Multi-system coupled agronomic and water-saving measure space optimization configuration method and system

Abstract

The invention discloses a multi-system coupled agronomic and water-saving measure space optimization configuration method and system, and belongs to the technical field of agricultural production field management. Comprising the following steps: S1, dividing a planting area into M planting sub-areas; s2, constructing a prediction model, wherein the prediction model at least comprises an overall and vertical soil moisture change prediction model and a crop irrigation prediction model; s3, obtaining the temperature and rainfall of each planting sub-region at the future moment; utilizing the integral and vertical soil moisture change prediction model and outputting a prediction result; determining a target planting sub-region based on the prediction result; and calculating the irrigation amount required by the target planting sub-region. According to the invention, the dynamic change of soil moisture in different sub-regions can be predicted more accurately; the irrigation opportunity and the irrigation amount can be accurately judged, and crop growth limitation caused by excessive consumption of water resources or insufficient irrigation is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural production field management, and in particular relates to a multi-system coupled agronomic and water-saving measure spatial optimization configuration method and system. Background Art

[0002] In current agricultural production, reasonable water resource management and efficient agronomic measures are the key to increasing crop yields and ensuring sustainable agricultural development. However, traditional agricultural management methods often lack accurate water forecasting and irrigation decision support, resulting in an increasingly prominent problem of water resource waste and mismatch between crop growth needs. Specifically, existing technologies have the following limitations: Traditional agricultural irrigation mostly relies on empirical judgment or simple meteorological data, which cannot accurately predict the dynamic changes of soil moisture, making it difficult to accurately control the timing and amount of irrigation, which may cause excessive consumption of water resources and affect crop growth due to insufficient irrigation. In addition, the soil types, crop types, and climatic conditions in different regions vary significantly, but the existing irrigation and agronomic management often ignores these spatial differences and adopts unified management measures, making it difficult to achieve optimal allocation and efficient use of water resources. In addition, the existing technology lacks advanced monitoring, prediction, and decision support systems, and cannot obtain key information such as soil moisture and crop growth status in real time, making it difficult to achieve precise regulation and dynamic adjustment of irrigation and agronomic measures. Summary of the invention

[0003] Purpose of the invention: In order to solve the above problems, the present invention provides a method and system for spatial optimization configuration of agronomic and water-saving measures with multi-system coupling.

[0004] Technical solution: A multi-system coupled agronomic and water-saving measures spatial optimization configuration method, including the following steps: step S 1. Get the planting area N Slope data; based on the slope data, the planting area is divided into M Planting sub-areas; step S 2. Obtain historical hydrological data and crop data for each planting sub-area; construct a multi-system coupled prediction model based on the historical hydrological data and crop data, wherein the prediction model includes at least an overall and vertical soil moisture change prediction model and a crop irrigation prediction model; step S 3. Obtain the temperature and rainfall of each planting sub-area at a future time; input the temperature and rainfall at a future time into the overall, vertical soil moisture change prediction model and output the prediction result; determine the target planting sub-area based on the prediction result; calculate the required irrigation amount for the target planting sub-area based on the crop irrigation prediction model.

[0005] In a further embodiment, the step S 3 further comprises the following steps: Get the future time period of each planting sub-area The temperature at any time and rainfall , the temperature and rainfall Input into the overall soil moisture change prediction model to calculate the future time period of each planting sub-area Soil moisture content , and future time periods The soil moisture change rate at any time ; Soil moisture content and soil moisture change rate Conduct analysis and judgment to determine the target planting area; The future time period corresponding to the target planting sub-area The temperature and rainfall at any moment in the vertical soil moisture change prediction model are input to obtain the vertical soil moisture dynamic change result in the target planting sub-area; the soil moisture dynamic change result is judged to obtain a judgment result; based on the judgment result, the crop irrigation prediction model is used to calculate the irrigation amount required for the target planting sub-area.

[0006] In a further embodiment, the overall soil moisture change prediction model is as follows: ; ; ; ; ; In the formula, Is the planting sub-area in the future time period soil moisture content; is the soil moisture content at the initial moment; The future time period Middle i a moment; n The future time period The total number of moments; is the time step; It's time The amount of rainfall at that time; is the area of ​​the planted sub-region; is the amount of irrigation water per unit area; It's time When the temperature is Water loss due to soil evaporation and plant transpiration; is the soil moisture diffusion; is the soil volume of the planted subarea; is the basal transpiration; is the coefficient; is the reference temperature.

[0007] In a further embodiment, the soil moisture content and soil moisture change rate Analyze and determine the target planting area, including the following steps: Compare the predicted soil moisture content with the threshold range and get the comparison result: ; In the formula, the comparison result It means that the planting area is in a state of water balance. Indicates that the planting area is in an over-water state. Compare the results Indicates that the planting sub-area is in a state of water shortage; When the planting sub-area is in a water balance state, the soil moisture change rate of the planting sub-area is determined. Whether it exceeds the change threshold: if so, define the planting sub-area as the target planting sub-area; otherwise, keep the status quo; Or, when the planting sub-area is in an over-water state, a drainage warning is issued for the planting sub-area; Or, when the planting sub-area is in a state of water shortage, irrigation measures are taken for the planting sub-area.

[0008] In a further embodiment, the vertical soil moisture change prediction model is as follows: ; ; In the formula, is the soil moisture content Anytime The rate of change of It's time Soil moisture content at 100 ℃; The moisture along the slope The diffusion term of The temperature is Soil permeability coefficient at ; is the sine of the slope inclination angle of the planting sub-area; is the reference temperature Soil permeability coefficient under ; is the coefficient; is the reference temperature.

[0009] In a further embodiment, the crop irrigation prediction model is: ; In the formula, It's time Irrigation requirements; Is the crop at the moment Water content required at the time of It is the effective depth of the soil layer where the crop roots are located.

[0010] In a further embodiment, the step S 1 further comprises the following steps: Measure and record the planting area N Slope data; analyzing and identifying slope changes within the planting area based on the slope data; Based on the slope changes, the planting area is divided into M A planting sub-area is defined, and the planting sub-area is marked.

[0011] In a further embodiment, the step S The historical hydrological data described in 2 at least include historical rainfall and historical temperature data; the crop data at least include crop types and crop growth cycles.

[0012] In another technical solution, a multi-system coupled agronomy and water-saving measures spatial optimization configuration system is provided, which is used to implement the multi-system coupled agronomy and water-saving measures spatial optimization configuration method as described above, and the system includes: The first module is configured to obtain the planting area N Slope data; based on the slope data, the planting area is divided into M Planting sub-areas; The second module is configured to obtain historical hydrological data and crop data of each planting sub-area; construct a multi-system coupling prediction model based on the historical hydrological data and crop data, wherein the prediction model at least includes an overall and vertical soil moisture change prediction model and a crop irrigation prediction model; The third module is configured to obtain the temperature and rainfall of each planting sub-area at a future moment; input the temperature and rainfall at a future moment into the overall, vertical soil moisture change prediction model and output the prediction result; determine the target planting sub-area based on the prediction result; and calculate the required irrigation amount for the target planting sub-area based on the crop irrigation prediction model.

[0013] Beneficial effects: The present invention divides the planting sub-areas by using the slope, and combines the overall and vertical soil moisture change prediction model to more accurately predict the dynamic changes of soil moisture in different sub-areas; this helps to accurately judge the timing and amount of irrigation, and avoid excessive consumption of water resources or restricted crop growth due to insufficient irrigation.

[0014] The present invention can not only accurately guide irrigation operations and reduce water waste according to the real-time changes in soil moisture and crop growth needs, but also ensure that different regions receive the most suitable moisture and agronomic management, thereby improving overall agricultural production efficiency and water resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] Example 1 like Figure 1 As shown, this embodiment provides a multi-system coupled agronomic and water-saving measures spatial optimization configuration method, comprising the following steps: step S 1. Get the planting area N Slope data; based on the slope data, the planting area is divided into M A planting sub-area.

[0017] Divide the planting area into: Measure and record the planting area N Slope data; analyzing and identifying the slope change in the planting area based on the slope data; dividing the planting area into M A planting sub-area is defined, and the planting sub-area is marked.

[0018] Use high-precision surveying equipment (such as GPS , total station, etc.) to conduct detailed slope measurements in the planting area and obtain a large number of slope data points. Statistical analysis is performed on the collected slope data, including calculation of key indicators such as average slope, maximum slope, minimum slope, slope distribution range, etc., to obtain the slope changes in the planting area. The planting sub-areas are divided according to the slope changes. For example, the first type of planting sub-area has a slope of 0°~3°, which is very gentle, and the soil moisture distribution is relatively uniform and not easy to lose; the second type of planting sub-area has a slope of 3°~6°, and the slope begins to increase, but it is still relatively gentle; the third type of planting area has a slope of 6°~9°, and the slope increases further, and the risk of soil moisture loss increases; and so on. As the slope increases, the moisture changes in the corresponding planting sub-areas become larger, and more attention is needed.

[0019] step S2. Obtain historical hydrological data and crop data for each planting sub-area; construct a multi-system coupled prediction model based on the historical hydrological data and crop data, wherein the prediction model includes at least an overall, vertical soil moisture change prediction model and a crop irrigation prediction model.

[0020] The above historical hydrological data at least include historical rainfall and historical temperature data, which are obtained from authoritative institutions such as the National Climate Center or local meteorological bureaus. Crop data at least include crop types and crop growth cycles, which are obtained by consulting official websites and academic literature, as well as by communicating with local agricultural technicians, farmers, etc. In addition, it is also necessary to obtain historical irrigation volume and soil moisture data of the planting area measured by sensors. The above data are updated in the database, and the above prediction model is constructed by computer fitting analysis and processing data.

[0021] step S 3. Obtain the temperature and rainfall of each planting sub-area at a future time; input the temperature and rainfall at a future time into the overall, vertical soil moisture change prediction model and output the prediction result; determine the target planting sub-area based on the prediction result; calculate the required irrigation amount for the target planting sub-area based on the crop irrigation prediction model.

[0022] Obtain forecasts of weather conditions (such as temperature and rainfall) for a period of time in the future from the Meteorological Bureau, and use forecast models to accurately estimate the dynamic changes in soil moisture; this helps to avoid irrigation when it is not needed, thereby saving water resources and reducing problems such as soil salinization and nutrient loss that may be caused by excessive irrigation; it can also ensure that irrigation is carried out in a timely manner when soil moisture is insufficient to meet the growth needs of crops.

[0023] In addition, extreme weather events (such as droughts and floods) have a huge impact on agricultural production. By predicting changes in soil moisture in advance, timely measures can be taken to deal with possible disasters. For example, increase irrigation before a drought or make drainage preparations in advance when heavy rainfall is expected to reduce the impact of disasters on crop growth. This irrigation management method based on weather forecasts and prediction models helps to achieve precision and intelligence in agricultural production. By rationally utilizing water resources and optimizing the crop growth environment, the sustainability of agricultural production can be improved and the impact on the environment can be reduced.

[0024] Among them, the steps S 3 further comprising the steps S 3-1 and steps S 3-2: step S 3-1. Obtain the future time period of each planting sub-area The temperature at any time and rainfall , the temperature and rainfall Input into the overall soil moisture change prediction model to calculate the future time period of each planting sub-area Soil moisture content , and future time periods The soil moisture change rate at any time ; Soil moisture content and soil moisture change rate Conduct analysis and judgment to determine the target planting area.

[0025] The above future time period Set to 2~5 days, the interval between adjacent moments in the future time period is 1 hour.

[0026] Furthermore, the above-mentioned overall soil moisture change prediction model is as follows: ; ; ; ; ; In the formula, Is the planting sub-area in the future time period soil moisture content; is the soil moisture content at the initial moment (the initial moment refers to a moment before the start of the future time period); It is the future time period Middle i a moment; n The future time period The total number of moments; is the time step; It's time The amount of rainfall at that time; is the area of ​​the planted sub-region; is the amount of irrigation water per unit area; It's time When the temperature is Water loss due to soil evaporation and plant transpiration; is the soil moisture diffusion; is the soil volume of the planted subarea; is the basal transpiration; is the coefficient; is a reference temperature, such as 20℃ or 25℃.

[0027] Furthermore, the soil moisture content and soil moisture change rate Analyze and determine the target planting area, including the following steps: Compare the predicted soil moisture content with the threshold range and get the comparison result: ; In the formula, the comparison result It means that the planting area is in a state of water balance. Indicates that the planting area is in an over-water state. Compare the results Indicates that the planting sub-area is in a state of water shortage; When the planting sub-area is in a water balance state, the soil moisture change rate of the planting sub-area is determined. Whether it exceeds the change threshold: if so, define the planting sub-area as the target planting sub-area; otherwise, keep the status quo; Or, when the planting sub-area is in an over-water state, a drainage warning is issued for the planting sub-area; Or, when the planting sub-area is in a state of water shortage, irrigation measures are taken for the planting sub-area.

[0028] Set appropriate soil moisture content threshold range according to crop type and growth stage For example, for most crops, the soil moisture threshold range is set at 60% to 80% of field capacity. The numerical value of is divided into three cases for example discussion: (1) If the predicted soil moisture content When the field water holding capacity reaches 90%, a drainage warning needs to be issued and drainage measures need to be taken for the planting area.

[0029] (2) If the predicted soil moisture content When the field water holding capacity reaches 35%, a drought warning needs to be issued and irrigation measures need to be taken for the planting sub-area. The crop irrigation prediction model is used to calculate the amount of irrigation required for the planting sub-area.

[0030] (3) Predicted soil moisture content If it is 70% of the field water holding capacity, the soil moisture content in the planting area is within the appropriate range.

[0031] When using the overall soil moisture change prediction model to predict the soil moisture content of the planting sub-area in the future cycle, the soil moisture status of the entire planting area can be quickly understood based on the prediction results, and whether the current soil moisture content is within the appropriate range can be quickly determined, so that appropriate irrigation or drainage measures can be taken in time; this global assessment method greatly improves management efficiency and reduces the possibility of ignoring overall risks due to local problems. In addition, global prediction is particularly important under extreme weather conditions such as drought and floods. When the predicted soil moisture content is too low or too high, the global prediction can quickly identify these abnormal conditions and trigger the corresponding early warning mechanism. This helps agricultural managers take timely countermeasures, such as increasing irrigation or drainage operations, thereby effectively reducing the impact of extreme weather on crop growth.

[0032] Since global predictions are often based on the average value of overall data, this may lead to neglect of changes in soil moisture content in some scenarios. In particular, when there are natural terrain differences such as slope in the planting area, changes in soil moisture content may be more significant, and global predictions are difficult to capture these subtle differences.

[0033] In order to make up for the shortcomings of global prediction, the steps are proposed S 3-2: step S 3-2. The future time period corresponding to the target planting sub-area The temperature and rainfall at any moment in the vertical soil moisture change prediction model are input to obtain the vertical soil moisture dynamic change result in the target planting sub-area; the soil moisture dynamic change result is judged to obtain a judgment result; based on the judgment result, the crop irrigation prediction model is used to calculate the irrigation amount required for the target planting sub-area.

[0034] The vertical soil moisture change prediction model is used to further predict the soil moisture changes in the target planting sub-area to obtain the vertical soil moisture dynamic change value; the vertical soil moisture dynamic change value is compared with the corresponding change threshold. If it is greater than the change threshold, it means that the change in the target planting sub-area is abnormal, and an early warning is required, and the irrigation amount required for the target sub-planting is calculated; otherwise, no early warning is needed.

[0035] The threshold is based on a future time period Specific settings, such as future time periods The change threshold is set to 10% for 3 days. When the dynamic change value of vertical soil moisture is greater than 10%, it means that the target planting sub-area has changed abnormally, and an early warning is required, and the irrigation amount required for the target planting is calculated; when the dynamic change value of vertical soil moisture is ≤10%, it means that the change of the target planting sub-area is within the normal range.

[0036] Furthermore, the vertical soil moisture change prediction model is as follows: ; ; In the formula, is the soil moisture content Anytime The rate of change of It's time Soil moisture content at 100 ℃; The moisture along the slope The diffusion term of The temperature is Soil permeability coefficient at ; is the sine of the slope inclination angle of the planting sub-area; is the reference temperature Soil permeability coefficient under ; is the coefficient; is a reference temperature, such as 20℃ or 25℃.

[0037] The vertical soil moisture change prediction model can make more detailed predictions for the target planting sub-area and capture subtle changes in soil moisture in the vertical direction, thereby improving the accuracy and reliability of the prediction. By real-time monitoring and analyzing the dynamic changes in vertical soil moisture, abnormal changes in soil moisture in the target planting sub-area, such as excessive dryness or wetness in local areas, can be discovered in a timely manner, providing strong support for timely irrigation or drainage measures. Comparing the dynamic changes in vertical soil moisture with the corresponding change thresholds can more scientifically determine whether an early warning is needed; this method avoids false alarms or missed alarms that may be caused by ignoring local changes in global predictions, and improves the accuracy and practicality of the early warning system.

[0038] The calculation of the required irrigation amount in the above text is calculated using a crop irrigation prediction model, wherein the crop irrigation prediction model is: ; In the formula, It's time Irrigation requirements; Is the crop at the moment Water content required at the time of It is the effective depth of the soil layer where the crop roots are located.

[0039] The crop irrigation prediction model comprehensively considers the impact of factors such as temperature and slope on soil moisture dynamics. The model can more accurately predict the water requirements of crops under different conditions, thereby formulating a more reasonable irrigation plan. This helps avoid over-irrigation or under-irrigation and improves the efficiency of irrigation water use.

[0040] Example 2 This embodiment provides a multi-system coupled agronomy and water-saving measures spatial optimization configuration system, which is used to implement a multi-system coupled agronomy and water-saving measures spatial optimization configuration method as described in Example 1, and the system includes: The first module is configured to obtain the planting area N Slope data; based on the slope data, the planting area is divided into M Planting sub-areas; The second module is configured to obtain historical hydrological data and crop data of each planting sub-area; construct a multi-system coupling prediction model based on the historical hydrological data and crop data, wherein the prediction model at least includes an overall and vertical soil moisture change prediction model and a crop irrigation prediction model; The third module is configured to obtain the temperature and rainfall of each planting sub-area at a future moment; input the temperature and rainfall at a future moment into the overall, vertical soil moisture change prediction model and output the prediction result; determine the target planting sub-area based on the prediction result; and calculate the required irrigation amount for the target planting sub-area based on the crop irrigation prediction model.

Claims

1. A multi-system coupled agronomic and water-saving measures spatial optimization configuration method, characterized in that: The following steps are involved: step S 1. Get the planting area N Slope data; Based on the slope data, the planting area is divided into M Planting sub-areas; step S 2. Obtain historical hydrological data and crop data for each planting sub-area; construct a multi-system coupled prediction model based on the historical hydrological data and crop data, wherein the prediction model includes at least an overall and vertical soil moisture change prediction model and a crop irrigation prediction model; step S 3. Obtain the temperature and rainfall of each planting sub-area at a future time; input the temperature and rainfall at a future time into the overall, vertical soil moisture change prediction model and output the prediction result; determine the target planting sub-area based on the prediction result; calculate the required irrigation amount for the target planting sub-area based on the crop irrigation prediction model.

2. A multi-system coupled agronomic and water-saving measures spatial optimization configuration method as claimed in claim 1, characterized in that: step S 3 further comprises the following steps: Get the future time period of each planting sub-area The temperature at any time and rainfall , the temperature and rainfall Input into the overall soil moisture change prediction model to calculate the future time period of each planting sub-area Soil moisture content , and future time periods The soil moisture change rate at any time ; Soil moisture content and soil moisture change rate Conduct analysis and judgment to determine the target planting area; The future time period corresponding to the target planting sub-area The temperature and rainfall at any moment in the vertical soil moisture change prediction model are input to obtain the vertical soil moisture dynamic change result in the target planting sub-area; the soil moisture dynamic change result is judged to obtain a judgment result; based on the judgment result, the crop irrigation prediction model is used to calculate the irrigation amount required for the target planting sub-area.

3. A multi-system coupled agronomic and water-saving measures spatial optimization configuration method as claimed in claim 2, characterized in that: The overall soil moisture change prediction model is as follows: ； ； ； ； ； In the formula, Is the planting sub-area in the future time period soil moisture content; is the soil moisture content at the initial moment; It is the future time period Middle i a moment; n The future time period The total number of moments; is the time step; It's time The amount of rainfall at that time; is the area of ​​the planted sub-region; is the amount of irrigation water per unit area; It's time When the temperature is Water loss due to soil evaporation and plant transpiration; is the soil moisture diffusion; is the soil volume of the planted subarea; is the basal transpiration; is the coefficient; is the reference temperature.

4. A multi-system coupled agronomic and water-saving measures spatial optimization configuration method as claimed in claim 3, characterized in that: Soil moisture content and soil moisture change rate Analyze and determine the target planting area, including the following steps: Compare the predicted soil moisture content with the threshold range and get the comparison result: ； In the formula, the comparison result It means that the planting area is in a state of water balance. Indicates that the planting area is in an over-water state. Compare the results Indicates that the planting sub-area is in a state of water shortage; When the planting sub-area is in a water balance state, the soil moisture change rate of the planting sub-area is determined. Whether it exceeds the change threshold: if so, define the planting sub-area as the target planting sub-area; otherwise, keep the status quo; Or, when the planting sub-area is in an over-water state, a waterlogging warning is issued for the planting sub-area; Or, when the planting sub-area is in a state of water shortage, irrigation measures are taken for the planting sub-area.

5. The method for spatial optimization configuration of agronomic and water-saving measures with multi-system coupling as claimed in claim 2, characterized in that: The vertical soil moisture change prediction model is as follows: ； ； In the formula, is the soil moisture content Anytime The rate of change of It's time Soil moisture content at 100 ℃; The moisture along the slope The diffusion term of The temperature is Soil permeability coefficient at ; is the sine of the slope inclination angle of the planting sub-area; is the reference temperature Soil permeability coefficient under ; is the coefficient; is the reference temperature.

6. A multi-system coupled agronomic and water-saving measures spatial optimization configuration method as claimed in claim 2, characterized in that: The crop irrigation prediction model is: ； In the formula, It's time Irrigation requirements; Is the crop at the moment Water content required at the time of It is the effective depth of the soil layer where the crop roots are located.

7. The method for spatial optimization configuration of agronomic and water-saving measures with multi-system coupling as claimed in claim 1, characterized in that: step S 1 further comprises the following steps: Measure and record the planting area N Slope data; analyzing and identifying slope changes within the planting area based on the slope data; Based on the slope changes, the planting area is divided into M A planting sub-area is defined, and the planting sub-area is marked.

8. The method for spatial optimization configuration of agronomic and water-saving measures with multi-system coupling as claimed in claim 1, characterized in that: step S The historical hydrological data described in 2 at least include historical rainfall and historical temperature data; the crop data at least include crop types and crop growth cycles.

9. A multi-system coupled agronomy and water-saving measures spatial optimization configuration system, used to implement a multi-system coupled agronomy and water-saving measures spatial optimization configuration method as claimed in any one of claims 1 to 8, characterized in that: The system comprises: The first module is configured to obtain the planting area N Slope data; based on the slope data, the planting area is divided into M Planting sub-areas; The second module is configured to obtain historical hydrological data and crop data of each planting sub-area; construct a multi-system coupling prediction model based on the historical hydrological data and crop data, wherein the prediction model at least includes an overall and vertical soil moisture change prediction model and a crop irrigation prediction model; The third module is configured to obtain the temperature and rainfall of each planting sub-area at a future moment; input the temperature and rainfall at a future moment into the overall, vertical soil moisture change prediction model and output the prediction result; determine the target planting sub-area based on the prediction result; and calculate the required irrigation amount for the target planting sub-area based on the crop irrigation prediction model.

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