the post-loan risk control system provided by the present embodiment determines the first post-loan risk value of the target farmer using the target agricultural machinery equipment based on two factors, namely the operating status code information of the target agricultural machinery equipment obtained through the OBD interface and the location alarm information of the target agricultural machinery equipment, the determined first post-loan risk value is more accurate than determining the first post-loan risk value of the target farmer using the target agricultural machinery equipment by considering only a single factor, for example, determining the first post-loan risk value of the target farmer using the target agricultural machinery equipment by considering only the operating status code information of the target agricultural machinery equipment, or determining the first post-loan risk value of the target farmer using the target agricultural machinery equipment by considering only the location alarm information of the target agricultural machinery equipment.

[0009] In combination with the first aspect, in a possible implementation, the method also includes: if the OBD interface does not collect the operating status code information of the target agricultural machinery equipment in N time periods out of M time periods, or the N position information of the target agricultural machinery equipment collected in N time periods are equal, it is determined that the target farmer has engaged in loan fraud; and a second indication information is output, the second indication information being used to indicate that the target farmer has engaged in loan fraud.

[0010] In this embodiment, the post-loan risk control system can also determine whether the target farmer has committed loan fraud based on whether the operating status code information of the target agricultural machinery equipment is collected in N time periods, or N location information of the target agricultural machinery equipment collected in N time periods.

[0011] In combination with the first aspect, in a possible implementation, the method also includes: obtaining natural disaster data within the target farm of the target farmer collected by the sensors in the post-loan risk control system in each of M time periods, the natural disaster data including at least any one of the following: wind speed data of the target farm, earthquake data of the target farm, rainfall data of the target farm, and insect pest data of the target farm; obtaining management data of the target farmer when managing the target farm collected by the sensors in the post-loan risk control system in each of the M time periods, the management data including at least any one of the following: water quality data of the target farm, light data of the target farm, soil data of the target farm, and temperature and humidity data of the target farm; using a third preset rule to process the natural disaster data to obtain natural disaster situation information of the target farm, the natural disaster situation information indicating whether there is a natural disaster on the target farm. , the third preset rule is used to indicate the mapping relationship between the natural disaster data collected by the sensor and the natural disaster situation information; the fourth preset rule is used to process the management data to obtain the management situation information of the target farmer, and the management situation information indicates whether the target farmer manages the target farm. The fourth preset rule is used to indicate the mapping relationship between the management data collected by the sensor and the management situation information; the fifth preset rule is used to determine the second post-loan risk value of the target farmer based on the M natural disaster situation information corresponding to the M time periods and the management situation information of the M target farmers corresponding to the M time periods. The fifth preset rule is used to indicate the mapping relationship between the natural disaster situation information of the farm in at least one time period, the management situation information in at least one time period and the post-loan risk value of the farmers using the farm; the third prompt information is output, and the third prompt information is used to indicate the second post-loan risk value.

[0012] It can be understood that, since the natural disaster situation information corresponding to the M time periods and the management situation information of the target farmers in this embodiment are obtained through the Internet of Things data collection equipment, the real-time and accuracy of the acquired data can be guaranteed first.

[0013] In addition, since the post-loan risk control system provided by the present embodiment determines the second post-loan risk value of the target farmer based on two factors: natural disaster situation information and management situation information of the target farmer, the determined second post-loan risk value is more accurate than determining the second post-loan risk value of the target farmer using the target agricultural machinery and equipment by considering only a single factor, such as determining the second post-loan risk value of the target farmer by considering only natural disaster situation information, or determining the second post-loan risk value of the target farmer by considering only management situation information of the target farmer.

[0014] In combination with the first aspect, in a possible implementation, the method further includes: if the management situation information of the sensor in N time periods out of M time periods indicates that the target farmer has not managed the target farm, determining that the target farmer has engaged in loan fraud; and outputting the second indication information.

[0015] In this implementation, the post-loan risk control system can also determine whether the target farmer has engaged in loan fraud based on the management information in N time periods.

[0016] In a second aspect, the present application provides a post-loan risk analysis device for agricultural loans, which is applied to a post-loan risk control system, and the device includes: an acquisition module, which is used to acquire the operating status code information of the target agricultural machinery equipment collected by the on-board diagnostic system OBD interface in the post-loan risk control system in each of M time periods, and the operating status code information indicates whether the target agricultural machinery equipment has a fault; the acquisition module is also used to acquire the location information of the target agricultural machinery equipment collected by the OBD interface in each of the M time periods; a processing module is used to process the location information using a first preset rule to obtain the location alarm information of the target agricultural machinery equipment, and the location alarm information is used to indicate whether the target agricultural machinery equipment is located within a preset geographical range, and the first preset A rule is set to indicate the mapping relationship between the location information of the agricultural machinery equipment collected by the OBD interface and the location alarm information; the processing module is also used to use a second preset rule to determine the first post-loan risk value of the target farmer using the target agricultural machinery equipment based on the M operating status code information corresponding to the target agricultural machinery equipment in M ​​time periods, and the M target agricultural machinery equipment location alarm information corresponding to the target agricultural machinery equipment in M ​​time periods, the second preset rule is used to indicate the mapping relationship between the operating status code information of the agricultural machinery equipment in at least one time period, the location alarm information in at least one time period and the post-loan risk value of the farmer using the agricultural machinery equipment; the output module is used to output a first prompt information, and the first prompt information is used to indicate the first post-loan risk value.

[0017] In combination with the second aspect, in a possible implementation method, the processing module is also used to: if the OBD interface does not collect the operating status code information of the target agricultural machinery equipment in N time periods out of M time periods, or the N position information of the target agricultural machinery equipment collected in N time periods are equal, it is determined that the target farmer has committed loan fraud; the output module is also used to: output second indication information, and the second indication information is used to indicate that the target farmer has committed loan fraud.

[0018] In combination with the second aspect, in a possible implementation, the acquisition module is also used to: acquire natural disaster data in a target farm of a target farmer collected by sensors in a post-loan risk control system in each of M time periods, the natural disaster data including at least any one of the following: wind speed data of the target farm, earthquake data of the target farm, rainfall data of the target farm, and insect pest data of the target farm; the acquisition module is also used to acquire management data of the target farmer when managing the target farm collected by sensors in a post-loan risk control system in each of M time periods, the management data including at least any one of the following: water quality data of the target farm, light data of the target farm, soil data of the target farm, and temperature and humidity data of the target farm; the processing module is also used to process the natural disaster data using a third preset rule to obtain natural disaster situation information of the target farm, the natural disaster situation information indicating whether there is a natural disaster in the target farm, and the The third preset rule is used to indicate the mapping relationship between the natural disaster data collected by the sensor and the natural disaster situation information; the processing module is also used to use the fourth preset rule to process the management data to obtain the management situation information of the target farmer, the management situation information indicates whether the target farmer manages the target farm, and the fourth preset rule is used to indicate the mapping relationship between the management data collected by the sensor and the management situation information; the processing module is also used to use the fifth preset rule based on the M natural disaster situation information corresponding to the M time periods and the management situation information of the M target farmers corresponding to the M time periods to determine the second post-loan risk value of the target farmer, and the fifth preset rule is used to indicate the mapping relationship between the natural disaster situation information of the farm in at least one time period, the management situation information in at least one time period and the post-loan risk value of the farmers using the farm; the output module is also used to: output a third prompt information, and the third prompt information is used to indicate the second post-loan risk value.

[0019] In combination with the second aspect, in a possible implementation, the processing module is also used for: if the management situation information of the sensor in the N time periods out of the M time periods all indicates that the target farmer has not managed the target farm, determining that the target farmer has engaged in loan fraud; the output module is also used for: outputting the second indication information.

[0020] In a third aspect, the present application provides a post-loan risk analysis device for agricultural loans, comprising: a processor, the processor is coupled to a memory, the memory is used to store a computer program, when the processor calls the computer program, the device executes the method described in the first aspect and any possible implementation method thereof.

[0021] In a fourth aspect, the present application provides a post-loan risk control system, which includes the device in the second aspect or the third aspect or any possible implementation thereof.

[0022] In a fifth aspect, the present application provides a chip comprising at least one processor and a communication interface, wherein the communication interface and the at least one processor are interconnected via a line, and the at least one processor is used to run a computer program or instruction to execute the method described in the first aspect and any possible implementation thereof.

[0023] In a sixth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable medium stores a program code for computer execution, wherein the program code includes a method for executing the method described in the first aspect and any possible implementation thereof.

[0024] In a seventh aspect, the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method described in the first aspect or any possible implementation thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the structure of the post-loan risk control system provided for this application;

[0026] Figure 2 A schematic diagram of the process of a post-loan risk analysis method for agricultural loans provided in one embodiment of the present application;

[0027] Figure 3 A schematic diagram of a process for analyzing the post-loan risk of agricultural loans provided in another embodiment of the present application;

[0028] Figure 4 A schematic diagram of the structure of a post-loan risk analysis device for agricultural loans provided in one embodiment of the present application;

[0029] Figure 5 A schematic structural diagram of a post-loan risk analysis device for agricultural loans provided in another embodiment of the present application. DETAILED DESCRIPTION

[0030] Agricultural loans refer to the act of banks or other financial institutions taking farmers as loan objects, issuing a certain amount of loans to farmers, and asking farmers to purchase agricultural production materials, use their own labor or agricultural machinery and equipment to carry out agricultural production on farms such as cultivated land and greenhouses, and then repay the loans with funds obtained from the sale of agricultural products. In agricultural loans, farmers' normal loan repayment is the prerequisite for ensuring that the loans of banks or other financial institutions can be recovered.

[0031] At present, banks or other financial institutions generally conduct post-loan risk assessments in order to recover the loans they have issued. Among them, post-loan risk assessment refers to the various measures and methods taken by banks or other financial institutions to reduce the losses caused by risk events. For agricultural loans, post-loan risk assessment refers to the assessment of the operation of the farmer's cultivated land, greenhouses or agricultural machinery and equipment after the bank or other financial institution issues a loan to the farmer to determine the farmer's repayment ability. When it is assessed that the farmer may be at risk of not being able to repay the loan, measures such as mortgaging the farmer's other property can be taken to reduce the possibility of the loan being unable to be recovered.

[0032] However, after banks or other financial institutions issue loans to farmers, they can only conduct post-loan risk assessments through manual inspections (for example, once a month). They are unable to accurately grasp the actual operating conditions of the farmers' cultivated land, greenhouses or agricultural machinery in real time, resulting in low accuracy in post-loan risk assessments.

[0033] In view of this, the embodiment of the present application provides a post-loan risk analysis method for agricultural loans. In the analysis method provided by the present application, the Internet of Things technology is introduced, and the real-time data of natural disasters, pests, temperature and humidity, rainfall, wind, light, soil, water quality, working status of agricultural machinery, location of agricultural machinery equipment, etc., which are concerned with the post-loan risks of agricultural loans, are obtained through the Internet of Things data collection equipment, and then the data collected by the sensor are analyzed according to the preset rules to obtain the post-loan risk value of the target farmer, thereby providing a judgment basis for banks or other financial institutions to judge the post-loan risk and loan fraud risk.

[0034] For example, Figure 1 This is a structural diagram of a post-loan risk control system provided by an embodiment of the present application. Figure 1 As shown, the system 100 includes a data collection layer 101 , a network layer 102 , an algorithm layer 103 and an application layer 104 .

[0035] The data collection layer 101 collects the original data of the target farm or agricultural machinery and other collection objects through the IoT data collection equipment. The target farm can be open-air cultivated land or greenhouse planting. The IoT data collection equipment can include, for example, wind speed sensors, earthquake sensors, light sensors, soil moisture monitors, on-board diagnostics (OBD), etc.

[0036] The network layer 102 is used to send the raw data collected by the data collection layer 101 to the algorithm layer 103. Exemplarily, the network that can be used by the network layer is the 2nd generation mobile communication technology (2G) network, the 3rd generation mobile communication technology (3G) network, the 4th generation mobile communication technology (4G) network, the 5th generation mobile communication technology (5G) network, or a satellite positioning network and a fixed network, etc., which is not limited in the embodiments of the present application.

[0037] The algorithm layer 103 is used to analyze the raw data sent by the data collection layer 101 and upload the analysis results to the application layer 104. Specifically, the algorithm layer can use corresponding preset rules to analyze the raw data of different collection objects respectively. For example, the raw data of open-air cultivated land collected by the sensor is analyzed by the preset algorithm corresponding to open-air cultivated land, the raw data of greenhouse planting collected by the sensor is analyzed by the preset algorithm corresponding to greenhouse planting, and the raw data of agricultural machinery and equipment collected by the sensor is analyzed by the preset algorithm corresponding to agricultural machinery and equipment, and then the respective analysis results are sent to the application layer 104.

[0038] The algorithm layer 103 is used to analyze the analysis results sent by the data collection layer 101 to obtain the post-loan risk score of the target farmer, or whether the target farmer has committed loan fraud.

[0039] Next, combine Figure 2 , and the post-loan risk analysis method for agricultural loans provided by the embodiment of the present application is described in detail. Figure 2 As shown, the method of this embodiment may include S201, S202, S203, S204 and S205. The method of this embodiment may be Figure 1 The post-loan risk control system shown is implemented.

[0040] S201, obtaining the operation status code information of the target agricultural machinery equipment collected by the on-board diagnostic system OBD interface in the post-loan risk control system in each of M time periods, wherein the operation status code information indicates whether the target agricultural machinery equipment has a fault, and M is a positive integer greater than 1.

[0041] The target agricultural machinery and equipment can be considered as mechanical tools used by target farmers in agricultural activities. For example, the target agricultural equipment is a rice transplanter, a rotary tiller or a harvester, etc., which is not limited in the present embodiment of the application.

[0042] In this embodiment, each time period can be considered as a time cycle, for example, each time period can be one day, then M time periods are M days, for example, the operating status code information of the target agricultural machinery equipment collected by the OBD interface every day for 30 consecutive days is obtained.

[0043] In this embodiment, the operation status code is used to indicate whether the target agricultural machinery equipment has a fault. Under normal circumstances, when the agricultural machinery equipment is working normally, the operation status code information of the target agricultural machinery equipment is 1, and when the agricultural machinery equipment has a fault, the operation status code information of the target agricultural machinery equipment is 0. That is, when the operation status code information collected by the OBD interface is 1, it indicates that the target agricultural machinery equipment has no fault, and when the operation status code information collected by the OBD interface is 0, it indicates that the target agricultural machinery equipment has a fault.

[0044] It is understandable that if agricultural machinery fails, farmers will be unable to use the agricultural machinery for operations. In some cases, the inability of farmers to use agricultural machinery for operations directly affects the output value of agricultural products. Therefore, in this embodiment, whether the target agricultural machinery fails is considered as a factor in risk assessment.

[0045] S202, obtaining the location information of the target agricultural machinery collected by the OBD interface in each of the M time periods.

[0046] Exemplarily, the location information of the target agricultural machinery equipment collected by the OBD interface in each time period is the latitude and longitude information of the target agricultural machinery equipment.

[0047] It is understandable that the location information of the target agricultural machinery and equipment can reflect the operation of the farmer's farm. For example, if the location of the agricultural machinery and equipment remains consistent for 30 consecutive days, it may indicate that the farmer did not actually farm after the bank or other financial institution issued the loan to the farmer. Furthermore, since the farmer did not actually farm, it may affect the farmer's income from the farm, and there may be a risk that the farmer cannot repay the loan. Therefore, in this embodiment, the location information of the target agricultural machinery and equipment is also taken into account as a factor in risk assessment.

[0048] S203, use the first preset rule to process the location information to obtain the location alarm information of the target agricultural machinery equipment, the location alarm information is used to indicate whether the target agricultural machinery equipment is located within a preset geographical range, and the first preset rule is used to indicate the mapping relationship between the location information of the agricultural machinery equipment collected by the OBD interface and the location alarm information.

[0049] Among them, the preset geographical scope refers to the operating area agreed upon by farmers and banks or other financial institutions. For example, farmers and banks or other financial institutions agree that the operating area of ​​agricultural machinery equipment is centered on the farmer’s home address and is 5,000 meters outward.

[0050] In this embodiment, the first preset rule is used to indicate the mapping relationship between the location information of the agricultural machinery equipment collected by the OBD interface and the location alarm information, and the location alarm information is used to indicate whether the location information of the agricultural machinery equipment is located within a pre-set geographical range.

[0051] It is understandable that if the location of the target agricultural machinery and equipment does not change within the preset geographical range, it means that the target agricultural machinery and equipment may deviate from the agreed operating area, or the target agricultural machinery and equipment may be used unreasonably by farmers or stolen by others, which may also affect the farmers' income through the farm. Therefore, in this embodiment, after obtaining the location information of the target agricultural machinery and equipment, the location information is further analyzed to analyze whether the target agricultural machinery and equipment is located within the preset geographical range.

[0052] Exemplarily, assuming that the operating area of ​​the target agricultural machinery is represented by Y1, and the location information of the target agricultural machinery collected by the OBD interface is represented by Y, the first preset rule can be expressed as: when Y is not included in Y1, the agricultural machinery location alarm information is set to 1, indicating that the target agricultural machinery is not located in the preset geographical range; on the contrary, when Y is included in Y1, the agricultural machinery location alarm information is set to 0, indicating that the target agricultural machinery is located in the preset geographical range.

[0053] S204, using a second preset rule to determine a first post-loan risk value of a target farmer using the target agricultural machinery equipment based on M operating status code information corresponding to the target agricultural machinery equipment in M ​​time periods, and M location alarm information of the target agricultural machinery equipment corresponding to the target agricultural machinery equipment in M ​​time periods, wherein the second preset rule is used to indicate a mapping relationship between the operating status code information of the agricultural machinery equipment in at least one time period, the location alarm information in at least one time period, and the post-loan risk value of the farmer using the agricultural machinery equipment.

[0054] In this embodiment, the second preset rule is used to indicate the mapping relationship between the operating status code information of the agricultural machinery equipment in at least one time period, the location alarm information in at least one time period and the post-loan risk value of the farmer using the agricultural machinery equipment.

[0055] Exemplarily: assuming that the first post-loan risk value is represented by R3, the operating status code information of the target agricultural machinery equipment is represented by X, and the position alarm information is represented by U, the second preset rule can be represented by the rule R3=if(X=1,0,3)+if(U=1,1,0), wherein if(X=1,0,3) means that when the value of X is 1, if(X=1,0,3) takes a value of 0, and when the value of X is not 1, if(X=1,0,3) takes a value of 3, and if(U=1,1,0) means that when the value of U is 1, if(U=1,1,0) takes a value of 1, and when the value of U is not 1, if(U=1,1,0) takes a value of 0.

[0056] Therefore, in this embodiment, after obtaining the operating status code information and location alarm information of the target agricultural machinery equipment, the first post-loan risk value of the target farmer using the target agricultural machinery equipment can be determined by the first rule.

[0057] More specifically, when determining the first post-loan risk value of the target farmer using the target agricultural machinery, this embodiment is based on M operation status code information and M target agricultural machinery location alarm information, and the M operation status code information and the M target agricultural machinery are in one-to-one correspondence with the M time periods. That is, in this embodiment, the information of the M time periods is used to determine the first post-loan risk value of the target farmer.

[0058] It can be understood that, compared with using information from a single time period, using information from M time periods can more comprehensively reflect the use of agricultural machinery and equipment by target farmers. Therefore, the first post-loan risk value of the target farmers determined by using information from M time periods is more accurate.

[0059] S205, outputting first prompt information, where the first prompt information is used to indicate a first post-loan risk value.

[0060] In this embodiment, after the post-loan risk control system determines the first post-loan risk value of the target farmer using the target agricultural machinery and equipment, it can also output prompt information (i.e., first prompt information) to indicate the first post-loan risk value of the target farmer using the target agricultural machinery and equipment to the bank or other financial institution, so that the bank or other financial institution can take different measures based on the first post-loan risk value.

[0061] It can be understood that since the operating status code information of the target agricultural machinery equipment corresponding to the M time periods in this embodiment is obtained through the OBD interface, and the location alarm information of the target agricultural machinery equipment is also obtained based on the location information of the target agricultural machinery equipment collected by the OBD interface, the real-time and accuracy of the acquired target agricultural machinery equipment can be guaranteed first.

[0062] In addition, since the post-loan risk control system provided by the present embodiment determines the first post-loan risk value of the target farmer using the target agricultural machinery equipment based on two factors, namely the operating status code information of the target agricultural machinery equipment obtained through the OBD interface and the location alarm information of the target agricultural machinery equipment, the determined first post-loan risk value is more accurate than determining the first post-loan risk value of the target farmer using the target agricultural machinery equipment by considering only a single factor, for example, determining the first post-loan risk value of the target farmer using the target agricultural machinery equipment by considering only the operating status code information of the target agricultural machinery equipment, or determining the first post-loan risk value of the target farmer using the target agricultural machinery equipment by considering only the location alarm information of the target agricultural machinery equipment.

[0063] As an optional embodiment, in Figure 2 On the basis of the shown embodiment, if the OBD interface does not collect the operating status code information of the target agricultural machinery equipment in N time periods out of M time periods, or the N position information of the target agricultural machinery equipment collected in N time periods are equal, it is determined that the target farmer has committed loan fraud, and N is less than or equal to M; the second indication information is output, and the second indication information is used to indicate that the target farmer has committed loan fraud.

[0064] It should be noted that this embodiment does not limit the specific form of the N time periods. For example, the N time periods may be N consecutive time periods or N discontinuous time periods.

[0065] For example, taking M time periods as 40 days and N time periods as 30 days as an example, it may be that the operating status code information of the target agricultural machinery equipment is not collected for 30 consecutive days out of 40 days, or the operating status code information of the target agricultural machinery equipment is not collected for any 30 days out of 40 days.

[0066] It can be understood that if the OBD interface does not collect the operating status code information of the target agricultural machinery equipment in N time periods out of M time periods, it means that the target agricultural machinery equipment remains in the shutdown state most of the time, and if the N position information of the target agricultural machinery equipment collected in N time periods are equal, it means that the target agricultural machinery equipment is placed in the same position most of the time, that is, in most cases the target agricultural machinery equipment is not used for farming. At this time, it can be considered that the target farmers using the target agricultural machinery equipment may be engaged in loan fraud.

[0067] Furthermore, in this embodiment, when the post-loan risk control system determines that the target farmer may have committed loan fraud, it can also output prompt information (second prompt information) to indicate to the bank or other financial institution that the target farmer using the target agricultural machinery may have committed loan fraud, so that the bank or other financial institution can take corresponding measures.

[0068] As an optional embodiment, the present application can also determine the post-loan risk value corresponding to the target farmer by analyzing the situation of the target farm operated by the target farmer.

[0069] Next, combine Figure 3 , illustrating a post-loan risk analysis method for agricultural loans provided by another embodiment of the present application. Figure 3 As shown, the method of this embodiment may include S301, S302, S303, S304, S305 and S306. The method of this embodiment may also be Figure 1 The post-loan risk control system shown is implemented.

[0070] S301, obtaining natural disaster data in the target farm of the target farmer collected by the sensors in the post-loan risk control system in each of M time periods, the natural disaster data including at least any one of the following: wind speed data of the target farm, earthquake data of the target farm, rainfall data of the target farm, and insect pest data of the target farm.

[0071] It is understandable that the output of agricultural products is greatly affected by natural factors, which are difficult to accurately predict. Therefore, in this embodiment, the wind speed data of the target farm, the earthquake data of the target farm, the rainfall data of the target farm, and the pest data of the target farm are collected by different sensors to reflect the natural disaster situation.

[0072] For example, the wind speed data of the target farm can be obtained through a wind speed sensor, the earthquake data of the target farm can be obtained through a seismic sensor, the rainfall data of the target farm can be obtained through a rainfall monitoring device, and the insect pest data of the target farm can be obtained through an insect monitoring light.

[0073] It should be noted that this embodiment does not limit the specific form of the target farm. For example, it can be greenhouse cultivation, or it can be open-air cultivated land.

[0074] It is noted here that the concept of time period can be referred to Figure 2 The description in the embodiments will not be repeated here.

[0075] S302, obtaining management data collected by sensors in the post-loan risk control system when the target farmer manages the target farm in each of M time periods, wherein the management data includes at least any one of the following: water quality data of the target farm, light data of the target farm, soil data of the target farm, and temperature and humidity data of the target farm.

[0076] It is understandable that farmers' farm management is also an important factor in agricultural product output. Therefore, in this embodiment, different sensors are used to collect water quality data, light data, soil data, and temperature and humidity data of the target farm to reflect the management of the target farm by the target farmers.

[0077] S303, use the third preset rule to process the natural disaster data to obtain the natural disaster situation information of the target farm, wherein the natural disaster situation information indicates whether there is a natural disaster in the target farm, and the third preset rule is used to indicate the mapping relationship between the natural disaster data collected by the sensor and the natural disaster situation information.

[0078] In this embodiment, the third preset rule is used to indicate the mapping relationship between the natural disaster data collected by the sensor and the natural disaster situation information.

[0079] It is understandable that if a natural disaster occurs, it may affect the income of farmers. Therefore, in this embodiment, after obtaining the natural disaster data in the target farm, the natural disaster data is further analyzed to analyze whether there is a natural disaster in the target farm.

[0080] Exemplarily, assuming that the natural disaster situation information is represented by Z, the wind speed is represented by F, the seismic wave is represented by D, the rainfall is represented by J, and the insect density is represented by C, and the wind speed disaster level threshold is set to F', the seismic wave disaster level threshold is set to D', the rainfall disaster level threshold is set to J', and the insect density disaster level threshold is set to C', then the third preset rule can be expressed as: if F is greater than F' or D is greater than D' or J is greater than J' or C is greater than C', then Z=1, indicating that a natural disaster situation exists; otherwise, Z=0, indicating that there is no natural disaster situation.

[0081] S304, use the fourth preset rule to process the management data to obtain the management status information of the target farmer, the management status information indicates whether the target farmer manages the target farm, and the fourth preset rule is used to indicate the mapping relationship between the management data collected by the sensor and the management status information.

[0082] In this embodiment, the fourth preset rule is used to indicate the mapping relationship between the management data collected by the sensor and the management situation information.

[0083] It is understandable that if the target farmer does not manage the target farm or manages it poorly, it may affect the farmer's income. Therefore, in this embodiment, after obtaining the management data within the target farm, the management data is further analyzed to analyze whether the target farmer manages the target farm.

[0084] Exemplarily, assuming that the management situation information is represented by Q, the collected water quality data is represented by S, the light is represented by G, the soil is represented by T, and the temperature and humidity are represented by W, and the normal interval of water quality is set to S', the normal interval of light is G', the normal interval of soil is T', and the normal interval of temperature and humidity is W', then the fourth preset rule can be expressed as: if S is not included in S' or G is not included in G' or T is not included in T' or W is not included in W', then Q=1, indicating that the target farmer does not manage the target farm or manages it poorly; otherwise, Q=0, indicating that the target farmer manages the target farm.

[0085] It should be noted that the target farms have different shapes, and the above-mentioned S, G', T' and W' may be different, so they can be set according to actual conditions, and the embodiments of the present application do not limit this.

[0086] S305, using the fifth preset rule to determine the second post-loan risk value of the target farmer based on the M natural disaster situation information corresponding to the M time periods and the management situation information of the M target farmers corresponding to the M time periods, the fifth preset rule is used to indicate the mapping relationship between the natural disaster situation information of the farm in at least one time period, the management situation information in at least one time period and the post-loan risk value of the farmers using the farm.

[0087] In this embodiment, the fifth preset rule is used to indicate the mapping relationship between the natural disaster situation information of the farm in at least one time period, the management situation information in at least one time period and the post-loan risk value of the farmers using the farm.

[0088] Exemplarily: assuming that the second post-loan risk value is represented by R1, the natural disaster situation information of the target farm is represented by Z, and the management situation information is represented by Q, the fifth preset rule can be represented by the rule R1=if(Z=1,3,0)+if(Q=1,2,0), wherein if(Z=1,3,0) means that when the value of Z is 1, if(Z=1,3,0) takes a value of 3, and when the value of Z is not 1, if(Z=1,3,0) takes a value of 0; if(Q=1,2,0) means that when the value of Q is 1, if(Q=1,2,0) takes a value of 2, and when the value of Q is not 1, if(Q=1,2,0) takes a value of 0.

[0089] Therefore, in this embodiment, after obtaining the natural disaster situation information and the management situation information of the target farmer, the second post-loan risk value of the target farmer can be determined by the fifth rule.

[0090] S306, outputting third prompt information, where the third prompt information is used to indicate the second post-loan risk value.

[0091] In this embodiment, after the post-loan risk control system determines the second post-loan risk value, it can also output prompt information (i.e., third prompt information) to indicate the second post-loan risk value of the target farmer using the target agricultural machinery to the bank or other financial institution, so that the bank or other financial institution can take different measures based on the first post-loan risk value.

[0092] It can be understood that, since the natural disaster situation information corresponding to the M time periods and the management situation information of the target farmers in this embodiment are obtained through the Internet of Things data collection equipment, the real-time and accuracy of the acquired data can be guaranteed first.

[0093] In addition, since the post-loan risk control system provided by the present embodiment determines the second post-loan risk value of the target farmer based on two factors: natural disaster situation information and management situation information of the target farmer, the determined second post-loan risk value is more accurate than determining the second post-loan risk value of the target farmer using the target agricultural machinery and equipment by considering only a single factor, such as determining the second post-loan risk value of the target farmer by considering only natural disaster situation information, or determining the second post-loan risk value of the target farmer by considering only management situation information of the target farmer.

[0094] As an optional embodiment, the method further includes: if the management situation information of the sensor in N time periods out of M time periods all indicates that the target farmer has not managed the target farm, it is determined that the target farmer has engaged in loan fraud; and outputting a second indication information.

[0095] For a detailed description of N time periods among the M time periods, reference may be made to the description in the above embodiment, which will not be repeated here.

[0096] Figure 4 A schematic diagram of the structure of a post-loan risk analysis device for agricultural loans provided by one embodiment of the present application. Figure 4 As shown, the device of this embodiment is applied to a post-loan risk control system, and the device 400 includes: an acquisition module 401 , a processing module 402 and an output module 403 .

[0097] The acquisition module 401 is used to acquire the operation status code information of the target agricultural machinery equipment collected by the on-board diagnostic system OBD interface in the post-loan risk control system in each of the M time periods, and the operation status code information indicates whether the target agricultural machinery equipment has a fault; the acquisition module 401 is also used to acquire the position information of the target agricultural machinery equipment collected by the OBD interface in each of the M time periods; the processing module 402 is used to process the position information using the first preset rule to obtain the position alarm information of the target agricultural machinery equipment, and the position alarm information is used to indicate whether the target agricultural machinery equipment is located within a preset geographical range, and the first preset rule is used to indicate the position alarm information of the agricultural machinery equipment collected by the OBD interface. The processing module 402 is also used to determine the first post-loan risk value of the target farmer using the target agricultural machinery equipment based on the M operating status code information corresponding to the target agricultural machinery equipment in M ​​time periods and the M target agricultural machinery equipment position alarm information corresponding to the target agricultural machinery equipment in M ​​time periods using a second preset rule, wherein the second preset rule is used to indicate the mapping relationship between the operating status code information of the agricultural machinery equipment in at least one time period, the position alarm information in at least one time period and the post-loan risk value of the farmer using the agricultural machinery equipment; the output module 403 is used to output the first prompt information, wherein the first prompt information is used to indicate the first post-loan risk value.

[0098] As an example, the output module 403 can be used to perform Figure 2 The step of outputting the first prompt information in the method. For example, the output module 403 is used to execute S205.

[0099] In a possible implementation, the processing module 402 is also used to: if the OBD interface does not collect the operating status code information of the target agricultural machinery equipment in N time periods out of M time periods, or the N position information of the target agricultural machinery equipment collected in N time periods are equal, it is determined that the target farmer has committed loan fraud; the output module 403 is also used to: output a second indication information, wherein the second indication information is used to indicate that the target farmer has committed loan fraud.

[0100] In a possible implementation, the acquisition module 401 is also used to: acquire natural disaster data in a target farm of a target farmer collected by sensors in a post-loan risk control system in each of M time periods, the natural disaster data including at least any one of the following: wind speed data of the target farm, earthquake data of the target farm, rainfall data of the target farm, and insect pest data of the target farm; the acquisition module 401 is also used to acquire management data of the target farmer when managing the target farm collected by sensors in a post-loan risk control system in each of M time periods, the management data including at least any one of the following: water quality data of the target farm, light data of the target farm, soil data of the target farm, and temperature and humidity data of the target farm; the processing module 402 is also used to process the natural disaster data using a third preset rule to obtain natural disaster situation information of the target farm, the natural disaster situation information indicating whether there is a natural disaster in the target farm, and the third preset rule The rule is used to indicate the mapping relationship between the natural disaster data collected by the sensor and the natural disaster situation information; the processing module 402 is also used to use the fourth preset rule to process the management data to obtain the management situation information of the target farmer, the management situation information indicates whether the target farmer manages the target farm, and the fourth preset rule is used to indicate the mapping relationship between the management data collected by the sensor and the management situation information; the processing module 402 is also used to use the fifth preset rule based on the M natural disaster situation information corresponding to the M time periods and the management situation information of the M target farmers corresponding to the M time periods to determine the second post-loan risk value of the target farmer, and the fifth preset rule is used to indicate the mapping relationship between the natural disaster situation information of the farm in at least one time period, the management situation information in at least one time period and the post-loan risk value of the farmers using the farm; the output module 403 is also used to: output a third prompt information, and the third prompt information is used to indicate the second post-loan risk value.

[0101] In a possible implementation, the processing module 402 is also used for: if the management status information of the sensor in the N time periods out of the M time periods all indicates that the target farmer has not managed the target farm, determining that the target farmer has engaged in loan fraud; the output module 403 is also used for: outputting the second indication information.

[0102] Figure 5 A structural schematic diagram of a post-loan risk analysis device for agricultural loans provided in another embodiment of the present application. Figure 5 The device shown can be used to execute the method described in any of the above embodiments.

[0103] like Figure 5As shown, the device 500 of this embodiment includes: a memory 501, a processor 502, a communication interface 503 and a bus 504. The memory 501, the processor 502 and the communication interface 503 are connected to each other through the bus 504.

[0104] The memory 501 may be a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM). The memory 501 may store a program. When the program stored in the memory 501 is executed by the processor 502, the processor 502 is used to execute the program. Figure 2 or Figure 3 The individual steps of the method are shown.

[0105] The processor 502 may be a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits for executing relevant programs to implement the present application. Figure 2 or Figure 3 The method shown.

[0106] The processor 502 may also be an integrated circuit chip having the ability to process signals. Figure 2 or Figure 3 Each step of the method can be completed by an integrated logic circuit of hardware in the processor 502 or an instruction in the form of software.

[0107] The processor 502 may also be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The methods, steps, and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0108] The steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 501, and the processor 502 reads the information in the memory 501, and combines its hardware to complete the functions required to be executed by the units included in the device of the present application, for example, it can execute Figure 2 or Figure 3 The various steps / functions of the illustrated embodiment.

[0109] The communication interface 503 may use, but is not limited to, a transceiver or other transceiver device to implement communication between the apparatus 500 and other devices or a communication network.

[0110] The bus 504 may include a path for transmitting information between various components of the device 500 (eg, the memory 501 , the processor 502 , and the communication interface 503 ).

[0111] It should be understood that the device 500 shown in the embodiment of the present application may be an electronic device, or may also be a chip configured in the electronic device.

[0112] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0113] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0114] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0115] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 application.

[0116] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0118] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0121] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disk.

[0122] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A post-loan risk analysis method for agricultural loans, characterized in that: Applied to a post-loan risk control system, the method comprises: Obtaining the operation status code information of the target agricultural machinery equipment collected by the on-board diagnostic system OBD interface in the post-loan risk control system in each of M time periods, wherein the operation status code information indicates whether the target agricultural machinery equipment has a fault, and M is a positive integer greater than 1; Acquire the location information of the target agricultural machinery equipment collected by the OBD interface in each of the M time periods; Processing the location information using a first preset rule to obtain location alarm information of the target agricultural machinery equipment, wherein the location alarm information is used to indicate whether the target agricultural machinery equipment is located within a preset geographical range, and the first preset rule is used to indicate a mapping relationship between the location information of the agricultural machinery equipment collected by the OBD interface and the location alarm information; Determine a first post-loan risk value of a target farmer using the target agricultural machinery equipment based on the M operation status code information corresponding to the target agricultural machinery equipment in the M time periods and the M position alarm information of the target agricultural machinery equipment corresponding to the target agricultural machinery equipment in the M time periods using a second preset rule, wherein the second preset rule is used to indicate a mapping relationship between the operation status code information of the agricultural machinery equipment in at least one time period, the position alarm information in at least one time period and the post-loan risk value of the farmer using the agricultural machinery equipment; Outputting first prompt information, where the first prompt information is used to indicate the first post-loan risk value; The method further comprises: Acquire the natural disaster data in the target farm of the target farmer collected by the sensor in the post-loan risk control system in each of the M time periods; Acquire management data of the target farmer when managing the target farm collected by the sensor in the post-loan risk control system in each of the M time periods; The natural disaster data is processed using a third preset rule to obtain natural disaster situation information of the target farm, wherein the natural disaster situation information indicates whether a natural disaster exists in the target farm, and the third preset rule is used to indicate a mapping relationship between the natural disaster data collected by the sensor and the natural disaster situation information; Processing the management data using a fourth preset rule to obtain management situation information of the target farmer, wherein the management situation information indicates whether the target farmer manages the target farm, and the fourth preset rule is used to indicate a mapping relationship between the management data collected by the sensor and the management situation information; Determine the second post-loan risk value of the target farmer based on the M natural disaster situation information corresponding to the M time periods and the management situation information of the M target farmers corresponding to the M time periods using a fifth preset rule, wherein the fifth preset rule is used to indicate a mapping relationship between the natural disaster situation information of the farm in at least one time period, the management situation information in at least one time period, and the post-loan risk value of the farmer using the farm; Output third prompt information, where the third prompt information is used to indicate the second post-loan risk value.

2. The method according to claim 1, characterized in that: The method further comprises: If the OBD interface does not collect the operation status code information of the target agricultural machinery equipment in N time periods among the M time periods, or the N position information of the target agricultural machinery equipment collected in the N time periods are equal, it is determined that the target farmer has committed loan fraud, and N is less than or equal to M; Output second indication information, where the second indication information is used to indicate that the target farmer has engaged in loan fraud.

3. The method according to claim 1, characterized in that The natural disaster data includes at least any one of the following: wind speed data of the target farm, earthquake data of the target farm, rainfall data of the target farm, and insect pest data of the target farm; The management data includes at least any one of the following: water quality data of the target farm, light data of the target farm, soil data of the target farm, and temperature and humidity data of the target farm.

4. The method according to claim 1, characterized in that: The method further comprises: If the management information of the sensor in N time periods out of the M time periods all indicates that the target farmer has not managed the target farm, it is determined that the target farmer has engaged in loan fraud; Output second indication information.

5. A post-loan risk analysis device for agricultural loans, characterized in that: Applied to a post-loan risk control system, the device comprises: An acquisition module is used to acquire the operation status code information of the target agricultural machinery equipment collected by the on-board diagnostic system OBD interface in the post-loan risk control system in each of the M time periods, wherein the operation status code information indicates whether the target agricultural machinery equipment has a fault; The acquisition module is also used to acquire the location information of the target agricultural machinery equipment collected by the OBD interface in each of the M time periods; a processing module, configured to process the location information using a first preset rule to obtain location alarm information of the target agricultural machinery equipment, wherein the location alarm information is used to indicate whether the target agricultural machinery equipment is located within a preset geographical range, and the first preset rule is used to indicate a mapping relationship between the location information of the agricultural machinery equipment collected by the OBD interface and the location alarm information; The processing module is also used to determine a first post-loan risk value of a target farmer using the target agricultural machinery based on the M operation status code information corresponding to the target agricultural machinery in the M time periods and the M position alarm information of the target agricultural machinery corresponding to the target agricultural machinery in the M time periods using a second preset rule, wherein the second preset rule is used to indicate a mapping relationship between the operation status code information of the agricultural machinery in at least one time period, the position alarm information in at least one time period and the post-loan risk value of the farmer using the agricultural machinery; An output module, used for outputting first prompt information, where the first prompt information is used for indicating the first post-loan risk value; The acquisition module is also used to: acquire the natural disaster data in the target farm of the target farmer collected by the sensor in the post-loan risk control system in each of the M time periods; The acquisition module is also used to acquire the management data of the target farmer when managing the target farm, which is collected by the sensor in the post-loan risk control system in each of the M time periods; The processing module is further used to process the natural disaster data using a third preset rule to obtain natural disaster situation information of the target farm, wherein the natural disaster situation information indicates whether a natural disaster exists in the target farm, and the third preset rule is used to indicate a mapping relationship between the natural disaster data collected by the sensor and the natural disaster situation information; The processing module is further used to process the management data using a fourth preset rule to obtain management information of the target farmer, wherein the management information indicates whether the target farmer manages the target farm, and the fourth preset rule is used to indicate a mapping relationship between the management data collected by the sensor and the management information; The processing module is also used to determine the second post-loan risk value of the target farmer based on the M natural disaster situation information corresponding to the M time periods and the management situation information of the M target farmers corresponding to the M time periods using a fifth preset rule, wherein the fifth preset rule is used to indicate a mapping relationship between the natural disaster situation information of the farm in at least one time period, the management situation information in at least one time period and the post-loan risk value of the farmer using the farm; The output module is further used to output third prompt information, where the third prompt information is used to indicate the second post-loan risk value.

6. The device according to claim 5, characterized in that The processing module is also used to: if the OBD interface does not collect the operation status code information of the target agricultural machinery equipment in N time periods among the M time periods, or the N position information of the target agricultural machinery equipment collected in the N time periods are equal, determine that the target farmer has committed loan fraud; The output module is also used to: output second indication information, where the second indication information is used to indicate that the target farmer has engaged in loan fraud.

7. The device according to claim 5, characterized in that The natural disaster data includes at least any one of the following: wind speed data of the target farm, earthquake data of the target farm, rainfall data of the target farm, and insect pest data of the target farm; The management data includes at least any one of the following: water quality data of the target farm, light data of the target farm, soil data of the target farm, and temperature and humidity data of the target farm.

8. The device according to claim 5, characterized in that The processing module is further used to: determine that the target farmer has committed loan fraud if the management situation information of the sensor in N time periods out of the M time periods indicates that the target farmer has not managed the target farm; The output module is further used to: output second indication information.

9. A post-loan risk analysis device for agricultural loans, characterized in that: include: memory, processors, and transceivers; The memory is used to store program instructions; The processor is used to call the program instructions in the memory to execute the method according to any one of claims 1 to 4.

10. A post-loan risk control system, characterized in that: Comprising the device for use in any one of claims 5 to 8 and 9.

11. A chip, characterized in that: The system comprises at least one processor and a communication interface, wherein the communication interface and the at least one processor are interconnected via a line, and the at least one processor is used to run a computer program or instruction to execute the method according to any one of claims 1 to 4.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program code for computer execution, wherein the program code includes instructions for executing the method according to any one of claims 1 to 4.

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