Farmland regional planting structure optimization method and system based on multi-source data

Abstract

The present invention relates to the field of smart agricultural technology, and in particular to a method and system for optimizing the planting structure of a farmland region based on multi-source data, the method comprising acquiring multi-source data; classifying the monitored farmland region data step by step and calculating the suitable evaluation value of the crops planted in the sub-region, and determining the farmland region to be optimized; performing a primary optimization and a secondary optimization on the farmland region to be optimized; comparing the optimization result with the target optimization result and adjusting the evaluation index to obtain the adjusted evaluation value; calculating the idle planting area of ​​the optimized sub-region and feeding back the soil moisture level and the idle planting area of ​​the sub-region to the adjacent production entity. The present invention realizes the self-learning adjustment of the evaluation model through the optimization result and determines the adjusted evaluation value by classifying the monitored farmland region data step by step and performing a primary optimization and a secondary optimization on the farmland region to be optimized, and adjusting the evaluation index according to the optimization result, thereby improving the accuracy of the optimization of the planting structure of the farmland region.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural technology, and in particular to a method and system for optimizing farmland area planting structure based on multi-source data. Background Art

[0002] With the development of information and communication technology, agriculture has also shifted from traditional agriculture to digital agriculture.

[0003] The patent document with publication number CN116797046A discloses a crop planting structure optimization method based on multi-scale and comprehensive evaluation. The SWAT-WF distributed hydrological model is used to simulate regional crop water productivity and production water footprint, and a multi-objective spatial crop planting structure optimization model is established with the goals of maximizing regional economic water productivity, minimizing blue water dependence and minimizing gray water footprint; a comprehensive evaluation index system is constructed by combining multi-level benefit indicators; and the entropy weight-TOPS I S comprehensive evaluation model is used to achieve the optimization of multiple crop planting structure optimization schemes.

[0004] However, the existing technology optimizes the crop planting structure by connecting virtual water trade of different crops in the region, resulting in a single optimization condition for the crop planting structure and a lack of self-learning and adjustment capabilities based on the optimization results, leading to inaccurate optimization of the crop planting structure. Summary of the invention

[0005] To this end, the present invention provides a method and system for optimizing the planting structure of farmland areas based on multi-source data, which classifies the farmland area data step by step and determines the farmland areas to be optimized in a graded manner, optimizes the farmland areas to be optimized once and twice, and adjusts the evaluation index according to the optimization results to obtain an adjusted evaluation value, so as to solve the problem of inaccurate optimization of crop planting structure in the prior art.

[0006] To achieve the above object, the present invention provides a method for optimizing farmland regional planting structure based on multi-source data, the method comprising:

[0007] Obtaining data on the farmland area to be monitored, production entity data, and historical precipitation data of the area where the farmland area to be monitored is located;

[0008] According to the evaluation index in the preset comprehensive suitability evaluation model, the data of the farmland area to be monitored are classified step by step and the suitability evaluation value of the crops planted in the sub-area of ​​the farmland area to be monitored is calculated; when the evaluation value is less than the standard evaluation value or the evaluation value is greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-area is low, and the sub-area is determined to be the farmland area to be optimized;

[0009] According to the soil moisture level of the sub-region, a subset of planted crop types suitable for growth is selected from the product raw material type set of the production entity to perform a primary optimization on the first-level farmland area to be optimized, and according to the supply-demand relationship between the yield of the planted crop types in the sub-region radiated by the production entity and the demand for the planted crop types required by the production entity, the farmland area to be optimized is secondary optimized to obtain an optimization result;

[0010] Comparing the optimization result with the target optimization result and judging whether the optimization result is qualified to obtain a judgment result, and adjusting the evaluation index according to the judgment result to obtain an adjusted evaluation value;

[0011] The difference between the optimized planting area of ​​the sub-region and the area of ​​the sub-region is calculated to obtain the optimized idle planting area of ​​the sub-region, and the soil moisture level of the sub-region and the idle planting area are fed back to the adjacent production entity.

[0012] Furthermore, the farmland area to be monitored includes several sub-areas, and the data of each sub-area corresponds to a data group, and the data in the data group includes the geographical location of the sub-area, the type of crops planted in the sub-area, and the planting area of ​​the sub-area; the production subject data includes the geographical location of the production subject, the type of product raw materials, and the quantity of product raw materials;

[0013] The evaluation index includes a first evaluation index, a second evaluation index and a third evaluation index, wherein the first evaluation index is the ratio of the spatial straight-line distance from the geographical location of the farmland sub-area to be monitored to the geographical location of the production subject to the standard distance radiated by the production subject; when the first evaluation index is less than or equal to 1, it is judged that the sub-area is within the radiation range of the production subject; when the first evaluation index is greater than 1, it is judged that the sub-area is not within the radiation range of the production subject; the standard distance radiated by the production subject is the radius of the circle drawn with the position of the production subject as the center;

[0014] According to the first evaluation index, the data of the farmland area to be monitored are classified into a first-level category, including first-level category one data and first-level category two data, wherein the first-level category one data is a data group corresponding to the sub-area within the radiation range of the production subject, and the first-level category two data is a data group corresponding to the sub-area not within the radiation range of the production subject;

[0015] The second evaluation index is a correlation coefficient between the types of crops planted in the sub-region and the types of product raw materials of the production entity. When the types of crops planted in the sub-region are included in the types of product raw materials of the production entity, the second evaluation index is 1; when the types of crops planted in the sub-region are not included in the types of product raw materials of the production entity, the second evaluation index is 0;

[0016] The first-level first-category data is classified into second-level categories according to the second evaluation index, including second-level first-category data and second-level second-category data, wherein the second-level first-category data is a data group corresponding to the sub-region whose planted crop types are included in the product raw material type set of the production entity, and the second-level second-category data is a data group corresponding to the sub-region whose planted crop types are not included in the product raw material type set of the production entity;

[0017] The third evaluation index is the ratio of the yield of the crops planted in the sub-region to the quota of the raw materials of the products corresponding to the crop planting type required by the production entity to be allocated to the crops planted in the sub-region. When the third evaluation index is less than 1, it is judged that the yield of the crops planted in the sub-region is insufficient. When the third evaluation index is equal to 1, it is judged that the yield of the crops planted in the sub-region is qualified. When the third evaluation index is greater than 1, it is judged that the yield of the crops planted in the sub-region is excessive.

[0018] The second-level first-category data are classified into three levels according to the third evaluation index, including third-level first-category data, third-level second-category data and third-level third-category data, wherein the third-level first-category data is the data group corresponding to the sub-region with insufficient crop yield, the third-level second-category data is the data group corresponding to the sub-region with qualified crop yield, and the third-level third-category data is the data group corresponding to the sub-region with excessive crop yield.

[0019] Furthermore, the preset comprehensive suitability evaluation model is that the suitability evaluation value of the crops planted in the sub-region of the farmland area to be monitored is equal to the sum of the first evaluation index, the second evaluation index and the third evaluation index, a standard evaluation value is set, and a maximum evaluation value is set. When the evaluation value is less than the standard evaluation value or greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-region is low, and the sub-region is determined to be the farmland area to be optimized;

[0020] When the first evaluation index is greater than 1, it is determined that the crops planted in the sub-region corresponding to the first-level second-category data are not suitable for evaluation;

[0021] When the first evaluation index is less than or equal to 1, it is determined that the data contained in the first-level and first-category data is further suitable for evaluation, including:

[0022] When the first evaluation index is less than or equal to 1 and the second evaluation index is 0, the suitability evaluation value of the crops planted in the sub-region corresponding to the second-level second-category data is calculated as the first evaluation index, and the evaluation value is less than the standard evaluation value, it is judged that the comprehensive suitability of the crops planted in the sub-region corresponding to the second-level second-category data is low, and the sub-region corresponding to the second-level second-category data is determined to be a first-level farmland area to be optimized;

[0023] When the first evaluation index is less than or equal to 1 and the second evaluation index is 1, it is determined that the data included in the second-level first-category data is further suitable for evaluation, including:

[0024] When the first evaluation index is less than or equal to 1, the second evaluation index is 1, and the third evaluation index is less than 1, the suitability evaluation value of the crops planted in the sub-region corresponding to the third-level first-category data is calculated as the sum of the first evaluation index, 1, and the third evaluation index, and the evaluation value is less than the standard evaluation value, and it is judged that the comprehensive suitability of the crops planted in the sub-region corresponding to the third-level first-category data is low, and the sub-region corresponding to the third-level first-category data is determined to be a second-level first-category farmland area to be optimized;

[0025] When the first evaluation index is less than or equal to 1, the second evaluation index is 1, and the third evaluation index is equal to 1, the suitability evaluation value of the crops planted in the sub-region corresponding to the three-level two-category data is calculated as the sum of the first evaluation index, 1, and 1, and the evaluation value is greater than or equal to the standard evaluation value and less than or equal to the maximum evaluation value, and it is judged that the comprehensive suitability of the crops planted in the sub-region corresponding to the three-level two-category data is high, and the sub-region corresponding to the three-level two-category data is determined to be a suitable farmland area;

[0026] When the first evaluation index is less than or equal to 1, the second evaluation index is 1, and the third evaluation index is greater than 1, the suitability evaluation value of the crops in the sub-area corresponding to the three-level three-category data is calculated as the sum of the first evaluation index, 1, and the third evaluation index. If the evaluation value is greater than the maximum evaluation value, it is judged that the comprehensive suitability of the crops in the sub-area corresponding to the three-level three-category data is low, and the sub-area corresponding to the three-level three-category data is determined to be a second-level second-category farmland area to be optimized.

[0027] Further, according to the soil moisture level of the sub-region, suitable planting crop types are selected from the product raw material type set of the production entity to optimize the first-level farmland area to be optimized, wherein the soil moisture level of the sub-region is positively correlated with the precipitation state index of the area where the farmland area to be optimized is located, and the precipitation state index is calculated based on the historical precipitation data of the area where the farmland area to be monitored is located;

[0028] The product raw material type set of the production entity includes several types of crops, each type of crop corresponds to a different soil moisture level suitable for growth, the soil moisture levels include primary soil moisture, secondary soil moisture, and tertiary soil moisture, wherein each of the soil moisture levels corresponds to a subset of crop types, each of the subset of crop types includes one or more crop types, the multiple crop types are sorted from high to low in the crop type subset according to the degree to which the crop types are suitable for growth at the soil moisture level, and the crop types in the crop type subset are the crop types to be selected in the primary farmland area to be optimized.

[0029] Furthermore, the farmland area to be optimized is secondarily optimized according to the supply-demand relationship between the yield of the planted crop types in the sub-area radiated by the production entity and the demand for the planted crop types by the production entity. The farmland area to be optimized includes a first-level farmland area to be optimized, a second-level first-class farmland area to be optimized, and a second-level second-class farmland area to be optimized, and the supply-demand relationship includes insufficient supply and excessive supply.

[0030] Furthermore, the insufficient supply of the crop yield is determined based on a first difference between the crop yield of the sub-region corresponding to the second-level first-category farmland area to be optimized and the quota quantity of product raw materials corresponding to the crop planting type required by the production entity to be allocated to the crops planted in the sub-region, and then the crop types with insufficient crop yield are compared with the crop types to be selected in the first-level farmland area to be optimized within the crop type subset; when the crop types to be selected include the crop types with insufficient crop yield, the crop types with insufficient crop yield are selected to perform secondary optimization on the first-level farmland area to be optimized;

[0031] The increased planting area of ​​the crop types selected within the crop type subset is determined based on the first difference and the standard per-acre yield of the selected crop types, where the increased planting area = first difference / standard per-acre yield, wherein the first difference = quota quantity - yield.

[0032] Furthermore, based on the second difference between the yield of planted crops in the sub-area corresponding to the second-level second-category farmland area to be optimized and the quota quantity of product raw materials corresponding to the crop planting type required by the production entity to be allocated to the planted crops in the sub-area, it is determined that the yield of the planted crops is in oversupply. Based on the second difference and the standard per-acre yield of the crop type, it is determined that the planted crop area in the second-level second-category farmland area to be optimized should be reduced, and the reduction in planted area = second difference / standard per-acre yield, wherein the second difference = yield - quota quantity.

[0033] Further, a judgment result is obtained by comparing the optimization result with the target optimization result and judging whether the optimization result is qualified, and the evaluation index is adjusted according to the judgment result to obtain an adjusted evaluation value, the optimization result is an actual third evaluation index, and the target optimization result is that the actual third evaluation index is equal to 1;

[0034] The actual output of the sub-area after the secondary optimization is obtained and the ratio thereof to the quota quantity is obtained to obtain an actual third evaluation index; when the actual third evaluation index is not equal to 1, it is determined that the actual output of the sub-area after the secondary optimization is unqualified; the first evaluation index is adjusted to obtain an adjusted first evaluation index and the third evaluation index is adjusted to obtain an adjusted third evaluation index; and the sum of the adjusted first evaluation index, the second evaluation index and the adjusted third evaluation index is calculated to obtain an adjusted evaluation value;

[0035] When the actual third evaluation index is less than 1, it is judged that the actual yield of the sub-region after the secondary optimization is insufficient, and the first adjustment coefficient is reduced to expand the standard distance of the production entity radiation, and the second adjustment coefficient is increased to increase the planting area for the second time;

[0036] When the actual third evaluation index is greater than 1, it is judged that the actual yield of the sub-area after the secondary optimization is excessive, and the first adjustment coefficient is increased to reduce the standard distance of the production entity's radiation, and the second adjustment coefficient is reduced to reduce the planting area for the second time.

[0037] Furthermore, the optimized planting area of ​​the sub-region to be optimized is obtained according to the increase in the planting area of ​​the second-level type one farmland area to be optimized and the reduction in the planting area of ​​the second-level type two farmland area to be optimized; when the ratio of the optimized planting area to the area of ​​the sub-region is less than 1, the difference between the optimized planting area of ​​the sub-region and the area of ​​the sub-region is calculated to obtain the idle planting area of ​​the optimized sub-region and the soil moisture level of the sub-region and the idle planting area are fed back to the adjacent production entities.

[0038] On the other hand, the present invention also provides a system for optimizing a method for cropping structure in a farmland region based on multi-source data, comprising:

[0039] An acquisition module is used to acquire the farmland area data to be monitored, the production subject data and the historical precipitation data of the area where the farmland area to be monitored is located;

[0040] An evaluation module, connected to the acquisition module, comprising a processing unit, a first calculation unit and a judgment unit, for classifying the farmland area data to be monitored step by step according to the evaluation index in the preset comprehensive suitability evaluation model and calculating the suitability evaluation value of the crops planted in the sub-area of ​​the farmland area to be monitored; when the evaluation value is less than the standard evaluation value or the evaluation value is greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-area is low, and the sub-area is determined to be the farmland area to be optimized;

[0041] an optimization module, connected to the evaluation module, comprising a first optimization unit and a second optimization unit, for selecting a subset of planted crop species suitable for growth from the product raw material species set of the production subject according to the soil moisture level of the sub-region to optimize the first-level farmland region to be optimized, and for performing a second optimization on the farmland region to be optimized according to the supply-demand relationship between the yield of the planted crop species of the sub-region radiated by the production subject and the demand for the planted crop species required by the production subject, and obtaining an optimization result;

[0042] An adjustment module, connected to the optimization module, for comparing the optimization result with the target optimization result and determining whether the optimization result is qualified to obtain a determination result, and adjusting the evaluation index according to the determination result to obtain an adjusted evaluation value;

[0043] The feedback module is connected to the adjustment module and includes a second calculation unit and a feedback unit, which is used to calculate the difference between the optimized planting area of ​​the sub-area and the area of ​​the sub-area to obtain the optimized idle planting area of ​​the sub-area and feed back the soil moisture level of the sub-area and the idle planting area to the adjacent production entity.

[0044] Compared with the prior art, the beneficial effects of the present invention are that the data of the monitored farmland area are classified step by step through the evaluation index, and the data is classified according to the evaluation conditions, which can improve the accuracy of data analysis; the suitability evaluation value of the crops in the sub-area of ​​the monitored farmland area is obtained by calculation, so that the result of the evaluation value is more scientific and reliable; the suitability of the crops in the sub-area is judged by comparing the evaluation value with the standard evaluation value and the maximum evaluation value, thereby improving the reliability of the planting structure analysis; the farmland area to be optimized is determined according to the suitability of the crops, so that the determination of the farmland area to be optimized is more accurate; a subset of crop types suitable for growth is selected according to the soil moisture level of the sub-area to perform a primary optimization on the first-level farmland area to be optimized, thereby realizing the preliminary optimization of the farmland area to be optimized; the farmland area to be optimized is secondary optimized according to the supply and demand relationship between the yield of the crop types in the sub-area radiated by the production entity and the demand for the crop types required by the production entity, so that the farmland areas to be optimized are mutually optimized according to the supply and demand relationship, thereby realizing Further optimization of the preliminary optimization improves the optimization accuracy of the farmland area to be optimized; by calculating the idle planting area of ​​the optimized sub-area, the accuracy of the planting structure analysis of the optimized farmland area is improved; by comparing the optimization results with the target optimization results to determine whether the optimization is qualified, it is helpful to verify the optimization process and improve the accuracy of the optimization; by adjusting the evaluation index according to different judgment results and obtaining the adjusted evaluation value, the evaluation model is verified to improve the accuracy of the evaluation value of the planting structure of the farmland area, and the evaluation model is continuously self-learned and adjusted through the optimization results to determine the adjusted evaluation value, so as to guide the next optimization of the farmland planting structure; by feeding back the soil moisture level and idle planting area of ​​the sub-area to the adjacent production entity, the type of crops planted in the idle planting area can be further determined according to the adjacent production entity data and the soil moisture level and idle planting area of ​​the sub-area, so as to achieve re-optimization of the idle area in the farmland area after the secondary optimization and improve the accuracy of the planting structure analysis of the farmland area.

[0045] In particular, by obtaining the geographical location of the farmland sub-area and the geographical location of the production entity, it is helpful to obtain the distance from the farmland sub-area to the production entity, by obtaining the types of raw materials of the production entity's products, it is helpful to determine the types of crops planted in the farmland sub-area, and by obtaining the quantity of product raw materials, it is helpful to determine the yield of crops planted in the farmland sub-area, making the analysis of farmland planting structure more accurate and reliable; by setting the first evaluation index, it is helpful to evaluate the suitability of crops planted in the farmland sub-area according to the distance from the location of the sub-area to the location of the production entity, by setting the second evaluation index, it is helpful to evaluate the suitability of crop types planted in the farmland sub-area, and by setting the third evaluation index, it is helpful to evaluate the suitability of crops planted in the farmland sub-area according to the supply and demand relationship between the yield of the farmland sub-area and the product raw material demand of the production entity; by presetting the comprehensive suitability evaluation model, the scientificity and objectivity of the comprehensive evaluation of the suitability of crops planted in the farmland sub-area are improved.

[0046] In particular, by classifying the monitored farmland area data into a primary level according to the results of the first evaluation index, classifying the monitored farmland area data into a secondary level according to the results of the second evaluation index, and classifying the monitored farmland area data into a tertiary level according to the results of the third evaluation index, a step-by-step classification of the data is achieved, which is helpful for the layer-by-layer analysis of the farmland planting structure.

[0047] In particular, by conducting a suitability evaluation on the secondary data, the first-level farmland areas to be optimized were determined, and by conducting a further suitability evaluation on the tertiary data, the second-level Class I and second-level Class II farmland areas to be optimized were determined, thus achieving a layer-by-layer analysis of the farmland planting structure and improving the accuracy of the analysis of the farmland planting structure.

[0048] In particular, the precipitation state index is calculated based on the historical precipitation data of the area where the monitored farmland area is located, and the soil moisture is determined based on the precipitation state index, which improves the scientificity and accuracy of the determination of soil moisture; by determining the types of crops to be selected in the first-level area to be optimized according to the soil moisture level, a primary optimization of the first-level area to be optimized is achieved.

[0049] In particular, the farmland area to be optimized is secondary optimized based on the supply-demand relationship between the yield of the crop types planted in the region and the demand for the crop types planted by the production entities, thereby optimizing the planting structure of the sub-region through supply and demand balance, making the optimization of the planting structure of the area to be optimized more scientific and reliable.

[0050] In particular, by determining the amount of insufficient supply of the production entity by the output of the farmland area through the first difference, further secondary optimization of the first-level data to be optimized is achieved, and the types of planted crops in the sub-areas corresponding to the first-level farmland areas to be optimized are determined, making the optimization of the first-level farmland areas to be optimized more accurate, and realizing the optimization of the planting structure of the second-level first-class farmland areas to be optimized.

[0051] In particular, the second difference is used to determine the amount of excess supply of the farmland area output to the production entity, thereby achieving optimization of the second-level and second-class farmland areas to be optimized.

[0052] In particular, by comparing the optimization results with the target optimization results to determine whether the optimization is qualified, it is helpful to verify the optimization process and improve the accuracy of the optimization; by using different real-time adjustment methods according to different judgment results, the optimization results are made closer to the target optimization results, and the optimization deviations gradually converge, thereby improving the scientific nature and reliability of the optimization and the accuracy of the optimization results; by adjusting the evaluation index to obtain the adjusted evaluation value, the evaluation model is verified, the accuracy of the evaluation value of the planting structure of the farmland area is improved, and the evaluation model is continuously self-learned and adjusted through the optimization results to determine the adjusted evaluation value, so as to guide the next optimization of the farmland planting structure.

[0053] In particular, by calculating the difference in the areas of the sub-areas, the idle planting area of ​​the optimized sub-areas is obtained, and the soil moisture level and idle planting area of ​​the sub-areas are fed back to the adjacent production entities. The types of crops planted in the idle planting areas can be further determined based on the adjacent production entity data and the soil moisture level and idle planting area of ​​the sub-areas, thereby achieving further optimization of the idle areas in the farmland areas after the secondary optimization, and improving the accuracy of the planting structure analysis and optimization results of the farmland areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of a method for optimizing cropping structure in farmland area based on multi-source data provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a farmland area to be monitored by radiation from production entities in a farmland area planting structure optimization method based on multi-source data provided in an embodiment of the present invention;

[0056] Figure 3 A structural diagram for classifying the farmland area data to be monitored in the farmland area planting structure optimization method based on multi-source data provided in an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of the structure of a farmland area planting structure optimization system based on multi-source data provided by an embodiment of the present invention;

[0058] Figure numerals: 1. Acquisition module; 2. Evaluation module; 201. Processing unit; 202. First calculation unit; 203. Judgment unit; 3. Optimization module; 301. First optimization unit; 302. Second optimization unit; 4. Adjustment module; 5. Feedback module; 501. Second calculation unit; 502. Feedback unit; 6. Production entity; 7. Farmland area to be monitored; 8. First sub-area; 9. Second sub-area; 10. i-th sub-area. DETAILED DESCRIPTION

[0059] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0061] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0062] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] Method and system for optimizing farmland regional planting structure based on multi-source data, please refer to Figure 1-4 As shown, it can be implemented as follows:

[0064] A method for optimizing cropping structure in farmland area based on multi-source data, such as Figure 1 As shown, the method includes:

[0065] S01, obtaining the data of the farmland area to be monitored, the production entity data and the historical precipitation data of the area where the farmland area to be monitored is located;

[0066] S02, classifying the data of the farmland area to be monitored step by step according to the evaluation index in the preset comprehensive suitability evaluation model and calculating the suitability evaluation value of the crops planted in the sub-area of ​​the farmland area to be monitored; when the evaluation value is less than the standard evaluation value or the evaluation value is greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-area is low, and the sub-area is determined to be the farmland area to be optimized;

[0067] S03, according to the soil moisture level of the sub-region, a subset of planted crop types suitable for growth is selected from the product raw material type set of the production entity to optimize the first-level farmland area to be optimized, and according to the supply-demand relationship between the yield of the planted crop types in the sub-region radiated by the production entity and the demand for the planted crop types required by the production entity, a secondary optimization is performed on the farmland area to be optimized and an optimization result is obtained;

[0068] S04, comparing the optimization result with the target optimization result and judging whether the optimization result is qualified to obtain a judgment result, and adjusting the evaluation index according to the judgment result to obtain an adjusted evaluation value;

[0069] S05, calculating the difference between the optimized planting area of ​​the sub-region and the area of ​​the sub-region to obtain the idle planting area of ​​the optimized sub-region and feeding back the soil moisture level of the sub-region and the idle planting area to the adjacent production entity.

[0070] The data of the monitored farmland area are classified step by step through the evaluation index, so that the data can be classified according to the evaluation conditions, which can improve the accuracy of data analysis; the suitability evaluation value of the crops in the sub-area of ​​the monitored farmland area is obtained by calculation, so that the evaluation value result is more scientific and reliable; the suitability of the crops in the sub-area is judged by comparing the evaluation value with the standard evaluation value and the maximum evaluation value, which improves the reliability of the planting structure analysis; the farmland area to be optimized is determined according to the suitability of the crops, so that the determination of the farmland area to be optimized is more accurate; according to the soil moisture level of the sub-area, a subset of crop types suitable for growth is selected to optimize the first-level farmland area to be optimized, so as to achieve the preliminary optimization of the farmland area to be optimized; according to the supply and demand relationship between the yield of the crop types in the sub-area radiated by the production entity and the demand for the crop types required by the production entity, the farmland area to be optimized is optimized twice, so that the farmland areas to be optimized can achieve mutual optimization according to the supply and demand relationship, so as to achieve further optimization of the preliminary optimization. The optimization accuracy of the optimized farmland area is improved; the idle planting area of ​​the optimized sub-area is obtained by calculation, which improves the accuracy of the analysis of the planting structure of the optimized farmland area; by comparing the optimization result with the target optimization result to judge whether the optimization is qualified, it is helpful to verify the optimization process and improve the accuracy of the optimization; by adjusting the evaluation index according to different judgment results and obtaining the adjusted evaluation value, the evaluation model is verified, the accuracy of the evaluation value of the planting structure of the farmland area is improved, and the evaluation model is adjusted through continuous self-learning through the optimization results to determine the adjusted evaluation value, so as to guide the optimization of the farmland planting structure next time; by feeding back the soil moisture level and idle planting area of ​​the sub-area to the adjacent production entity, the type of crops planted in the idle planting area can be further determined according to the adjacent production entity data and the soil moisture level and idle planting area of ​​the sub-area, so as to realize the re-optimization of the idle area in the farmland area after the secondary optimization and improve the accuracy of the analysis of the planting structure of the farmland area.

[0071] like Figure 2 As shown, specifically, the farmland area 7 to be monitored includes several sub-areas, and the sub-areas include the first sub-area 8, the second sub-area 9 ... the i-th sub-area 10. The farmland area data to be monitored includes several data groups, each data group corresponds to the data of a sub-area, and the data in the data group include the geographical location of the sub-area, the type of crops planted in the sub-area, and the planting area of ​​the sub-area; the production subject data include the geographical location of the production subject 6, the type of product raw materials, and the quantity of product raw materials;

[0072] The preset comprehensive suitability evaluation model is M=k1+k2+k3, where M is the suitability evaluation value of the crops in the sub-region of the farmland area to be monitored, the standard evaluation value is set to M0, and the maximum evaluation value is set to Mx. When M<M0 or M>Mx, it is judged that the suitability of the crops in the sub-region is low, and the sub-region is determined to be the farmland area to be optimized;

[0073] The evaluation index includes a first evaluation index, a second evaluation index and a third evaluation index, wherein k1 is the first evaluation index, which is the correlation coefficient between the distance from the location of the sub-region to the location of the production subject and the standard distance radiated by the production subject, k2 is the second evaluation index, which is the correlation coefficient between the types of crops planted in the sub-region and the types of product raw materials of the production subject, and k3 is the third evaluation index, which is the correlation coefficient between the yield of crops planted in the sub-region and the quantity of product raw materials of the production subject;

[0074] The first evaluation index k1 = s0 / si, where s0 is the standard distance of the radiation of the production subject, si is the spatial straight-line distance from the geographical location of the ith sub-region in the farmland area to be monitored to the geographical location of the production subject, when 0 < k1 ≤ 1, it is judged that the sub-region is within the radiation range of the production subject, and when k1 > 1, it is judged that the sub-region is not within the radiation range of the production subject;

[0075] The second evaluation index k2=1 or k2=0, when the types of crops planted in the sub-region are included in the types of product raw materials of the production entity, k2=1, when the types of crops planted in the sub-region are not included in the types of product raw materials of the production entity, k2=0;

[0076] The third evaluation index k3 = Qi / Q0i, set the crop yield of the i-th sub-region to Qi, set the quota quantity of product raw materials corresponding to the crop planting type required by the production entity to be allocated to the crops planted in the i-th sub-region to Q0i,

[0077] When 0≤Q i<Q0 i, k3<1, it is judged that the yield of crops planted in the sub-region is insufficient;

[0078] When Qi = Q0i, k3 = 1, the yield of crops planted in the sub-region is judged to be qualified;

[0079] When Qi>Q0i, k3>1, it is judged that the yield of crops planted in the sub-region is excessive.

[0080] By obtaining the geographical location of the farmland sub-area and the geographical location of the production entity, it is helpful to obtain the distance from the farmland sub-area to the production entity. By obtaining the types of raw materials of the production entity's products, it is helpful to determine the types of crops planted in the farmland sub-area. By obtaining the quantity of product raw materials, it is helpful to determine the yield of crops planted in the farmland sub-area, making the analysis of farmland planting structure more accurate and reliable; by setting the first evaluation index, it is helpful to evaluate the suitability of crops planted in the farmland sub-area according to the distance from the location of the sub-area to the location of the production entity. By setting the second evaluation index, it is helpful to evaluate the suitability of crop types planted in the farmland sub-area. By setting the third evaluation index, it is helpful to evaluate the suitability of crops planted in the farmland sub-area according to the supply and demand relationship between the yield of the farmland sub-area and the product raw material demand of the production entity; by presetting the comprehensive suitability evaluation model, the scientificity and objectivity of the comprehensive evaluation of the suitability of crops planted in the farmland sub-area are improved.

[0081] Specifically, if Figure 3 As shown, the monitored farmland area data is classified level by level, and the first-level classification is performed according to the result of the first evaluation index, including first-level Class I data and first-level Class II data, wherein the first-level Class I data is the data group corresponding to the sub-area within the radiation range of the production subject, and the first-level Class II data is the data group corresponding to the sub-area not within the radiation range of the production subject;

[0082] According to the result of the second evaluation index, the first-level category one data is classified into a second-level category one data and a second-level category two data, wherein the second-level category one data is a data group corresponding to a sub-region whose planted crop species are included in the product raw material type set of the production entity, and the second-level category two data is a data group corresponding to a sub-region whose planted crop species are not included in the product raw material type set of the production entity;

[0083] According to the results of the third evaluation index, the second-level category one data are classified into three levels, including third-level category one data, third-level category two data and third-level category three data, among which the third-level category one data is the data group corresponding to the sub-regions with insufficient crop yields, the third-level category two data is the data group corresponding to the sub-regions with qualified crop yields, and the third-level category three data is the data group corresponding to the sub-regions with excessive crop yields.

[0084] By classifying the monitored farmland area data into a primary level according to the results of the first evaluation index, classifying the monitored farmland area data into a secondary level according to the results of the second evaluation index, and classifying the monitored farmland area data into a tertiary level according to the results of the third evaluation index, we achieve a level-by-level classification of the data, which is helpful for the layer-by-layer analysis of the farmland planting structure.

[0085] Specifically, when k1>1, it is determined that no suitability evaluation is performed on the planting crops in the sub-region corresponding to the first-level second-category data;

[0086] When 0<k1≤1, it is determined that the data contained in the first-level and first-category data shall be further evaluated appropriately, including:

[0087] When 0<k1≤1 and k2=0, the suitability evaluation value M=k1 of the crops in the sub-region corresponding to the second-level second-category data is calculated, then M<M0, and it is judged that the comprehensive suitability of the crops in the sub-region corresponding to the second-level second-category data is low, and the sub-region corresponding to the second-level second-category data is determined to be the first-level farmland area to be optimized;

[0088] When 0<k1≤1 and k2=1, it is determined that the data contained in the second-level first-category data shall be further appropriately evaluated, including:

[0089] When 0<k1≤1 and k2=1 and k3<1, the suitability evaluation value of the crops in the sub-region corresponding to the third-level first-class data is calculated as M=k1+1+k3, then M<M0, and it is judged that the comprehensive suitability of the crops in the sub-region corresponding to the third-level first-class data is low, and the sub-region corresponding to the third-level first-class data is determined to be the second-level first-class farmland area to be optimized;

[0090] When 0<k1≤1 and k2=1 and k3=1, the suitability evaluation value of the crops in the sub-region corresponding to the third-level second-class data is calculated as M=k1+1+1, then M0≤M≤Mx, and it is judged that the comprehensive suitability of the crops in the sub-region corresponding to the third-level second-class data is high, and the sub-region corresponding to the third-level second-class data is determined to be a suitable farmland area;

[0091] When 0<k1≤1 and k2=1 and k3>1, the suitability evaluation value of crops in the sub-area corresponding to the third-level third-category data is calculated as M=k1+1+k3, then M>Mx, and it is judged that the comprehensive suitability of crops in the sub-area corresponding to the third-level third-category data is low, and the sub-area corresponding to the third-level third-category data is determined to be the second-level second-category farmland area to be optimized.

[0092] By conducting a suitability evaluation on the secondary data, the first-level farmland areas to be optimized were determined. By conducting a further suitability evaluation on the tertiary data, the second-level Class I and second-level Class II farmland areas to be optimized were determined. This enabled us to analyze the farmland planting structure layer by layer, thereby improving the accuracy of the analysis of the farmland planting structure.

[0093] Specifically, according to the soil moisture level of the sub-region, the types of crops suitable for growth are selected from the types of raw materials of the production entity to optimize the first-level farmland area to be optimized, wherein the soil moisture level of the sub-region is positively correlated with the precipitation state index of the area where the farmland area to be optimized is located, and the precipitation state index is calculated based on the historical precipitation data of the area where the farmland area to be monitored is located;

[0094] The precipitation state index calculation model PCI=(TRMNmax-TRMN) / (TRMNmax-TRMNmim) is pre-set, where PCI is the precipitation state index, TRMNmax is the maximum rainfall within 10 days in the same period of the past ten years at the location of the farmland area to be optimized, TRMNmim is the minimum rainfall within 10 days in the same period of the past ten years at the location of the farmland area to be optimized, and TRMN is the rainfall within the current 10 days at the location of the farmland area to be optimized;

[0095] The production entity's product raw material category set includes several types of crops, each type of crop corresponds to a different soil moisture level suitable for growth, the soil moisture levels include primary soil moisture, secondary soil moisture, and tertiary soil moisture, wherein each soil moisture level corresponds to a subset of crop types, each subset of crop types includes one or more crop types, and the multiple crop types are sorted from high to low in the crop type subset according to the degree to which the crop types are suitable for growth in the soil moisture level, and the crop types in the crop type subset are the first-level crop types to be selected in the farmland area to be optimized.

[0096] The precipitation status index is calculated based on the historical precipitation data of the area where the farmland area to be monitored is located, and the soil moisture is determined based on the precipitation status index, which improves the scientificity and accuracy of the determination of soil moisture; by determining the type of crops to be selected in the first-level area to be optimized according to the soil moisture level, the first-level area to be optimized is optimized once.

[0097] Specifically, the farmland areas to be optimized are secondary optimized according to the supply and demand relationship between the yield of the crop types planted in the sub-areas radiated by the production entity and the demand for the crop types planted by the production entity. The farmland areas to be optimized include the first-level farmland areas to be optimized, the second-level first-class farmland areas to be optimized, and the second-level second-class farmland areas to be optimized. The supply and demand relationship includes insufficient supply and oversupply.

[0098] According to the supply-demand relationship between the yield of the crop types planted in the region and the demand for the crop types planted by the production entities, the farmland area to be optimized is secondary optimized, and the planting structure of the sub-region is optimized through the balance of supply and demand, making the optimization of the planting structure of the area to be optimized more scientific and reliable.

[0099] Specifically, the insufficient supply of crop yield is determined based on the first difference between the crop yield of the sub-region corresponding to the second-level first-category farmland area to be optimized and the quota quantity of product raw materials corresponding to the crop planting types required by the production entity and allocated to the sub-region crops, then the crop types with insufficient crop yield are compared with the crop types to be selected in the first-level farmland area to be optimized within the crop type subset, and when the crop types to be selected include the crop types with insufficient crop yield, the crop types with insufficient crop yield are selected to perform secondary optimization on the first-level farmland area to be optimized;

[0100] The increased planting area of ​​the crop types selected within the crop type subset is determined based on the first difference and the standard per-acre yield of the selected crop types, where the increased planting area = first difference / standard per-acre yield, wherein the first difference = quota quantity - yield.

[0101] By determining the amount of insufficient supply of the production entity by the output of the farmland area through the first difference, further secondary optimization of the first-level data to be optimized is achieved, and the types of planted crops in the sub-areas corresponding to the first-level farmland areas to be optimized are determined, making the optimization of the first-level farmland areas to be optimized more accurate, and realizing the optimization of the planting structure of the second-level first-class farmland areas to be optimized.

[0102] Specifically, the oversupply of crop yield is determined based on the second difference between the crop yield of the sub-area corresponding to the second-level second-category farmland area to be optimized and the quota quantity of product raw materials corresponding to the crop planting type required by the production entity allocated to the sub-area crop yield. Based on the second difference and the standard per-acre yield of the crop type, the reduction in the planting area of ​​crops in the second-level second-category farmland area to be optimized is determined, and the reduction in planting area = second difference / standard per-acre yield, where the second difference = yield - quota quantity.

[0103] The second difference is used to determine the amount of excess supply of farmland area output to production entities, thereby optimizing the second-level and second-category farmland areas to be optimized.

[0104] The optimization result is compared with the target optimization result to determine whether the optimization result is qualified to obtain a judgment result, and the evaluation index is adjusted according to the judgment result to obtain an adjusted evaluation value, the optimization result is the actual third evaluation index, and the target optimization result is that the actual third evaluation index is equal to 1;

[0105] The actual output of the sub-region after the secondary optimization is obtained and the ratio of the actual output to the quota quantity is obtained to obtain the actual third evaluation index. When the actual third evaluation index is not equal to 1, it is judged that the actual output of the sub-region after the secondary optimization is unqualified, and the first evaluation index and the third evaluation index are adjusted to obtain the adjusted evaluation value;

[0106] The adjusted evaluation model is set to M'=k1'+k2+k3', where M' is the adjusted evaluation value, k1' is the adjusted first evaluation index, k3' is the adjusted third evaluation index, k1'=α×k1, k3'=γ×k3, α is the first adjustment coefficient, γ is the second adjustment coefficient,

[0107] When the actual third evaluation index is less than 1, it is judged that the actual yield of the sub-region after the secondary optimization is insufficient, and the first adjustment coefficient is adjusted to α-m×j to expand the standard distance of the production subject radiation, and the second adjustment coefficient is adjusted to γ+n×j to increase the planting area for the second time; m is the first adjustment step number, n is the second adjustment step number, j is the adjustment step length and 0<j<1;

[0108] When the actual third evaluation index is greater than 1, it is judged that the actual yield of the sub-area after the secondary optimization is excessive, and the first adjustment coefficient is adjusted to α+m×j to shorten the standard distance of the production entity radiation, and the second adjustment coefficient is adjusted to γ-n×j to reduce the planting area for the second time.

[0109] By comparing the optimization results with the target optimization results to determine whether the optimization is qualified, it is helpful to verify the optimization process and improve the accuracy of the optimization; by using different real-time adjustment methods according to different judgment results, the optimization results are made closer to the target optimization results, and the optimization deviations are gradually converged, thereby improving the scientific nature and reliability of the optimization and the accuracy of the optimization results; by adjusting the evaluation index to obtain the adjusted evaluation value, the evaluation model is verified, the accuracy of the evaluation value of the planting structure of the farmland area is improved, and the evaluation model is continuously self-learned and adjusted through the optimization results to determine the adjusted evaluation value, so as to guide the next optimization of the farmland planting structure.

[0110] Specifically, the optimized planting area of ​​the sub-region to be optimized is obtained by increasing the planting area of ​​the second-level type-1 farmland area to be optimized and reducing the planting area of ​​the second-level type-2 farmland area to be optimized. When the ratio of the optimized planting area to the area of ​​the sub-region is less than 1, the difference between the optimized planting area of ​​the sub-region and the area of ​​the sub-region is calculated to obtain the idle planting area of ​​the optimized sub-region, and the soil moisture level and idle planting area of ​​the sub-region are fed back to the adjacent production entities.

[0111] By calculating the difference in the areas of the sub-areas, the idle planting area of ​​the optimized sub-areas is obtained, and the soil moisture level and idle planting area of ​​the sub-areas are fed back to the adjacent production entities. The types of crops planted in the idle planting areas can be further determined based on the data of the adjacent production entities and the soil moisture level and idle planting area of ​​the sub-areas, thereby achieving further optimization of the idle areas in the farmland areas after the secondary optimization, and improving the accuracy of the planting structure analysis and optimization results of the farmland areas.

[0112] On the other hand, the present invention also provides a system for optimizing the planting structure of farmland areas based on multi-source data, such as Figure 4 As shown, the system includes:

[0113] Acquisition module 1 is used to obtain the farmland area data to be monitored, production subject data and historical precipitation data of the area where the farmland area to be monitored is located;

[0114] The evaluation module 2 is connected to the acquisition module, and includes a processing unit 201, a first calculation unit 202 and a judgment unit 203, which is used to classify the farmland area data to be monitored step by step according to the evaluation index in the preset comprehensive suitability evaluation model and calculate the suitability evaluation value of the crops planted in the sub-area of ​​the farmland area to be monitored. When the evaluation value is less than the standard evaluation value or the evaluation value is greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-area is low, and the sub-area is determined to be the farmland area to be optimized;

[0115] The optimization module 3 is connected to the evaluation module, and includes a first optimization unit 301 and a second optimization unit 302, which are used to select a subset of planted crop types suitable for growth from the product raw material type set of the production subject according to the soil moisture level of the sub-region to optimize the first-level farmland area to be optimized, and to perform a second optimization on the farmland area to be optimized according to the supply-demand relationship between the yield of the planted crop types in the sub-region radiated by the production subject and the demand for the planted crop types required by the production subject;

[0116] The adjustment module 4 is connected to the optimization module, and is used to compare the optimization result with the target optimization result and determine whether the optimization result is qualified to obtain a judgment result, and to adjust the evaluation index according to the judgment result to obtain an adjusted evaluation value;

[0117] The feedback module 5 is connected to the adjustment module, and includes a second calculation unit 501 and a feedback unit 502, which are used to calculate the difference between the optimized planting area of ​​the sub-area and the area of ​​the sub-area to obtain the idle planting area of ​​the optimized sub-area and feed back the soil moisture level and idle planting area of ​​the sub-area to the adjacent production entity.

[0118] Specifically, in this embodiment, when the adjacent production entity receives the optimized idle planting area information of the farmland sub-area sent by the feedback module, the type of crops planted in the idle planting area is further determined based on the adjacent production entity data, the soil moisture level of the sub-area, and the idle planting area. The idle planting area is the area corresponding to the idle planting area, and the type of crops planted in the idle planting area is determined based on the adjacent production entity data, the soil moisture level of the sub-area, and the idle planting area.

[0119] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing farmland area planting structure based on multi-source data, characterized in that: include: Obtaining data on the farmland area to be monitored, production entity data, and historical precipitation data of the area where the farmland area to be monitored is located; According to the evaluation index in the preset comprehensive suitability evaluation model, the data of the farmland area to be monitored are classified step by step and the suitability evaluation value of the crops planted in the sub-area of ​​the farmland area to be monitored is calculated; when the evaluation value is less than the standard evaluation value or the evaluation value is greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-area is low, and the sub-area is determined to be the farmland area to be optimized; According to the soil moisture level of the sub-area, a subset of planted crop types suitable for growth is selected from the product raw material type set of the production entity, and the first-level farmland area to be optimized is optimized once, and according to the supply-demand relationship between the yield of the planted crop types in the sub-area radiated by the production entity and the demand for the planted crop types required by the production entity, the farmland area to be optimized is optimized twice to obtain the optimization result; Comparing the optimization result with the target optimization result and judging whether the optimization result is qualified, and adjusting the evaluation index according to the judgment result to obtain an adjusted evaluation value; Calculate the difference between the optimized planting area of ​​the sub-region and the area of ​​the sub-region to obtain the optimized idle planting area of ​​the sub-region and feed back the soil moisture level of the sub-region and the idle planting area to the adjacent production entity; The farmland area to be monitored includes several sub-areas, and the data of each sub-area corresponds to a data group, and the data in the data group includes the geographical location of the sub-area, the type of crops planted in the sub-area, and the planting area of ​​the sub-area; the production subject data includes the geographical location of the production subject, the type of product raw materials, and the quantity of product raw materials; The evaluation index includes a first evaluation index, a second evaluation index and a third evaluation index, wherein the first evaluation index is the ratio of the spatial straight-line distance from the geographical location of the sub-area of ​​the farmland area to be monitored to the geographical location of the production subject to the standard distance radiated by the production subject; when the first evaluation index is less than or equal to 1, it is judged that the sub-area is within the radiation range of the production subject; when the first evaluation index is greater than 1, it is judged that the sub-area is not within the radiation range of the production subject; the standard distance radiated by the production subject is the radius of the circle drawn with the position of the production subject as the center; According to the first evaluation index, the data of the farmland area to be monitored are classified into a first-level category, including first-level category one data and first-level category two data, wherein the first-level category one data is a data group corresponding to the sub-area within the radiation range of the production subject, and the first-level category two data is a data group corresponding to the sub-area not within the radiation range of the production subject; The second evaluation index is a correlation coefficient between the types of crops planted in the sub-region and the types of product raw materials of the production entity. When the types of crops planted in the sub-region are included in the types of product raw materials of the production entity, the second evaluation index is 1; when the types of crops planted in the sub-region are not included in the types of product raw materials of the production entity, the second evaluation index is 0; The first-level first-category data is classified into second-level categories according to the second evaluation index, including second-level first-category data and second-level second-category data, wherein the second-level first-category data is a data group corresponding to the sub-region whose planted crop types are included in the product raw material type set of the production entity, and the second-level second-category data is a data group corresponding to the sub-region whose planted crop types are not included in the product raw material type set of the production entity; The third evaluation index is the ratio of the yield of the planted crops in the sub-region to the quota of the raw materials of the products corresponding to the planted crops required by the production entity to be allocated to the planted crops in the sub-region. When the third evaluation index is less than 1, it is judged that the yield of the planted crops in the sub-region is insufficient. When the third evaluation index is equal to 1, it is judged that the yield of the planted crops in the sub-region is qualified. When the third evaluation index is greater than 1, it is judged that the yield of the planted crops in the sub-region is excessive. The second-level first-category data are classified into three levels according to the third evaluation index, including third-level first-category data, third-level second-category data and third-level third-category data, wherein the third-level first-category data is a data group corresponding to the sub-region with insufficient crop yield, the third-level second-category data is a data group corresponding to the sub-region with qualified crop yield, and the third-level third-category data is a data group corresponding to the sub-region with excessive crop yield; The preset comprehensive suitability evaluation model is that the suitability evaluation value of the crops planted in the sub-region of the farmland area to be monitored is equal to the sum of the first evaluation index, the second evaluation index and the third evaluation index, a standard evaluation value is set, and a maximum evaluation value is set. When the evaluation value is less than the standard evaluation value or greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-region is low, and the sub-region is determined to be the farmland area to be optimized; When the first evaluation index is greater than 1, it is determined that the crops planted in the sub-region corresponding to the first-level second-category data are not suitable for evaluation; When the first evaluation index is less than or equal to 1, it is determined that the data contained in the first-level and first-category data is further suitable for evaluation, including: When the first evaluation index is less than or equal to 1 and the second evaluation index is 0, the suitability evaluation value of the crops planted in the sub-region corresponding to the second-level second-category data is calculated as the first evaluation index, and the evaluation value is less than the standard evaluation value, it is judged that the comprehensive suitability of the crops planted in the sub-region corresponding to the second-level second-category data is low, and the sub-region corresponding to the second-level second-category data is determined to be a first-level farmland area to be optimized; When the first evaluation index is less than or equal to 1 and the second evaluation index is 1, it is determined that the data included in the second-level first-category data is further suitable for evaluation, including: When the first evaluation index is less than or equal to 1, the second evaluation index is 1, and the third evaluation index is less than 1, the suitability evaluation value of the crops planted in the sub-region corresponding to the third-level first-category data is calculated as the sum of the first evaluation index, 1, and the third evaluation index, and the evaluation value is less than the standard evaluation value, and it is judged that the comprehensive suitability of the crops planted in the sub-region corresponding to the third-level first-category data is low, and the sub-region corresponding to the third-level first-category data is determined to be a second-level first-category farmland area to be optimized; When the first evaluation index is less than or equal to 1, the second evaluation index is 1, and the third evaluation index is equal to 1, the suitability evaluation value of the crops in the sub-region corresponding to the three-level two-category data is calculated as the sum of the first evaluation index, 1, and the third evaluation index, and the evaluation value is greater than or equal to the standard evaluation value and less than or equal to the maximum evaluation value, and it is judged that the comprehensive suitability of the crops in the sub-region corresponding to the three-level two-category data is high, and the sub-region corresponding to the three-level two-category data is determined to be a suitable farmland area; When the first evaluation index is less than or equal to 1, the second evaluation index is 1, and the third evaluation index is greater than 1, the suitability evaluation value of the crops in the sub-area corresponding to the three-level three-category data is calculated as the sum of the first evaluation index, 1, and the third evaluation index. If the evaluation value is greater than the maximum evaluation value, it is judged that the comprehensive suitability of the crops in the sub-area corresponding to the three-level three-category data is low, and the sub-area corresponding to the three-level three-category data is determined to be a second-level second-category farmland area to be optimized.

2. The method for optimizing farmland area planting structure based on multi-source data according to claim 1, characterized in that: According to the soil moisture level of the sub-area, the types of crops suitable for growth are selected from the product raw material types of the production entity to optimize the first-level farmland area to be optimized, wherein the soil moisture level of the sub-area is positively correlated with the precipitation state index of the area where the farmland area to be optimized is located, and the precipitation state index is calculated based on the historical precipitation data of the area where the farmland area to be monitored is located; The product raw material type set of the production entity includes several types of crops, each type of crop corresponds to a different soil moisture level suitable for growth, the soil moisture levels include primary soil moisture, secondary soil moisture, and tertiary soil moisture, wherein each of the soil moisture levels corresponds to a subset of crop types, each of the subset of crop types includes one or more crop types, the multiple crop types are sorted from high to low in the crop type subset according to the degree to which the crop types are suitable for growth at the soil moisture level, and the crop types in the crop type subset are the crop types to be selected in the primary farmland area to be optimized.

3. The method for optimizing farmland area planting structure based on multi-source data according to claim 2, characterized in that: The farmland area to be optimized is secondarily optimized according to the supply-demand relationship between the yield of the planted crop types in the sub-area radiated by the production entity and the demand for the planted crop types required by the production entity. The farmland area to be optimized includes a first-level farmland area to be optimized, a second-level first-class farmland area to be optimized, and a second-level second-class farmland area to be optimized. The supply-demand relationship includes insufficient supply and excessive supply.

4. The method for optimizing farmland area planting structure based on multi-source data according to claim 3, characterized in that: According to a first difference between the yield of the planted crops in the sub-region corresponding to the second-level first-category farmland area to be optimized and the quota quantity of the product raw materials corresponding to the planted crop type required by the production entity to be allocated to the planted crops in the sub-region, it is determined that the planted crop yield is in short supply, and then the planted crop types with insufficient planted crop yield are compared with the planted crop types to be selected in the first-level farmland area to be optimized in the planted crop type subset; when the planted crop types to be selected include the planted crop types with insufficient planted crop yield, the planted crop types with insufficient planted crop yield are selected to perform secondary optimization on the first-level farmland area to be optimized; The increased planting area of ​​the crop types selected within the crop type subset is determined based on the first difference and the standard per-acre yield of the selected crop types, where the increased planting area = first difference / standard per-acre yield, wherein the first difference = quota quantity - yield.

5. The method for optimizing farmland area planting structure based on multi-source data according to claim 4, characterized in that: According to the second difference between the crop yield of the sub-area corresponding to the second-level second-category farmland area to be optimized and the quota quantity of product raw materials corresponding to the crop type required by the production entity to be allocated to the crops in the sub-area, it is determined that the crop yield is in oversupply. Then, according to the second difference and the standard per-acre yield of the crop type, the reduced planting area of ​​the crops in the second-level second-category farmland area to be optimized is determined, and the reduced planting area = second difference / standard per-acre yield, wherein the second difference = yield - quota quantity.

6. The method for optimizing farmland area planting structure based on multi-source data according to claim 5, characterized in that: Comparing the optimization result with the target optimization result and judging whether the optimization result is qualified to obtain a judgment result, and adjusting the evaluation index according to the judgment result to obtain an adjusted evaluation value, wherein the optimization result is an actual third evaluation index, and the target optimization result is 1; The actual output of the sub-area after the secondary optimization is obtained and the ratio thereof to the quota quantity is obtained to obtain an actual third evaluation index; when the actual third evaluation index is not equal to 1, it is determined that the actual output of the sub-area after the secondary optimization is unqualified; the first evaluation index is adjusted to obtain an adjusted first evaluation index and the third evaluation index is adjusted to obtain an adjusted third evaluation index; and the sum of the adjusted first evaluation index, the second evaluation index and the adjusted third evaluation index is calculated to obtain an adjusted evaluation value; When the actual third evaluation index is less than 1, it is judged that the actual yield of the sub-region after the secondary optimization is insufficient, and the first adjustment coefficient is reduced to expand the standard distance of the production entity radiation, and the second adjustment coefficient is increased to increase the planting area for a second time; When the actual third evaluation index is greater than 1, it is judged that the actual yield of the sub-area after the secondary optimization is excessive, and the first adjustment coefficient is increased to reduce the standard distance of the production entity's radiation, and the second adjustment coefficient is reduced to reduce the planting area for the second time.

7. The method for optimizing farmland area planting structure based on multi-source data according to claim 6, characterized in that: The optimized planting area of ​​the sub-region to be optimized is obtained by increasing the planting area of ​​the second-level type one farmland area to be optimized and reducing the planting area of ​​the second-level type two farmland area to be optimized. When the ratio of the optimized planting area to the area of ​​the sub-region is less than 1, the difference between the optimized planting area of ​​the sub-region and the area of ​​the sub-region is calculated to obtain the optimized idle planting area of ​​the sub-region and the soil moisture level of the sub-region and the idle planting area are fed back to the adjacent production entities.

8. A system applied to the method for optimizing farmland area planting structure based on multi-source data as described in any one of claims 1 to 7, characterized in that: include: An acquisition module is used to acquire the farmland area data to be monitored, the production subject data and the historical precipitation data of the area where the farmland area to be monitored is located; An evaluation module, connected to the acquisition module, comprising a processing unit, a first calculation unit and a judgment unit, for classifying the farmland area data to be monitored step by step according to the evaluation index in the preset comprehensive suitability evaluation model and calculating the suitability evaluation value of the crops planted in the sub-area of ​​the farmland area to be monitored; when the evaluation value is less than the standard evaluation value or the evaluation value is greater than the maximum evaluation value, it is judged that the suitability of the crops planted in the sub-area is low, and the sub-area is determined to be the farmland area to be optimized; an optimization module, connected to the evaluation module, comprising a first optimization unit and a second optimization unit, for selecting a subset of planted crop species suitable for growth from a set of product raw material species of the production subject according to the soil moisture level of the sub-region and performing a primary optimization on the first-level farmland region to be optimized, and performing a secondary optimization on the farmland region to be optimized according to a supply-demand relationship between the yield of the planted crop species of the sub-region radiated by the production subject and the demand for the planted crop species required by the production subject, and obtaining an optimization result; An adjustment module, connected to the optimization module, for comparing the optimization result with the target optimization result and determining whether the optimization result is qualified, and adjusting the evaluation index according to the determination result to obtain an adjusted evaluation value; The feedback module is connected to the adjustment module and includes a second calculation unit and a feedback unit, which is used to calculate the difference between the optimized planting area of ​​the sub-area and the area of ​​the sub-area to obtain the optimized idle planting area of ​​the sub-area and feed back the soil moisture level of the sub-area and the idle planting area to the adjacent production entity.

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