An agricultural service area planning method and system, a storage medium and an electronic device

Abstract

The application provides a kind of farming service area planning method, system, storage medium and electronic equipment, the method includes: obtaining real-time farmland information and historical farmland information to determine corresponding real-time farmland data set and historical farmland data set respectively, and determine the farming demand data of preset time period according to real-time farmland data set and historical farmland data set;Obtain a plurality of service point information and determine the corresponding agricultural production resource comprehensive score of each service point according to a plurality of service point information by a predetermined evaluation algorithm, and map the farming demand data and the agricultural production resource comprehensive score into the grid map to determine the corresponding location traffic data;According to the target corresponding relationship data set of farming demand data, agricultural production resource comprehensive score and location traffic data, service point and farmland are determined, to carry out farming service area planning according to target corresponding relationship data set to service point and farmland.The application solves the problem that farming demand and agricultural resources are not matched in the prior art.

Description

Technical Field

[0001] This invention relates to the field of regional division technology, and in particular to a method, system, storage medium, and electronic device for agricultural service regional planning. Background Technology

[0002] With the development of technology, agriculture has entered the stage of mechanized farming. Modern agricultural machinery has formed a complete technological system encompassing intelligent navigation tractors, precision variable seeders, and unmanned plant protection drones. Each region typically has corresponding service points that provide services such as agricultural machinery repair and rental, seedling cultivation and supply, fertilizer and pesticide supply, and post-harvest grain drying—in other words, they provide the corresponding agricultural resources. These services may be provided by one or more service points to meet the mechanized farming needs of the corresponding region.

[0003] However, these service points are typically established based on local agricultural development conditions, with at least one corresponding service point in each region, and they usually only provide agricultural services to the corresponding village or town. As agricultural needs change over time, and unforeseen circumstances such as severe environmental conditions or natural disasters also cause these needs to fluctuate. Consequently, some agricultural service points may be unable to meet the current agricultural needs of their regions, while others may experience a surplus of agricultural resources. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a method, system, storage medium, and electronic device for planning agricultural service areas, aiming to solve the problem of mismatch between agricultural demand and agricultural resources in the prior art.

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

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

[0007] Furthermore, the step of determining the comprehensive agricultural production resource score for each service point based on the information of multiple service points using a preset evaluation algorithm includes: Determine the supply resources, agricultural machinery and equipment, and personnel information for each service point; The corresponding supply resource score, agricultural machinery and equipment score and personnel score are determined based on the corresponding indicator parameters in the supply resource information, agricultural machinery and equipment information and personnel information of each service point. The comprehensive score of agricultural production resources is determined 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 needs that the service point can meet. The cultivated land information includes the type of crops planted on each cultivated land.

[0008] Furthermore, the step of determining the target correspondence dataset between service points and cultivated land based on the agricultural demand data, the comprehensive score of agricultural production resources, and the location and traffic data using a preset allocation algorithm includes: Based on the location traffic data, the agricultural demand data in each preset division area is determined, as well as the difference data between the comprehensive scores of agricultural production resources in each preset division area. The difference data is obtained by subtracting the agricultural demand data from the comprehensive score of agricultural production resources. When the difference data is negative, it indicates that the service points within the preset division area cannot meet the agricultural needs. Then, the preset division area is adjusted and divided according to the difference data to determine the preliminary division area, so that the difference data of each preliminary division area is positive. Then, the preliminary division area is adjusted 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 the first preset value. When the difference number is positive, it indicates that the service points within the preset division area meet the agricultural needs. Then, the preset division area is adjusted 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 the first preset value. Obtain the service points and cultivated land within the theoretically divided area to determine the target correspondence dataset.

[0009] Furthermore, after adjusting the initially defined area based on the location traffic data to determine the theoretically defined area, the process includes: The theoretically defined region is divided into units to determine multiple target units, wherein each target unit contains service points, such 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 in each target unit is minimized, wherein the second preset value is less than the first preset value. Obtain the service points and cultivated land within the target unit to determine the target correspondence dataset.

[0010] Further, the step of determining agricultural demand data for a preset time period based on the real-time arable land dataset and the historical arable land dataset using a preset prediction model includes: Based on the aforementioned agricultural demand data, determine the incidental demand data and the long-term demand data; The dataset of occasional corresponding relationships is determined based on the incidental demand data, the comprehensive score of agricultural production resources, and the location and traffic data; The long-term demand data, the comprehensive score of agricultural production resources, and the location and traffic data are used to determine the long-term correspondence dataset between service points and cultivated land. Based on the occasional correspondence dataset and the long-term correspondence dataset, determine the normal regional planning information and the occasional regional planning information, respectively. The scope of the target area is adjusted based on the type of real-time demand data for the target area, according to the normal area planning information and the occasional area planning information.

[0011] Furthermore, after the step of planning agricultural service areas for service points and cultivated land based on the target correspondence dataset, the method further includes: After the region is divided, agricultural efficiency information within a preset time period is collected and evaluated, so as to adjust the preset prediction model, the preset evaluation algorithm and the preset allocation 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: The agricultural demand data determination module is used to acquire real-time cultivated land information and historical cultivated land information to determine the corresponding real-time cultivated land dataset and historical cultivated land dataset respectively, and to determine the agricultural demand data for a preset time period based on the real-time cultivated land dataset and the historical cultivated land dataset through a preset prediction model. The agricultural production resource comprehensive score determination module is used to acquire information on multiple service points and determine the comprehensive score of agricultural production resources for each service point based on the information on multiple service points through a preset evaluation algorithm, and map the agricultural demand data and the comprehensive score of agricultural production resources to a grid map to determine the corresponding location traffic data. The agricultural service area planning module is used to determine the target correspondence dataset between service points and cultivated land based on the agricultural demand data, the comprehensive score of agricultural production resources, and the location and traffic data through a preset allocation algorithm, so as to plan the agricultural service area for service points and cultivated land according to the target correspondence dataset.

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

[0014] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described agricultural service area planning method.

[0015] This invention trains a model using historical farmland information, enabling the model to determine farmland conditions for a preset time period based on real-time farmland data. This includes determining crop and land conditions within the preset timeframe. Based on this information, the model assesses the necessary agricultural resources, such as equipment, fertilizers, and pesticides. Furthermore, it collects and evaluates information from various service points, accurately determining the range of agricultural resources each service point can provide from multiple dimensions. Then, based on the location of each service point and farmland, the service area is divided to ensure that the agricultural resources provided by service points within each service area can meet the agricultural needs of each farmland within the preset time period. Because the allocation is based on real-time farmland information, and adjustments are made to each area after the initial division based on actual conditions, ensuring that agricultural resources in each area cover agricultural needs, dynamic allocation of agricultural resources is achieved. This allows for timely response to changes in agricultural demand, including seasonal variations and demand fluctuations caused by unforeseen events. This not only improves the efficiency and quality of agricultural services but also effectively avoids resource waste and unmet needs. Therefore, this invention solves the problem of mismatch between agricultural demand and agricultural resources in existing technologies. Attached Figure Description

[0016] Figure 1 This is a flowchart 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 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention; The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0017] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0018] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document 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 of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the 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 Please see Figure 1 The figure shows the agricultural service area planning method in the first embodiment of the present invention, which specifically includes steps S01-S03.

[0021] S01, acquire real-time farmland information and historical farmland information to determine the corresponding real-time farmland dataset and historical farmland dataset respectively, and determine the agricultural demand data for a preset time period based on the real-time farmland dataset and the historical farmland dataset through a preset prediction model.

[0022] Specifically, the historical farmland dataset includes at least geographical information, soil information, crop condition information, environmental information, and time node information. The step of determining agricultural demand data for a preset time period based on the real-time farmland dataset and the historical farmland dataset using a preset prediction model includes: The preset prediction model is trained using the historical farmland dataset to determine the farmland condition at a target time point based on geographical information, soil information, crop condition information, environmental information, and time node information. The real-time farmland dataset and environmental prediction information are then input into the trained preset prediction model to obtain farmland condition information for each time node within the preset time period. This information is then used to determine agricultural demand data based on the crop condition information. The preset prediction model, trained with big data, can predict the future condition of farmland based on farmland information. It is further trained using historical data of the current farmland to improve accuracy. Finally, it uses the real-time farmland dataset to predict farmland condition information for the preset time period, thereby determining agricultural needs at each time node within that period, including equipment, fertilizer, sowing, pesticide, and equipment maintenance and usage requirements. This provides support for regional planning that matches agricultural needs with agricultural resources. Understandably, taking the prediction of farmland conditions for the next month as an example, the preset time period is the entire next month, and the time nodes can be different numbers of days within the next month. The target time node is the farmland condition at the specific day within the next month that needs to be predicted. Furthermore, the environmental prediction information can be weather forecast information, or it can be environmental information within a 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] Additionally, the step of determining agricultural demand data for a preset time period based on the real-time arable land dataset and the historical arable land dataset using a preset prediction model includes: Based on the agricultural demand data, incidental demand data and long-term demand data are determined; based on the incidental demand data, the comprehensive score of agricultural production resources, and the location and transportation data, an incidental correspondence dataset is determined; based on the long-term demand data, the comprehensive score of agricultural production resources, and the location and transportation data, a long-term correspondence dataset between service points and cultivated land is determined; based on the incidental correspondence dataset and the long-term correspondence dataset, normal regional planning information and incidental regional planning information are determined respectively; based on the type of real-time demand data of the target area, the scope of the target area is adjusted according to the normal regional planning information and the incidental regional planning information. It is understood that after dividing the area according to the normal regional planning information, multiple areas are obtained, i.e., multiple target areas. Due to the existence of incidental demand, the scope of the target area will be adjusted according to whether incidental demand exists, and the scope adjustment is based on the incidental regional planning information. Specifically, due to the existence of unforeseen circumstances, such as natural disasters and severe weather, the accuracy of weather information prediction for the current preset time period is difficult to control accurately. Therefore, it is necessary to further divide the agricultural demand data to determine long-term demand data and incidental demand data. This will allow for at least two regional division schemes to be determined during subsequent regional allocation: a long-term regional division scheme under normal circumstances, and an incidental regional division scheme when incidental demand occurs in all arable land areas. The incidental regional division scheme is determined by readjusting the already divided regions based on the incidental demand data, within the framework of the long-term regional division scheme, specifically for localized areas of the incidentally affected arable land. This ensures that under normal circumstances, each region is properly divided to guarantee the matching of agricultural demand and resources. When incidental demand occurs in some arable land areas, localized adjustments are made, thus ensuring the matching of agricultural demand and resources under emergency conditions while avoiding large-scale regional re-division that could impact efficiency.

[0024] S02, acquire information on 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 on multiple service points, and map the agricultural demand data and the comprehensive score of agricultural production resources to a grid map to determine the corresponding location traffic data.

[0025] Specifically, the steps for determining the comprehensive score of agricultural production resources for each service point based on the service point information and using a preset evaluation algorithm include: The supply resource information, agricultural machinery and equipment information, and personnel information of each service point are determined respectively. Based on the corresponding indicator parameters in the supply resource information, agricultural machinery and equipment information, and personnel information of each service point, the corresponding supply resource score, agricultural machinery and equipment score, and personnel score are determined. Based on the supply resource score, agricultural machinery and equipment score, and personnel score, the comprehensive agricultural production resource score corresponding to different cultivated land information is determined. The comprehensive agricultural production resource score is used to determine the range of agricultural needs that the service point can meet. The cultivated land information includes the crop type information planted on each cultivated land.

[0026] In practice, while the agricultural machinery and resources available at each service point are fixed, differences in staffing and capabilities lead to significant variations in machinery utilization and resource supply efficiency. Therefore, a comprehensive evaluation of each service point across multiple dimensions is necessary to accurately determine its agricultural resource capacity. Specifically, standardized scores can be applied to each service point across various indicators, followed by a weighted average to determine the final comprehensive score. This comprehensive agricultural resource score helps determine the area of ​​arable land a service point can provide corresponding agricultural resources to, i.e., the area of ​​arable land it can meet. As an example, and not a limitation, in some alternative embodiments, variations in arable land and crop types significantly impact machinery utilization efficiency. Therefore, comprehensive scores under different arable land conditions and corresponding crop types should also be considered to more accurately determine the correspondence between agricultural resources provided by service points and agricultural needs during subsequent matching, leading to more precise regional division. Therefore, for different arable land conditions and crop types, it is necessary to further weight the comprehensive score of agricultural production resources based on arable land type and crop type to determine the corresponding comprehensive score of agricultural production resources under that condition. Taking an agricultural production resource comprehensive score of 100, where 100 points represent the amount of arable land that can meet the needs of 100 mu (approximately 6.7 hectares) under standard arable land conditions and standard crop types as an example, assuming that there are X mu (approximately 0.67 hectares) of crop A under standard arable land conditions and Y mu (approximately 0.67 hectares) of standard crop B under standard arable land conditions on a piece of arable land within the service area, multiplying X and Y by their respective weighting coefficients and adding them together yields a corresponding score. This score represents the agricultural demand data for that arable land. Comparing this score with the comprehensive score of agricultural production resources allows us to determine whether the service point meets the agricultural needs of that arable land and whether there are any surplus agricultural resources available to serve other arable land. It should be noted that the above examples are for the purpose of understanding the approach of this scheme. That is, after calculating the comprehensive score of agricultural demand for arable land and agricultural production resources of service points in multiple dimensions, they are unified into the same dimension for comparison and determination. In actual implementation, it is not limited to this calculation method.

[0027] It should be noted that location traffic data includes at least road information and geographic coordinates, so that subsequent steps can reasonably allocate the correspondence between service points and farmland based on their geographical location and road conditions.

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

[0029] Specifically, by using location traffic data, namely the geographical location and road conditions of each service point and farmland, the service points and farmland are matched within a certain range to avoid situations where the geographical distance between the service points and farmland is too far. Then, the service points and farmland within this range are allocated based on agricultural demand data and a comprehensive score of agricultural production resources.

[0030] More specifically, the steps of determining the target correspondence dataset between service points and arable land based on the agricultural demand data, the comprehensive score of agricultural production resources, and the location and traffic data using a preset allocation algorithm include: Based on the location traffic data, the agricultural demand data in each preset division area is determined, as well as the difference data between the comprehensive scores of agricultural production resources in each preset division area. The difference data is obtained by subtracting the agricultural demand data from the comprehensive score of agricultural production resources. When the difference data is negative, it indicates that the service points within the preset division area cannot meet the agricultural needs. Then, the preset division area is adjusted and divided according to the difference data to determine the preliminary division area, so that the difference data of each preliminary division area is positive. Then, the preliminary division area is adjusted 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 the first preset value. When the difference number is positive, it indicates that the service points within the preset division area meet the agricultural needs. Then, the preset division area is adjusted 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 the first preset value. Obtain the service points and cultivated land within the theoretically divided area to determine the target correspondence dataset.

[0031] In practical implementation, the pre-defined regions are historically defined areas. By identifying the differences in data within each region, it is determined whether agricultural resources in each region can meet agricultural needs. Then, the regions are further divided. If agricultural needs are met, local adjustments are made to ensure a closer match between agricultural resources and agricultural needs in each region. This avoids situations where some regions have an oversupply of agricultural resources while others have a meager supply, thus adjusting the efficiency of agricultural resource allocation. Understandably, the calculation between the comprehensive score of agricultural production resources and agricultural demand data can be performed using the above method, converting both into scores of the same dimension. The resulting difference data becomes the specific score. The positive or negative value of the score and the standard deviation of the scores corresponding to multiple difference data are used to adjust the regional division. This ensures that after regional division, the agricultural resources that service points in each region can provide can meet the agricultural needs of the arable land within that region, without resulting in excessive surplus and waste.

[0032] Furthermore, after adjusting the initially defined area based on the location traffic data to determine the theoretically defined area, the process includes: The theoretically defined region is divided into units to determine multiple target units, wherein each target unit contains service points, such 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 in each target unit is minimized, wherein the second preset value is less than the first preset value; the service points and cultivated land in the target units are obtained to determine the target correspondence dataset.

[0033] In practical implementation, the theoretical regional division is determined based on a pre-defined region. This pre-defined region is a region already divided in existing technologies, meaning its scope is relatively large, potentially divided by city, county, or township. To avoid dividing across large administrative regions and causing scheduling and management problems, a relatively large initial pre-defined value is set for the theoretical regional division. This value is then adjusted appropriately, provided that agricultural resources can meet agricultural needs. Therefore, to further improve resource allocation efficiency, the theoretically defined region can be further subdivided into units. This allows for the allocation of agricultural resources and agricultural needs among the units within the theoretically defined region, minimizing fluctuations in the differences in agricultural resources and needs within each unit, thus leading to more accurate resource allocation. Furthermore, adjustments can be made to each target unit based on the sum of the travel distances between service points and cultivated land, minimizing the total distance between service points and cultivated land to improve the efficiency of agricultural resource supply. Since these detailed adjustments are made in localized areas, scheduling and management problems are fewer and easier to overcome and implement. In addition, depending on actual needs and circumstances, the units can be divided multiple times, or the units can be divided based on the preset area. No further restrictions are imposed here.

[0034] Alternatively, dynamic region partitioning can be achieved through adaptive clustering algorithms. This involves four steps: feature extraction, similarity calculation, initial clustering, and adaptive clustering. As data on service point distribution, resource capacity, and transportation networks are incrementally acquired, the clustering results are dynamically adjusted. This aims to ensure that demand and resources are matched within each region, thereby improving overall service efficiency.

[0035] Furthermore, after the step of planning agricultural service areas for service points and arable land based on the target correspondence dataset, the system further includes: collecting and evaluating agricultural efficiency information within a preset time period after area division, so as to adjust the preset prediction model, the preset evaluation algorithm, and the preset allocation algorithm based on the evaluation results. Specifically, after area division, it is necessary to continuously track the execution effect through real-time monitoring, and then optimize the algorithm to further improve the resource allocation effect, thereby ensuring agricultural needs. In addition, the system will periodically evaluate the planning effect and make necessary adjustments based on the actual situation and long-term prediction results to ensure the continuous effectiveness of the planning.

[0036] In summary, the agricultural service area planning method in the above embodiments of the present invention trains the model using historical farmland information. This allows the model to determine the farmland conditions for a preset time period based on real-time farmland information, including crop and land conditions. Based on this information, the model determines the necessary agricultural machinery resources such as equipment, fertilizers, and pesticides. Furthermore, it collects and evaluates information from various service points, accurately assessing the range of agricultural resources each service point can provide from multiple dimensions. Then, based on the locations of each service point and farmland, the service area is divided to ensure that the agricultural resources provided by service points within each service area can meet the agricultural needs of each farmland within the preset time period. Because the allocation is based on real-time farmland information, and adjustments are made to each area after the initial division based on actual conditions, ensuring that agricultural resources in each area cover agricultural needs, dynamic allocation of agricultural resources is achieved. This allows for timely response to changes in agricultural demand, including seasonal variations and demand fluctuations caused by unforeseen events. 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 agricultural needs and agricultural resources in the prior art.

[0037] Example 2 Please see Figure 2 The diagram 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 scoring determination module 202, and an agricultural service area planning module 203, wherein: The agricultural demand data determination module 201 is used to acquire real-time cultivated land information and historical cultivated land information to determine the corresponding real-time cultivated land dataset and historical cultivated land dataset respectively, and to determine the agricultural demand data for a preset time period based on the real-time cultivated land dataset and the historical cultivated land dataset through a preset prediction model. The agricultural production resource comprehensive score determination module 202 is used to acquire information on multiple service points, determine the comprehensive score of agricultural production resources corresponding to each service point through a preset evaluation algorithm based on the information on multiple service points, and map the agricultural demand data and the comprehensive score of agricultural production resources to a grid map to determine the corresponding location traffic data. The agricultural service area planning module 203 is used to determine the target correspondence dataset between service points and cultivated land based on the agricultural demand data, the comprehensive score of agricultural production resources and the location and traffic data through a preset allocation algorithm, so as to plan the agricultural service area for service points and cultivated land according to the target correspondence dataset.

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

[0039] Furthermore, in other embodiments of the present invention, the agricultural production resource comprehensive scoring and determination module 202 includes: The information acquisition unit is used to determine the supply resource information, agricultural machinery and equipment information, and personnel information of each service point. A single-dimensional scoring determination unit is used to 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. The comprehensive scoring 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 agricultural needs that the service point can meet.

[0040] Furthermore, in other embodiments of the present invention, the agricultural service area planning module 203 includes: The difference data determination unit is used to determine the agricultural demand data in each preset division area based on the location traffic data, and the difference data between the comprehensive scores of agricultural production resources in each preset division area. The difference data is obtained by subtracting the agricultural demand data from the comprehensive scores of agricultural production resources. The preliminary division unit is used to adjust and divide the preset division area according to the difference data when the difference data is negative, indicating that the service points in the preset division area cannot meet the agricultural needs, so as to determine the preliminary division area, so that the difference data of each of the preliminary division areas are positive. Then, the preliminary division area is adjusted according to the location traffic data to determine the theoretical division area, 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 is used to indicate that the service points within the preset division area meet the agricultural needs when the difference number is positive. Then, the preset division area is adjusted 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 dataset determination unit is used to obtain service points and cultivated land within the theoretically divided area to determine the target correspondence dataset.

[0041] Furthermore, in other embodiments of the present invention, the agricultural service area planning module 203 further includes: The three-stage division unit is used to divide the theoretically divided area into units to determine multiple target units. Each target unit contains service points, such 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 in each target unit is minimized. The second preset value is less than the first preset value. The second target correspondence dataset determination unit is used to obtain service points and cultivated land within the target unit to determine the target correspondence dataset.

[0042] Furthermore, in other embodiments of the present invention, the agricultural service area planning system 200 also includes: The demand data type determination module is used to determine occasional demand data and long-term demand data based on the agricultural demand data. The occasional correspondence dataset determination module is used to determine the occasional correspondence dataset based on the occasional demand data, the comprehensive score of agricultural production resources, and the location and traffic data. The long-term correspondence dataset determination module is used to determine the 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. Based on the occasional correspondence dataset and the long-term correspondence dataset, determine the normal regional planning information and the occasional regional planning information, respectively. The target area range adjustment module is used to adjust the range of the target area based on the type of real-time demand data of the target area, according to the normal area planning information and the occasional area planning information.

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

[0044] The functions or operation steps implemented by the above modules are largely the same as those in the above method embodiments, and will not be repeated here.

[0045] Example 3 In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shows an electronic device according to 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, it implements the agricultural service area planning method as described above.

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

[0047] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic 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 will be output.

[0048] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0049] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the agricultural service area planning method described above.

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

[0051] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0052] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0053] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above 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 one or more embodiments or examples.

[0054] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for planning agricultural service areas, characterized in that, The method includes, Acquire real-time and historical farmland information to determine the corresponding real-time and historical farmland datasets respectively, and determine the agricultural demand data for a preset time period based on the real-time and historical farmland datasets using a preset prediction model. Acquire information on 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 on multiple service points. Map the agricultural demand data and the comprehensive score of agricultural production resources to a grid map to determine the corresponding location traffic data. Based on the agricultural demand data, the comprehensive score of agricultural production resources, and the location and traffic data, a target correspondence dataset between service points and cultivated land is determined by a preset allocation algorithm, so as to plan agricultural service areas for service points and cultivated land according to the target correspondence dataset. The historical farmland dataset includes at least geographical information, soil information, crop condition information, environmental information, and time node information. The steps of determining agricultural demand data for a preset time period based on the real-time farmland dataset and the historical farmland dataset using a preset prediction model include: The preset prediction model is trained using the historical farmland dataset so that the preset prediction model can determine the farmland status at the target time node based on the geographical information, the soil information, the crop status information, the environmental information, and the time node information. The real-time farmland dataset and environmental prediction information are input into the trained preset prediction model to obtain farmland status information at each time node within the preset time period, so as to determine the agricultural demand data based on the farmland status information. The steps for determining the comprehensive score of agricultural production resources for each service point based on information from multiple service points using a preset evaluation algorithm include: Determine the supply resources, agricultural machinery and equipment, and personnel information for each service point; The corresponding supply resource score, agricultural machinery and equipment score and personnel score are determined based on the corresponding indicator parameters in the supply resource information, agricultural machinery and equipment information and personnel information of each service point. The comprehensive score of agricultural production resources 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 score of agricultural production resources is used to determine the range of agricultural needs that the service point can meet.

2. The method for planning agricultural service areas according to claim 1, characterized in that, The steps for determining the target correspondence dataset between service points and arable land based on the agricultural demand data, the comprehensive score of agricultural production resources, and the location and traffic data using a preset allocation algorithm include: Based on the location traffic data, the agricultural demand data in each preset division area is determined, as well as the difference data between the comprehensive scores of agricultural production resources in each preset division area. The difference data is obtained by subtracting the agricultural demand data from the comprehensive scores of agricultural production resources. When the difference data is negative, it indicates that the service points within the preset division area cannot meet the agricultural needs. Then, the preset division area is adjusted and divided according to the difference data to determine the preliminary division area, so that the difference data of each preliminary division area is positive. Then, the preliminary division area is adjusted 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 the first preset value. When the difference number is positive, it indicates that the service points within the preset division area meet the agricultural needs. Then, the preset division area is adjusted 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 the first preset value. Obtain the service points and cultivated land within the theoretically divided area to determine the target correspondence dataset.

3. The method for planning agricultural service areas according to claim 2, characterized in that, After adjusting the initially defined area based on the location traffic data to determine the theoretically defined area, the following steps are included: The theoretically defined region is divided into units to determine multiple target units, wherein each target unit contains service points, such 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 in each target unit is minimized, wherein the second preset value is less than the first preset value. Obtain the service points and cultivated land within the target unit to determine the target correspondence dataset.

4. The method for planning agricultural service areas according to claim 1, characterized in that, The step of determining agricultural demand data for a preset time period based on the real-time arable land dataset and the historical arable land dataset using a preset prediction model includes: Based on the aforementioned agricultural demand data, determine the incidental demand data and the long-term demand data; The dataset of occasional corresponding relationships is determined based on the incidental demand data, the comprehensive score of agricultural production resources, and the location and traffic data; The long-term demand data, the comprehensive score of agricultural production resources, and the location and traffic data are used to determine the long-term correspondence dataset between service points and cultivated land. Based on the occasional correspondence dataset and the long-term correspondence dataset, determine the normal regional planning information and the occasional regional planning information, respectively. The scope of the target area is adjusted based on the type of real-time demand data for the target area, according to the normal area planning information and the occasional area planning information.

5. The method for planning agricultural service areas according to claim 1, characterized in that, Following the step of planning agricultural service areas for service points and cultivated land based on the target correspondence dataset, the following further steps are also included: After the region is divided, agricultural efficiency information within a preset time period is collected and evaluated, so as to adjust the preset prediction model, the preset evaluation algorithm and the preset allocation algorithm according to the evaluation results.

6. An agricultural service area planning system, characterized in that, The system for implementing the agricultural service area planning method as described in any one of claims 1 to 5 includes: The agricultural demand data determination module is used to acquire real-time cultivated land information and historical cultivated land information to determine the corresponding real-time cultivated land dataset and historical cultivated land dataset respectively, and to determine the agricultural demand data for a preset time period based on the real-time cultivated land dataset and the historical cultivated land dataset through a preset prediction model. The agricultural production resource comprehensive score determination module is used to acquire information on multiple service points and determine the comprehensive score of agricultural production resources for each service point based on the information on multiple service points through a preset evaluation algorithm, and map the agricultural demand data and the comprehensive score of agricultural production resources to a grid map to determine the corresponding location traffic data. The agricultural service area planning module is used to determine the target correspondence dataset between service points and cultivated land based on the agricultural demand data, the comprehensive score of agricultural production resources, and the location and traffic data through a preset allocation algorithm, so as to plan the agricultural service area for service points and cultivated land according to the target correspondence dataset.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the agricultural service area planning method as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the agricultural service area planning method as described in any one of claims 1-5.

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