Farming service area planning method and system, storage medium and electronic equipment

Abstract

The invention provides a farming service area planning method and system, a storage medium and electronic equipment, and the method comprises the steps: obtaining real-time cultivated land information and historical cultivated land information, so as to determine a corresponding real-time cultivated land data set and a historical cultivated land data set, determining farming demand data in a preset time period according to the real-time farming data set and the historical farming data set; acquiring information of a plurality of service points, respectively determining an agricultural production resource comprehensive score corresponding to each service point through a preset evaluation algorithm according to the information of the plurality of service points, and mapping the farming demand data and the agricultural production resource comprehensive scores to a grid map to determine corresponding position traffic data; and determining a target corresponding relation data set of the service point and the cultivated land according to the farming demand data, the agricultural production resource comprehensive score and the position traffic data, and performing farming service area planning on the service point and the cultivated land according to the target corresponding relation data set. The problem that in the prior art, farming requirements are not matched with agricultural resources is solved.

Description

Technical Field

[0001] The present invention relates to the field of regional division technology, and in particular to a method, system, storage medium and electronic equipment for planning agricultural service areas. Background Art

[0002] With the advancement of technology, agriculture has entered a stage of mechanized farming. Modern agricultural machinery has formed a complete technical system, including intelligent navigation tractors, precision variable seeding machines, and unmanned plant protection machines. Each region typically has a corresponding service point, which provides agricultural machinery repair and rental services, seedling cultivation and supply, fertilizer and pesticide supply, and post-harvest grain drying, thereby providing corresponding agricultural resources. These services may be provided by one or more service points to meet the mechanized farming needs of the corresponding area.

[0003] However, these service points are typically established based on local agricultural development, with at least one service point located in each region, typically providing agricultural services only to the corresponding village or town. However, as agricultural needs change over time, and unexpected circumstances, such as adverse environmental conditions or natural disasters, can also cause changes in agricultural needs within different regions, leading to some agricultural service points being unable to meet current regional agricultural needs, while others may have an excess of resources. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a farming service area planning method, system, storage medium and electronic equipment, aiming to solve the problem of mismatch between farming needs and agricultural resources in the existing technology.

[0005] A method for planning a farming service area according to an embodiment of the present invention includes: Acquire real-time cultivated land information and historical cultivated land information to respectively determine corresponding real-time cultivated land datasets and historical cultivated land datasets, and determine farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land datasets and the historical cultivated land datasets; Acquire information of a plurality of service points, and determine a comprehensive agricultural production resource score corresponding to each service point using a preset evaluation algorithm based on the information of the plurality of service points, and map the farming demand data and the comprehensive agricultural production resource score to a grid map to determine corresponding location traffic data; According to the farming demand data, the comprehensive score of agricultural production resources and the location traffic data, a target correspondence data set between service points and cultivated land is determined through a preset allocation algorithm, so as to plan the farming service area for service points and cultivated land according to the target correspondence data set.

[0006] In addition, the agricultural service area planning method according to the above embodiment of the present invention may also have the following additional technical features: Furthermore, the historical cultivated land dataset includes at least geographic information, soil information, crop condition information, environmental information, and time node information. The step of determining the farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land dataset and the historical cultivated land dataset includes: The preset prediction model is trained using the historical cultivated land dataset, so that the preset prediction model determines the cultivated land condition at a target time node based on the geographic information, the soil information, the crop condition information, the environmental information, and the time node information; The real-time farmland data set and environmental prediction information are input into the trained preset prediction model to obtain the farmland status information at each time node within the preset time period, so as to determine the farming demand data based on the farmland status information.

[0007] Furthermore, the step of determining the comprehensive score of agricultural production resources corresponding to each service point using a preset evaluation algorithm based on the information of the plurality of service points includes: Determine the supply resource information, agricultural machinery and equipment information and personnel information of each service point respectively; Determine corresponding supply resource scores, agricultural machinery scores, and personnel scores based on corresponding indicator parameters in the supply resource information, agricultural machinery information, and personnel information of each of the service points; The comprehensive agricultural production resource score corresponding to different cultivated land information is determined based on the supply resource score, the agricultural machinery and equipment score, and the personnel score. The comprehensive agricultural production resource score is used to determine the range of agricultural needs that can be met by the service point. The cultivated land information includes information on the types of crops planted on each cultivated land.

[0008] Furthermore, the step of determining a target correspondence relationship dataset between service points and cultivated land using a preset allocation algorithm based on the farming demand data, the comprehensive score of agricultural production resources, and the location and traffic data includes: Determining the farming demand data within each preset divided area based on the location and traffic data, and the difference data between the comprehensive scores of agricultural production resources within each preset divided area, wherein the difference data is obtained by subtracting the farming demand data from the comprehensive score of agricultural production resources; When the difference data is negative, it indicates that the service points in the preset divided area cannot meet the farming needs, and then the preset divided area is adjusted and divided according to the difference data to determine the preliminary divided area, so that the difference data of each preliminary divided area are all positive values, and then the preliminary divided area is adjusted according to the location traffic data to determine the theoretical divided area, so that the standard deviation of the difference data of each theoretical divided area is less than a first preset value; When the difference value is positive, it indicates that the service points in the preset divided area meet the farming needs, and the preset divided area is then adjusted according to the location traffic data to determine the theoretical divided area, so that the standard deviation of the difference data of each theoretical divided area is less than a first preset value; The service points and cultivated land within the theoretically divided area are obtained to determine the target correspondence relationship dataset.

[0009] Furthermore, after the step of adjusting the preliminary divided areas according to the location traffic data to determine the theoretical divided areas, the following steps are included: Dividing the theoretical divided area into units to determine a plurality of target units, wherein the service points are distributed within each target unit so that a standard deviation of the difference data of each target unit is less than a second preset value, and a sum of travel distances between service points and cultivated land within each target unit is minimized, and the second preset value is less than the first preset value; The service points and cultivated land within the target unit are obtained to determine the target correspondence relationship dataset.

[0010] Furthermore, after the step of determining the farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land dataset and the historical cultivated land dataset, the method further includes: determining occasional demand data and long-term demand data based on the farming demand data; Determining an occasional correspondence relationship data set based on the occasional demand data, the agricultural production resource comprehensive score, and the location traffic data; Determining a long-term correspondence dataset between service points and cultivated land based on the long-term demand data, the comprehensive score of agricultural production resources, and the location and traffic data; Determining normal area planning information and occasional area planning information according to the occasional correspondence relationship dataset and the long-term correspondence relationship dataset respectively; The scope of the target area is adjusted according to the normal area planning information and the occasional area planning information according to the type of the real-time demand data of the target area.

[0011] Furthermore, after the step of planning the agricultural service area for the service points and the farmland according to the target correspondence relationship dataset, the step further includes: The farming efficiency information within the preset time area after the area division is collected and evaluated, so as to adjust the preset prediction model, the preset evaluation algorithm and the preset deployment algorithm according to the evaluation results.

[0012] Another object of the present invention is to provide an agricultural service area planning system, the system comprising: a farming demand data determination module, configured to obtain real-time farmland information and historical farmland information to determine corresponding real-time farmland datasets and historical farmland datasets, respectively, and determine farming demand data for a preset time period using a preset prediction model based on the real-time farmland datasets and the historical farmland datasets; an agricultural production resource comprehensive score determination module, configured to obtain information on a plurality of service points and determine the agricultural production resource comprehensive score corresponding to each service point using a preset evaluation algorithm based on the information on the plurality of service points, and map the farming demand data and the agricultural production resource comprehensive score to a grid map to determine corresponding location traffic data; The agricultural service area planning module is used to determine the target correspondence data set between service points and cultivated land through a preset allocation algorithm based on the agricultural demand data, the comprehensive score of agricultural production resources and the location traffic data, so as to plan the agricultural service area for the service points and cultivated land according to the target correspondence data set.

[0013] Another object of an embodiment of the present invention is to provide a storage medium having a computer program stored thereon, which implements the steps of the above-mentioned agricultural service area planning method when executed by a processor.

[0014] Another object of an embodiment of the present invention is to provide an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned agricultural service area planning method when executing the program.

[0015] The present invention trains a model using historical farmland information. This allows the model to determine farmland conditions during a preset time period based on real-time farmland information, specifically the crop and land conditions during that time period. This information can then be used to determine the required agricultural resources, such as equipment, fertilizers, and pesticides. Information from each service point is then collected and evaluated, accurately determining the scope of agricultural resources each service point can provide from multiple dimensions. Furthermore, based on the location of each service point and the farmland, the service area is divided so that the agricultural resources provided by each service point within the preset time period meet the farming needs of each farmland. Because allocation is regulated based on real-time farmland information, and after the initial division, each area is adjusted based on actual conditions to ensure that agricultural resources in each area meet farming needs, dynamic allocation of agricultural resources is achieved, enabling timely response to changes in farming needs, including seasonal variations and fluctuations in demand caused by emergencies. This not only improves the efficiency and quality of agricultural services, but also effectively avoids resource waste and unmet needs. Therefore, the present invention addresses the existing problem of mismatch between farming needs and agricultural resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the agricultural service area planning method in the first embodiment of the present invention; Figure 2 This is a structural block diagram of the agricultural service area planning system in the second embodiment of the present invention; Figure 3 is a schematic structural diagram of an electronic device in a third embodiment of the present invention; The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0017] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly attached to the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 See also Figure 1 , which shows the agricultural service area planning method in the first embodiment of the present invention, and the method specifically includes steps S01 to S03.

[0021] S01, obtaining real-time cultivated land information and historical cultivated land information to respectively determine corresponding real-time cultivated land data sets and historical cultivated land data sets, and determining agricultural demand data for a preset time period through a preset prediction model based on the real-time cultivated land data sets and the historical cultivated land data sets.

[0022] Specifically, the historical cultivated land dataset includes at least geographic information, soil information, crop condition information, environmental information, and time node information. The step of determining the farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land dataset and the historical cultivated land dataset includes: The preset prediction model is trained using the historical farmland dataset, so that the preset prediction model determines the farmland condition at a target time point based on the geographic information, soil information, crop condition information, environmental information, and time point information. The real-time farmland dataset and environmental prediction information are input into the trained preset prediction model to obtain farmland condition information at each time point within the preset time period, thereby determining the farming demand data based on the crop condition information. The preset prediction model is a prediction model trained using big data that can predict the subsequent condition of the farmland based on farmland information to determine the subsequent farmland condition. The preset prediction model is then trained again using historical data on the current farmland, making the preset prediction model's predictions for the current farmland more accurate. Furthermore, the real-time farmland dataset is used to predict farmland condition information for the preset time period, thereby determining farming demand data for each time point within the preset time period, namely, equipment demand, fertilizer demand, sowing demand, pesticide demand, and equipment maintenance and use requirements (a proxy for equipment demand) within the preset time period. This supports regional planning for matching subsequent farming demand with agricultural resources. As can be understood, taking the example of predicting farmland conditions for the next month, the preset time period is a period within the next month, the time nodes can be different days within the next month, and the farmland conditions at the target time nodes are the farmland conditions corresponding to the specific days within the next month that need to be predicted. In addition, environmental prediction information can be weather forecast information, or environmental information within the preset time period that is inferred and predicted based on weather forecast information and real-time environmental information monitored near the farmland, such as humidity, temperature, and wind speed.

[0023] In addition, after the step of determining the farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land dataset and the historical cultivated land dataset, the method further includes: Determine occasional demand data and long-term demand data based on the farming demand data; determine an occasional correspondence data set based on the occasional demand data, the comprehensive score of agricultural production resources and the location traffic data; determine a long-term correspondence data set between service points and cultivated land based on the long-term demand data, the comprehensive score of agricultural production resources and the location traffic data; determine normal area planning information and occasional area planning information based on the occasional correspondence data set and the long-term correspondence data set; adjust the scope of the target area based on the normal area planning information and the occasional area planning information according to the type of real-time demand data of the target area. It can be understood that after the area is divided according to the normal area planning information, multiple areas, i.e., multiple target areas, are obtained. Due to the existence of occasional demand, the scope of the target area will be adjusted according to whether there is occasional demand, and the scope adjustment is performed based on the occasional area planning information. Specifically, due to the existence of emergencies, such as natural disasters and severe weather, the accuracy of the weather information forecast for the current preset time period is difficult to accurately control. Therefore, it is necessary to further divide the agricultural demand data to determine long-term demand data and occasional demand data. When the region is subsequently divided, at least two sets of regional division plans are determined based on the long-term demand data and the occasional demand data, namely the long-term regional division plan when there are no emergencies, and the occasional regional division plan when emergencies occur in all cultivated lands. The occasional regional division plan is determined by readjusting the divided areas for the local areas of occasional cultivated land based on the occasional demand data under the long-term regional division plan. In this way, under normal circumstances, each area is divided normally to ensure the matching of agricultural demand and agricultural resources. When occasional demand occurs in some cultivated land, the local area is readjusted, thereby ensuring the matching of agricultural demand and agricultural resources in emergency situations while avoiding large-scale regional re-division and affecting efficiency.

[0024] S02, obtain information of multiple service points, and determine the comprehensive score of agricultural production resources corresponding to each service point through a preset evaluation algorithm based on the information of the multiple service points, and map the farming demand data and the comprehensive score of agricultural production resources to a grid map to determine the corresponding location traffic data.

[0025] Specifically, the step of determining the comprehensive score of agricultural production resources corresponding to each service point using a preset evaluation algorithm according to the service point information includes: Determine the supply resource information, agricultural machinery and equipment information, and personnel information of each service point respectively; determine the corresponding supply resource score, agricultural machinery and equipment score, and personnel score based on the corresponding indicator parameters in the supply resource information, agricultural machinery and equipment information, and personnel information of each service point; determine the comprehensive score of agricultural production resources corresponding to different cultivated land information based on the supply resource score, the agricultural machinery and equipment score, and the personnel score. The comprehensive score of agricultural production resources is used to determine the range of agricultural farming needs that can be met by the service point. The cultivated land information includes information on the type of crops planted on each cultivated land.

[0026] In specific implementations, although the agricultural machinery and resources that can be provided by a service point are certain, due to differences in staffing and capabilities, there are significant differences in the efficiency of agricultural machinery utilization and resource supply efficiency. Therefore, it is necessary to comprehensively score each service point based on multiple dimensions to accurately determine the agricultural resources that each service point can provide. Specifically, standardized scores can be performed on each indicator dimension of the service point, and then the standardized scores of each dimension are weighted to determine the final comprehensive score. It can be understood that the comprehensive score of agricultural production resources can determine how much arable land the service point can provide corresponding agricultural resources, that is, how much arable land demand it can meet. By way of example and not limitation, in some optional embodiments, the differences in arable land and the types of crops have a significant impact on the efficiency of agricultural machinery utilization. Therefore, it is also necessary to refer to the comprehensive scores under different arable land conditions and corresponding crop types so that when performing subsequent matching, the corresponding relationship between the agricultural resources provided by the service point and agricultural demand can be more accurately determined, thereby achieving accurate regional division. Therefore, for different farmland conditions and crop types, it is necessary to perform a weighted calculation on the comprehensive agricultural production resource score for each farmland type and crop type to determine the corresponding comprehensive agricultural production resource score for each condition. For example, if the comprehensive agricultural production resource score is 100, and 100 points represents the ability to meet the demand for 100 mu of farmland under standard farmland conditions and standard crop types, then, assuming that within the service point's range, there are X mu of Class A crops under standard farmland conditions and Y mu of standard crops under Class B farmland conditions, multiply X and Y by their corresponding weighting coefficients and add them together to obtain a corresponding score. This score represents the agricultural demand data for the farmland. Comparing this score with the comprehensive agricultural production resource score can determine whether the service point meets the agricultural demand for the farmland and whether there are sufficient agricultural resources to serve other farmland. It should be noted that the above is an example to facilitate understanding of the idea of ​​this plan, that is, after multi-dimensional calculations are performed on the agricultural demand for cultivated land and the comprehensive score of agricultural production resources at service points, they are unified into the same dimension for comparison and determination. In specific implementation, it is not limited to this calculation method.

[0027] It should be noted that the location traffic data at least includes information such as road information and geographical location coordinates, so that subsequent steps can reasonably adjust the correspondence between service points and cultivated land based on their geographical locations and road conditions.

[0028] S03, determining a target correspondence data set between service points and cultivated land through a preset allocation algorithm based on the farming demand data, the comprehensive score of agricultural production resources and the location traffic data, so as to plan farming service areas for service points and cultivated land based on the target correspondence data set.

[0029] Specifically, through location and traffic data, that is, the geographical location and road conditions of each service point and cultivated land, the service points and cultivated land are matched within a certain range to avoid the situation where the geographical location of the service point and the cultivated land is too far away. Then, the service points and cultivated land within the range are allocated according to the agricultural demand data and the comprehensive score of agricultural production resources.

[0030] More specifically, the step of determining a target correspondence relationship dataset between service points and cultivated land using a preset allocation algorithm based on the farming demand data, the comprehensive score of agricultural production resources, and the location and traffic data includes: Determining the farming demand data within each preset divided area based on the location and traffic data, and the difference data between the comprehensive scores of agricultural production resources within each preset divided area, wherein the difference data is obtained by subtracting the farming demand data from the comprehensive score of agricultural production resources; When the difference data is negative, it indicates that the service points in the preset divided area cannot meet the farming needs, and then the preset divided area is adjusted and divided according to the difference data to determine the preliminary divided area, so that the difference data of each preliminary divided area are all positive values, and then the preliminary divided area is adjusted according to the location traffic data to determine the theoretical divided area, so that the standard deviation of the difference data of each theoretical divided area is less than a first preset value; When the difference value is positive, it indicates that the service points in the preset divided area meet the farming needs, and the preset divided area is then adjusted according to the location traffic data to determine the theoretical divided area, so that the standard deviation of the difference data of each theoretical divided area is less than a first preset value; The service points and cultivated land within the theoretically divided area are obtained to determine the target correspondence relationship dataset.

[0031] In specific implementation, the preset division area is the historical division area determined previously. By determining the difference data within each area, it is determined whether the agricultural resources in each area can meet the farming needs. Then the area is divided. Under the condition of meeting the farming needs, local adjustments are made to make the matching of agricultural resources and farming needs in each area closer, thereby avoiding the situation where agricultural resources in some areas are oversupplied and agricultural resources in some areas are barely supplied, and adjusting the allocation efficiency of agricultural resources. It can be understood that the calculation between the comprehensive score of agricultural production resources and the farming demand data can be converted into scores of the same dimension in the above manner. The difference data obtained is a specific score. The area is divided and adjusted by the positive and negative scores and the standard deviation of the scores corresponding to multiple difference data to ensure that after the regional division, the agricultural resources that can be provided by the service points in each area can meet the agricultural needs of the cultivated land in the area, and there will be no excessive waste.

[0032] Furthermore, after the step of adjusting the preliminary divided areas according to the location traffic data to determine the theoretical divided areas, the following steps are included: The theoretical divided area is divided into units to determine a plurality of target units, wherein the service points are distributed within each of the target units so that the standard deviation of the difference data of each target unit is less than a second preset value, and the sum of the driving distances between the service points and the cultivated land within each target unit is minimized, and the second preset value is less than the first preset value; the service points and cultivated land within the target units are obtained to determine the target correspondence relationship dataset.

[0033] In specific implementations, the theoretical regional division is determined based on pre-defined areas. Pre-defined areas are areas that have already been divided in the prior art. This means that the pre-defined areas are relatively large and may be divided into city, county, or township areas. To avoid divisions across large administrative regions, which can lead to scheduling and management issues, the first preset value is set relatively high during the theoretical regional division. Appropriate adjustments are then made to the areas, assuming that agricultural resources can meet farming needs. Therefore, to further improve resource allocation and supply efficiency, the theoretically divided areas can be further divided into units. This allows for the allocation of agricultural resources and farming needs between the units within the theoretically divided areas. This minimizes the fluctuation range of the differential data on agricultural resources and farming needs within each unit, leading to more accurate resource allocation. Furthermore, adjustments can be made to each target unit based on the sum of the driving distances between service points and farmland within each target unit. This minimizes the total distance between service points and farmland, thereby improving the efficiency of agricultural resource supply. Furthermore, since these adjustments are made in a localized manner, scheduling and management issues are reduced, making them easier to overcome and implement. In addition, multiple unit divisions may be performed according to actual needs and conditions, or unit divisions may be performed based on preset areas, and no further restrictions are made here.

[0034] Additionally, an adaptive clustering algorithm can be used for dynamic regional division. Through the four steps of feature extraction, similarity calculation, initial clustering, and adaptive clustering, the clustering results are dynamically adjusted as incremental data on service point distribution, resource capacity, and transportation networks is acquired. This ensures that demand and resources within each region are aligned, improving overall service efficiency.

[0035] Furthermore, after planning agricultural service areas for service points and farmland based on the target correspondence dataset, the system also includes collecting and evaluating agricultural efficiency information within a preset time zone after regional division, and adjusting the preset prediction model, the preset evaluation algorithm, and the preset allocation algorithm based on the evaluation results. Specifically, after regional division, it is necessary to continuously track the execution effect of real-time monitoring and then optimize the algorithm to further improve resource allocation efficiency and thus ensure agricultural needs. In addition, the system will regularly evaluate the planning results and make necessary adjustments based on actual conditions and long-term forecast results to ensure the continued effectiveness of the plan.

[0036] In summary, the agricultural service area planning method in the above-mentioned embodiment of the present invention uses historical farmland information to train a model, allowing the model to determine the farmland conditions during a preset time period based on real-time farmland information, namely, the crop and land conditions during the preset time period. Based on this information, the model determines which agricultural machinery resources, such as equipment, fertilizers, and pesticides, are needed. Information is then collected and evaluated for each service point, accurately determining the scope of agricultural resources that each service point can provide from multiple dimensions. Furthermore, based on the location of each service point and farmland, the service area is divided so that the agricultural resources provided by the service points within each service area can meet the agricultural needs of each farmland within the preset time period. Because the allocation is regulated based on real-time farmland information, and after the initial division, each area is adjusted according to actual conditions to ensure that the agricultural resources in each area can meet the agricultural needs, dynamic allocation of agricultural resources is achieved, which can promptly respond to changes in agricultural needs, including seasonal changes and demand fluctuations caused by emergencies. This not only improves the efficiency and quality of agricultural services, but also effectively avoids resource waste and unmet needs. Therefore, the present invention solves the problem of mismatch between farming demands and agricultural resources in the prior art.

[0037] Example 2 See also Figure 2 , shown is a structural block diagram of the agricultural service area planning system proposed in the second embodiment of the present invention. The agricultural service area planning system 200 includes: an agricultural demand data determination module 201, an agricultural production resource comprehensive score determination module 202, and an agricultural service area planning module 203, wherein: The farming demand data determination module 201 is used to obtain real-time farmland information and historical farmland information to determine corresponding real-time farmland datasets and historical farmland datasets, and determine the farming demand data for a preset time period based on the real-time farmland datasets and the historical farmland datasets using a preset prediction model; The agricultural production resource comprehensive score determination module 202 is configured to obtain information about a plurality of service points, determine the agricultural production resource comprehensive score corresponding to each service point using a preset evaluation algorithm based on the information about the plurality of service points, and map the farming demand data and the agricultural production resource comprehensive score to a grid map to determine corresponding location traffic data; The agricultural service area planning module 203 is used to determine the target correspondence data set between service points and cultivated land through a preset allocation algorithm based on the agricultural demand data, the comprehensive score of agricultural production resources and the location traffic data, so as to plan the agricultural service area for the service points and cultivated land according to the target correspondence data set.

[0038] Furthermore, in other embodiments of the present invention, the historical cultivated land dataset includes at least geographic information, soil information, crop condition information, environmental information, and time node information, and the farming demand data determination module 201 further includes: a model training unit, configured to train the preset prediction model using the historical cultivated land dataset, so that the preset prediction model determines the cultivated land condition at a target time node based on the geographic information, the soil information, the crop condition information, the environmental information, and the time node information; The farming demand data determination unit is used to input the real-time farmland data set and environmental prediction information into the trained preset prediction model to obtain the farmland status information at each time node within the preset time period, so as to determine the farming demand data based on the farmland status information.

[0039] Furthermore, in other embodiments of the present invention, the agricultural production resource comprehensive score determination module 202 includes: An information acquisition unit, used to determine the supply resource information, agricultural machinery and equipment information, and personnel information of each service point; a single-dimension score determination unit, configured to determine a corresponding supply resource score, agricultural machinery score, and personnel score based on corresponding indicator parameters in the supply resource information, the agricultural machinery information, and the personnel information of each of the service points; A comprehensive score determination unit is used to determine the comprehensive score of agricultural production resources corresponding to different cultivated land information based on the supply resource score, the agricultural machinery and equipment score, and the personnel score. The comprehensive score of agricultural production resources is used to determine the range of farming needs that can be met by the service point.

[0040] Furthermore, in other embodiments of the present invention, the farming service area planning module 203 includes: a difference data determining unit, configured to determine, based on the location and traffic data, the farming demand data within each preset divided area, and difference data between the comprehensive scores of agricultural production resources within each preset divided area, the difference data being obtained by subtracting the farming demand data from the comprehensive scores of agricultural production resources; a preliminary division unit, configured to, when the difference data is negative, indicate that the service points within the preset division area cannot meet the farming needs, adjust and divide the preset division area according to the difference data to determine preliminary division areas, so that the difference data of each of the preliminary division areas are all positive values, and then adjust the preliminary division areas according to the location traffic data to determine theoretical division areas, so that the standard deviation of the difference data of each of the theoretical division areas is less than a first preset value; a secondary division unit, configured to, when the difference value is positive, indicate that the service points within the preset division area meet the farming needs, and then adjust the preset division area according to the location traffic data to determine the theoretical division area, so that the standard deviation of the difference data of each theoretical division area is less than a first preset value; The first target correspondence relationship data set determining unit is configured to obtain service points and cultivated land within the theoretically divided area to determine the target correspondence relationship data set.

[0041] Furthermore, in other embodiments of the present invention, the farming service area planning module 203 further includes: a third division unit, configured to divide the theoretical divided area into units to determine a plurality of target units, wherein the service points are distributed within each target unit so that a standard deviation of the difference data of each target unit is less than a second preset value, and a sum of travel distances between service points and cultivated land within each target unit is minimized, and the second preset value is less than the first preset value; The second target correspondence relationship data set determining unit is configured to obtain the service points and cultivated land within the target unit to determine the target correspondence relationship data set.

[0042] Furthermore, in other embodiments of the present invention, the farming service area planning system 200 further includes: a demand data type determination module, configured to determine occasional demand data and long-term demand data based on the farming demand data; An occasional correspondence data set determination module, configured to determine an occasional correspondence data set based on the occasional demand data, the agricultural production resource comprehensive score, and the location traffic data; a long-term correspondence data set determination module, configured to determine a long-term correspondence data set between service points and cultivated land based on the long-term demand data, the comprehensive score of agricultural production resources, and the location and traffic data; Determining normal area planning information and occasional area planning information according to the occasional correspondence relationship dataset and the long-term correspondence relationship dataset respectively; The target area range adjustment module is used to adjust the range of the target area according to the normal area planning information and the occasional area planning information based on the type of the real-time demand data of the target area.

[0043] The feedback adjustment module is used to collect and evaluate the farming efficiency information in the preset time area after the area division, so as to adjust the preset prediction model, the preset evaluation algorithm and the preset deployment algorithm according to the evaluation results.

[0044] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments and will not be described in detail here.

[0045] Example 3 Another aspect of the present invention provides an electronic device, see Figure 3 , shown is a schematic diagram of an electronic device in the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the agricultural service area planning method as described above is implemented.

[0046] In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0047] The memory 20 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 20 may be an internal storage unit of the electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 may also be an external storage device of the electronic device, such as a plug-in hard disk equipped on the electronic device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Furthermore, the memory 20 may include both an internal storage unit of the electronic device and an external storage device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or is about to be output.

[0048] It should be pointed out that Figure 3 The structure shown does not constitute a limitation to the electronic device. In other embodiments, the electronic device may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0049] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the agricultural service area planning method as described above.

[0050] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0051] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0052] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0053] Throughout this specification, references to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. Throughout this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0054] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for planning an agricultural service area, characterized in that: The method comprises, Acquire real-time cultivated land information and historical cultivated land information to respectively determine corresponding real-time cultivated land datasets and historical cultivated land datasets, and determine farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land datasets and the historical cultivated land datasets; Acquire information of a plurality of service points, and determine a comprehensive agricultural production resource score corresponding to each service point using a preset evaluation algorithm based on the information of the plurality of service points, and map the farming demand data and the comprehensive agricultural production resource score to a grid map to determine corresponding location traffic data; According to the farming demand data, the comprehensive score of agricultural production resources and the location traffic data, a target correspondence data set between service points and cultivated land is determined through a preset allocation algorithm, so as to plan the farming service area for service points and cultivated land according to the target correspondence data set.

2. The agricultural service area planning method according to claim 1, characterized in that: The historical cultivated land dataset includes at least geographic information, soil information, crop condition information, environmental information, and time node information. The step of determining farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land dataset and the historical cultivated land dataset includes: The preset prediction model is trained using the historical cultivated land dataset, so that the preset prediction model determines the cultivated land condition at a target time node based on the geographic information, the soil information, the crop condition information, the environmental information, and the time node information; The real-time farmland data set and environmental prediction information are input into the trained preset prediction model to obtain the farmland status information at each time node within the preset time period, so as to determine the farming demand data based on the farmland status information.

3. The agricultural service area planning method according to claim 2, characterized in that: The steps of determining the comprehensive agricultural production resource score corresponding to each service point using a preset evaluation algorithm based on the information of the plurality of service points include: Determine the supply resource information, agricultural machinery and equipment information and personnel information of each service point respectively; Determine corresponding supply resource scores, agricultural machinery scores, and personnel scores based on corresponding indicator parameters in the supply resource information, agricultural machinery information, and personnel information of each of the service points; The comprehensive agricultural production resource score corresponding to different cultivated land information is determined based on the supply resource score, the agricultural machinery and equipment score, and the personnel score. The comprehensive agricultural production resource score is used to determine the range of farming needs that can be met by the service point.

4. The agricultural service area planning method according to claim 3, characterized in that: The step of determining a target correspondence relationship dataset between service points and cultivated land using a preset allocation algorithm according to the farming demand data, the comprehensive score of agricultural production resources, and the location traffic data includes: Determining the farming demand data within each preset divided area based on the location and traffic data, and the difference data between the comprehensive scores of agricultural production resources within each preset divided area, wherein the difference data is obtained by subtracting the farming demand data from the comprehensive score of agricultural production resources; When the difference data is negative, it indicates that the service points in the preset divided area cannot meet the farming needs, and then the preset divided area is adjusted and divided according to the difference data to determine the preliminary divided area, so that the difference data of each preliminary divided area are all positive values, and then the preliminary divided area is adjusted according to the location traffic data to determine the theoretical divided area, so that the standard deviation of the difference data of each theoretical divided area is less than a first preset value; When the difference value is positive, it indicates that the service points in the preset divided area meet the farming needs, and the preset divided area is then adjusted according to the location traffic data to determine the theoretical divided area, so that the standard deviation of the difference data of each theoretical divided area is less than a first preset value; The service points and cultivated land within the theoretically divided area are obtained to determine the target correspondence relationship dataset.

5. The agricultural service area planning method according to claim 4, characterized in that: After the step of adjusting the preliminary divided areas to determine theoretical divided areas according to the location traffic data, the following steps are included: Dividing the theoretical divided area into units to determine a plurality of target units, wherein the service points are distributed within each target unit so that a standard deviation of the difference data of each target unit is less than a second preset value, and a sum of travel distances between service points and cultivated land within each target unit is minimized, and the second preset value is less than the first preset value; The service points and cultivated land within the target unit are obtained to determine the target correspondence relationship dataset.

6. The agricultural service area planning method according to claim 2, characterized in that: After the step of determining the farming demand data for a preset time period using a preset prediction model based on the real-time cultivated land dataset and the historical cultivated land dataset, the following steps are included: determining occasional demand data and long-term demand data based on the farming demand data; Determining an occasional correspondence relationship data set based on the occasional demand data, the agricultural production resource comprehensive score, and the location traffic data; Determining a long-term correspondence dataset between service points and cultivated land based on the long-term demand data, the comprehensive score of agricultural production resources, and the location and traffic data; Determining normal area planning information and occasional area planning information according to the occasional correspondence relationship dataset and the long-term correspondence relationship dataset respectively; The scope of the target area is adjusted according to the normal area planning information and the occasional area planning information according to the type of the real-time demand data of the target area.

7. The agricultural service area planning method according to claim 1, characterized in that: After the step of planning the agricultural service area for the service points and the cultivated land according to the target correspondence relationship dataset, the following steps are further included: The farming efficiency information within the preset time area after the area division is collected and evaluated, so as to adjust the preset prediction model, the preset evaluation algorithm and the preset deployment algorithm according to the evaluation results.

8. A farming service area planning system, characterized in that: For implementing the agricultural service area planning method according to any one of claims 1 to 7, the system comprises: a farming demand data determination module, configured to obtain real-time farmland information and historical farmland information to determine corresponding real-time farmland datasets and historical farmland datasets, respectively, and determine farming demand data for a preset time period using a preset prediction model based on the real-time farmland datasets and the historical farmland datasets; an agricultural production resource comprehensive score determination module, configured to obtain information on a plurality of service points and determine the agricultural production resource comprehensive score corresponding to each service point using a preset evaluation algorithm based on the information on the plurality of service points, and map the farming demand data and the agricultural production resource comprehensive score to a grid map to determine corresponding location traffic data; The agricultural service area planning module is used to determine the target correspondence data set between service points and cultivated land through a preset allocation algorithm based on the agricultural demand data, the comprehensive score of agricultural production resources and the location traffic data, so as to plan the agricultural service area for the service points and cultivated land according to the target correspondence data set.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the agricultural service area planning method as described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for planning an agricultural service area as claimed in any one of claims 1 to 7 is implemented.

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