Grain planting standard period optimization method

Abstract

The invention relates to a grain planting standard period optimization method, which comprises the following steps: acquiring soil nutrient distribution data and crop growth monitoring data, and constructing a regional nutrient supply distribution map; according to the regional nutrient supply distribution map, the soil nutrient distribution data and the crop growth monitoring data, the grouting rate of the nutrient supply imbalance region is predicted; according to the filling rate, the soil moisture data and the climate data, calculating a grain dry matter mass accumulation process and a moisture content value change track of each region; identifying a delayed harvesting area with insufficient dry matter mass accumulation or overproof moisture content value in the standard harvesting window period; calculating the similarity between the mature process deviation degree of each region and the historical same-period condition; determining a harvest priority sequence of each region according to the maturity prediction correction coefficient; according to the prediction result of the grain water content value of each region, a differential drying pretreatment standard of each region is formulated. Intelligent management of the whole crop harvesting process is achieved, and the harvesting efficiency and the grain quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of grain planting, and in particular to a method for optimizing a grain planting standard cycle. Background Art

[0002] During the grain planting process, accurately assessing crop maturity and formulating scientific harvest scheduling plans are of decisive significance for ensuring grain quality, reducing losses, and improving storage efficiency.

[0003] Current crop maturity assessment methods rely primarily on traditional empirical judgment and single-indicator monitoring, lacking the ability to comprehensively analyze the multidimensional internal and external characteristics of grains. Existing harvest scheduling systems often employ fixed time windows and uniform standards, making it difficult to cope with the complex and changing environmental conditions and management differences in actual production, resulting in a lack of targeted and flexible harvesting decisions. In actual production, standard planting areas often experience spatial heterogeneity in the maturity process due to environmental changes or deviations from expected management practices. Regional nutrient imbalances directly impact the grain filling process, causing the filling rate to deviate from the normal developmental trajectory, leading to significant differences in grain development progress between regions. This developmental heterogeneity further results in insufficient dry matter accumulation and excessively high moisture content in grains from some areas within the pre-set standard harvest window, failing to meet established storage quality requirements. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, the present invention aims to provide a method for optimizing the standard crop planting cycle. This method enables intelligent management of the entire crop harvest process, improves harvest efficiency and grain quality, and provides scientific decision-making support for agricultural production.

[0005] The method for optimizing the standard period of grain planting according to the present invention comprises the following steps: S1. Acquire soil nutrient distribution data and crop growth monitoring data, construct a regional nutrient supply distribution map, and identify areas of nutrient supply imbalance in the regional nutrient supply distribution map based on changes in soil nutrient concentration; S2. Calculating a filling rate parameter for the nutrient supply imbalance region based on the regional nutrient supply distribution map, the soil nutrient distribution data, and the crop growth monitoring data, and predicting the filling rate for the nutrient supply imbalance region in combination with real-time meteorological data; S3. Calculating the accumulation process of grain dry matter and the change trajectory of moisture content in each region based on the grain filling rate, soil moisture data, and climate data, and predicting the grain maturity compliance in each region within the standard harvest window; S4. Identify delayed harvest areas where dry matter accumulation is insufficient or moisture content exceeds the standard within the standard harvest window, and calculate the additional maturation time required to meet storage standards; S5. Based on the historical evolution data, combined with the real-time meteorological data and the crop growth monitoring data, calculate the similarity between the degree of deviation of the maturity process in each region and the historical situation during the same period, and determine the maturity prediction correction coefficient; S6. Determine a harvest priority ranking for each region based on the maturity prediction correction coefficient, allocate resources within the planting cycle based on the priority ranking, analyze the filling rate and the real-time meteorological data, and determine a predicted result of the grain moisture content value for each region; S7. Based on the predicted results of the grain moisture content values in each region, formulate differentiated drying pretreatment standards for each region.

[0006] Preferably, the step S1 specifically includes: Obtain soil nitrogen, phosphorus, and potassium content data and crop leaf area index monitoring data, and construct a regional nutrient supply distribution map using the Kriging interpolation method; Determining nutrient concentration distribution values in different regions based on changes in nutrient concentration gradients in the regional nutrient supply distribution map; Comparing the nutrient concentration distribution value with a preset nutrient concentration threshold, and identifying the coordinates of a first region where the nutrient concentration is lower than the preset nutrient concentration threshold; Acquire the crop leaf area index monitoring data at a position corresponding to the first regional coordinates, and construct a data table of corresponding relationships between the first regional coordinates and the crop leaf area index monitoring data; According to the quantitative relationship between the crop leaf area index monitoring data and the nutrient concentration distribution values in the corresponding relationship data table, the first regional coordinates are divided into regional categories with different degrees of deficiency, and the distribution map of the nutrient supply imbalance area and the regional category division results in the regional nutrient supply distribution map are determined.

[0007] Preferably, the step S2 specifically includes: According to the coordinate information of the imbalanced area in the regional nutrient supply distribution map, the soil nitrogen, phosphorus and potassium content data and the crop leaf area index data at the corresponding location are obtained, a quantitative relationship between the degree of nutrient deficiency and the crop physiological indicators is established, and the basic value of the filling rate parameter of each imbalanced area is obtained; According to the basic value of the grouting rate parameter, meteorological monitoring data of real-time temperature, humidity and light intensity corresponding to the imbalance area are obtained, and the meteorological factor value is combined with the basic value of the grouting rate parameter to determine the combined data of the environmental factor parameters affecting the grouting process; A grouting rate prediction model is constructed by combining the environmental factor parameter combination data with pre-established grouting rate training sample data, a parameter configuration of the grouting rate prediction model is obtained, and a real-time environmental factor parameter value of the current imbalance area is obtained; The real-time environmental factor parameter value is input into the grouting rate prediction model to predict the grouting rate of the unbalanced area under the current environmental conditions.

[0008] Preferably, the step S3 specifically includes: Based on the predicted filling rate and soil moisture monitoring data, the average daily precipitation and temperature and humidity climate parameters of each imbalanced area are obtained, and a dynamic relationship between the filling rate and environmental moisture conditions is established. The daily growth value of grain dry matter mass and the change value of moisture content in each area are obtained, and the material accumulation sequence data of continuous time nodes are constructed to determine the accumulation process of grain dry matter mass and the change trajectory of moisture content value in each area; A time series analysis algorithm was used to identify the periodicity and trend characteristics of grain maturity changes, obtain the time series of grain maturity evolution from the filling stage to the mature stage in each region, and obtain the time distribution characteristic parameters of grain maturity. The temporal distribution characteristic parameters of grain maturity were compared and analyzed with the pre-set standard harvest window time range to obtain the distribution results of grain maturity compliance in each region.

[0009] Preferably, the step S4 specifically includes: Comparing the grain maturity of each region with a preset minimum dry matter threshold and a maximum moisture content threshold, identifying the coordinates of regions where the dry matter is lower than the minimum dry matter threshold or the moisture content is higher than the maximum moisture content threshold as delayed harvest regions, and obtaining spatial distribution position information of the delayed harvest regions; Obtaining the current measured value of grain moisture content and dry matter accumulation rate monitoring data for each of the delayed harvest areas, establishing a grain quality status parameter table for the delayed harvest areas, and determining the difference between each of the delayed harvest areas and the storage standard; Based on the difference values in the grain quality status parameter table and pre-established maturity rate reference data, a prediction relationship between the moisture content decrease rate and the dry matter increase rate is established, and the moisture content reduction time and dry matter replenishment time required for the grains in each delayed harvest area to meet the storage standard are obtained, thereby obtaining the maturity process time prediction parameters; According to the moisture content reduction time and dry matter replenishment time values in the maturity process time prediction parameters, the key limiting factors and corresponding time lengths for each delayed harvest area to meet the storage standards are determined, and the additional maturity time calculation results of each delayed harvest area are obtained.

[0010] Preferably, the step S5 specifically includes: Based on the calculation results of the additional maturity time of each delayed harvest area, the pre-established historical maturity evolution data of the same developmental stage in the same area was obtained, and the periodic regular pattern of maturity change in each area was established to obtain the historical maturity benchmark change curve and standard deviation range parameters for the same period; According to the historical maturity benchmark change curve for the same period, real-time temperature, humidity, and precipitation meteorological monitoring data and crop leaf area index growth data of each region are obtained, the deviation distance between the current maturity process and the historical maturity benchmark change curve for the same period is calculated, and a quantitative index of the degree of deviation of the maturity process in each region is determined; By using the maturity process deviation quantitative index and the maturity benchmark change curve of the same period in history, the similarity value between the maturity process of the current year and the situation of the same period in each historical year is calculated, and the similarity assessment results of each region and the situation of the same period in history are obtained; According to the similarity evaluation results and the calculation results of the additional maturity time of each region, the similarity value is used as a weight coefficient and multiplied by the additional maturity time value to obtain the maturity prediction adjustment parameters of each region corrected based on historical laws, and the calculation results of the maturity prediction correction coefficient are obtained.

[0011] Preferably, the step S6 specifically includes: According to the maturity prediction correction coefficient and the maturity time difference data of each area, the area with a smaller correction coefficient is set as a high-priority harvest area, and a harvest priority ranking list of each area and a corresponding priority level division result are obtained; According to the harvest priority sorting list and the priority level classification results, pre-configured machinery and human resource configuration information is obtained, and more harvest resources and time quotas are allocated to high-priority areas through a priority matching allocation method, thereby determining a dynamic resource allocation plan and operation time scheduling plan for each area during the planting cycle; By using the time node information in the operation time scheduling plan, historical change data of the filling rate and real-time temperature, humidity and precipitation meteorological monitoring data of each area in the corresponding time period are obtained, the time dependence relationship between the filling rate and meteorological factors is established, and a prediction trend curve of the grain moisture content value over time is obtained; According to the grain moisture content prediction trend curve and the harvest time node in the operation time arrangement plan, the moisture content prediction value corresponding to the scheduled harvest time is obtained, and the grain moisture content prediction result of each area is obtained.

[0012] Preferably, the step S7 specifically includes: Based on the predicted results of the grain moisture content values in each region and the pre-set safe storage moisture content range standard, the regions with different moisture content ranges were divided into three treatment level areas: high moisture content area, medium moisture content area, and low moisture content area. The moisture content level classification results and the corresponding drying treatment intensity requirements of each area were obtained. Obtain the difference between the target moisture content and the current moisture content corresponding to each grade area, determine the amount of water removal required for the grains in each area to meet the safe storage standard, and obtain differentiated moisture treatment target parameters for the three treatment grade areas; Based on the differentiated moisture treatment target parameters and pre-configured drying equipment performance parameter data, a correspondence between moisture removal amount and drying time and temperature settings is established. Long-term high-temperature drying parameters for high-moisture content areas, medium-time medium-temperature drying parameters for medium-moisture content areas, and short-time low-temperature drying parameters for low-moisture content areas are obtained, thereby determining differentiated drying pretreatment standards for each grade of area. According to the differentiated drying pretreatment standards and the pre-arranged post-processing operation time plan, the drying time, temperature setting and humidity control process parameters are associated with the grain characteristics of the corresponding area to obtain the differentiated drying pretreatment standard formulation results for the grains in each area.

[0013] The method for optimizing the grain planting standard cycle described in the present invention has the following advantages: The present invention proposes a method for optimizing the standard cycle of grain planting. This method constructs a regional nutrient supply map based on soil nutrient distribution, crop growth, and meteorological data, accurately identifying areas of nutrient imbalance and providing data support for targeted intervention. By combining real-time meteorological data to predict grain filling rates, the method dynamically deduces changes in grain dry matter accumulation and moisture content based on soil moisture and climatic factors, enabling refined prediction of maturity within the harvest window. By identifying delayed harvest areas and calculating additional maturity time, the method combines historical data with real-time monitoring to establish a maturity prediction correction mechanism, effectively improving prediction accuracy and adaptability. Harvest priority is determined based on correction coefficients, optimizing resource allocation. Furthermore, the method combines filling rates with meteorological data to predict moisture content, forming a differentiated management strategy for the entire harvest process, from maturity prediction to drying and pretreatment. This method enables intelligent management of the entire crop harvest process, improving harvest efficiency and grain quality, and providing scientific decision-making support for agricultural production. The method can not only increase the grain maturity compliance rate and reduce harvest losses, but also improve planting efficiency through optimized resource allocation. Furthermore, differentiated drying standards can be formulated for different regional characteristics to ensure the simultaneous optimization of grain quality and post-harvest processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a grain planting standard cycle optimization method described in the present invention. DETAILED DESCRIPTION

[0015] like Figure 1 As shown, the method for optimizing the standard period of grain planting according to the present invention comprises the following steps: S1. Obtain soil nutrient distribution data and crop growth monitoring data, construct a regional nutrient supply distribution map, and identify areas of nutrient supply imbalance in the regional nutrient supply distribution map based on changes in soil nutrient concentration; S2. Calculate the filling rate parameters for the nutrient supply imbalance area based on the regional nutrient supply distribution map, soil nutrient distribution data, and crop growth monitoring data. Combined with real-time meteorological data, predict the filling rate in the nutrient supply imbalance area. S3. Calculate the accumulation process of grain dry matter and moisture content in each region based on grain filling rate, soil moisture data, and climate data, and predict the grain maturity level in each region within the standard harvest window. S4. Identify delayed harvest areas where dry matter accumulation is insufficient or moisture content exceeds the standard within the standard harvest window, and calculate the additional maturation time required to meet storage standards; S5. Based on historical evolution data, combined with real-time meteorological data and crop growth monitoring data, calculate the similarity between the degree of deviation of the maturity process in each region and the historical situation during the same period, and determine the maturity prediction correction coefficient; S6. Determine the harvest priority of each region based on the maturity prediction correction coefficient, allocate resources within the planting cycle based on the priority, analyze the filling rate and real-time meteorological data, and determine the predicted results of the grain moisture content value in each region; S7. Based on the predicted results of grain moisture content in each region, formulate differentiated drying pretreatment standards for each region.

[0016] Furthermore, in this embodiment, step S1 specifically includes: Obtain soil nitrogen, phosphorus, and potassium content data and crop leaf area index monitoring data, and construct a regional nutrient supply distribution map using the Kriging interpolation method; According to the nutrient concentration gradient changes in the regional nutrient supply distribution map, determine the nutrient concentration distribution values in different regions; Comparing the nutrient concentration distribution value with a preset nutrient concentration threshold, and identifying the coordinates of a first region where the nutrient concentration is lower than the preset nutrient concentration threshold; Acquire crop leaf area index monitoring data at a position corresponding to the first regional coordinates, and construct a data table of corresponding relationships between the first regional coordinates and the crop leaf area index monitoring data; Based on the quantitative relationship between the crop leaf area index monitoring data and the nutrient concentration distribution values in the corresponding relationship data table, the first regional coordinates are divided into regional categories with different deficiency degrees, and the distribution map of the nutrient supply imbalance area in the regional nutrient supply distribution map and the regional category division results are determined; Here is an example: As a core technology in spatial statistics, Kriging interpolation method predicts the value of unknown location by analyzing the spatial correlation between known sampling points; After obtaining the nitrogen, phosphorus and potassium content data from 50 soil sampling points in the farmland, Kriging interpolation can calculate the nutrient concentration value at any location in the entire area based on the distance decay law and spatial autocorrelation characteristics; Specifically, sampling points that are closer have higher similarity weights, while those that are farther away have gradually lower weights, thus generating a continuous nutrient supply distribution map. The technical effect of this method is that it can convert discrete point data into continuous surface distribution information, providing a complete data foundation for subsequent spatial analysis. Based on the nutrient supply distribution map, the threshold comparison method can accurately identify the location of areas with abnormal nutrient supply. For example, when the available phosphorus content in the soil is lower than the preset threshold of 15 mg / kg, the system automatically marks the coordinates of the area as a phosphorus-deficient area; By traversing the entire distribution map pixel by pixel, the nutrient concentration value of each pixel position is compared with the corresponding threshold value. If any of the following conditions exist: nitrogen content is lower than 80mg / kg, phosphorus content is lower than 15mg / kg, and potassium content is lower than 120mg / kg, the pixel coordinate is identified as a nutrient supply imbalance location. After identifying the coordinates of the area with nutrient imbalance, the crop leaf area index monitoring data at the same coordinate location was extracted to form a three-dimensional data combination of location, nutrients, and growth. For example, the area with coordinates of 118.5 degrees east longitude and 32.2 degrees north latitude was identified as a phosphorus-deficient area. At the same time, the crop leaf area index at this location was 2.1, significantly lower than the 3.5 value in the normal area. When the crop leaf area index drops within 20% relative to normal levels, the corresponding area is classified as mild deficiency; When the decrease is more than 20% but less than 40%, it is classified as moderate deficiency; When the decline exceeds 40%, it is classified as severe deficiency; For example, the corn leaf area index in a certain area dropped from the normal 4.2 to 2.5, a decrease of 40.5%, so it was classified as a severely deficient area. The technical effect of this grading and classification is that it can formulate differentiated treatment strategies according to the severity of the problem, give priority to solving the nutrient supply problem in severely deficient areas, and achieve rational allocation and precise management of resources.

[0017] Furthermore, in this embodiment, step S2 specifically includes: Based on the coordinate information of the imbalanced areas in the regional nutrient supply distribution map, soil nitrogen, phosphorus, and potassium content data and crop leaf area index data at the corresponding locations were obtained to establish a quantitative relationship between the degree of nutrient deficiency and crop physiological indicators, and obtain the basic numerical values of the filling rate parameters for each imbalanced area. Among them, the quantitative relationship between the degree of nutrient deficiency and crop physiological indicators was established through a numerical correlation calculation method. Based on the basic values of the grouting rate parameters, meteorological monitoring data of real-time temperature, humidity, and light intensity corresponding to the imbalanced area are obtained. The meteorological factor values are combined with the basic values of the grouting rate parameters to determine the combined data of environmental factor parameters that affect the grouting process. The meteorological factor values and the basic values of the grouting rate parameters are combined using a data fusion method. By combining environmental factor parameter combination data with pre-established grouting rate training sample data, a grouting rate prediction model is constructed to obtain the parameter configuration of the grouting rate prediction model and the real-time environmental factor parameter values of the current imbalance area. The grouting rate prediction model is constructed using a random forest regression algorithm. Input the real-time environmental factor parameter values into the grouting rate prediction model to predict the grouting rate of the imbalance area under the current environmental conditions; Here is an example: The numerical correlation calculation method achieves accurate calculation of filling rate parameters by establishing a quantitative relationship between nutrient deficiency and crop physiological response. For example, when the soil nitrogen content in the imbalanced area was 60 mg / kg and the phosphorus content was 8 mg / kg, the corresponding crop leaf area index dropped to 1.8. The intensity of physiological stress caused by nutrient deficiency was determined through correlation analysis; Based on the linear relationship between nutrient deficiency and leaf area index, the system calculated that the base value of the filling rate parameter in this area was 0.65 mg / grain / day, a significant decrease from the 1.2 mg / grain / day in normal areas. The technical effect of this quantitative correlation is that it can accurately reflect the specific impact of nutrient stress on the crop filling process. Based on the basic value of the filling rate parameter, the data fusion method integrates meteorological factors and physiological parameters. For example, when the basic value of the filling rate parameter is 0.65 mg / grain / day, the system simultaneously obtains meteorological monitoring data for the area with an average daily temperature of 28°C, a relative humidity of 72%, and a sunshine duration of 8.5 hours. The data fusion process uses a weighted average method to set the temperature factor weight to 0.4, the humidity factor weight to 0.3, and the light factor weight to 0.3. Combined with the basic values of the grouting rate parameters, a multi-dimensional combination of environmental factor parameter data is formed. The technical effect of this fusion process is that it can fully reflect the complex environmental conditions that affect the grouting process and provide complete input features for subsequent predictions. As an ensemble learning method, the random forest regression algorithm constructs multiple decision trees to predict the complex nonlinear relationship of grouting rate; 500 sets of environmental factor parameter combination data collected historically were used as training samples. Each set of samples contained temperature, humidity, light intensity, and the corresponding measured grouting rate values. The random forest algorithm constructs 100 decision trees, each of which is trained using a different feature subset and sample subset, and the bootstrap sampling method is used to enhance the generalization ability of the model; For example, when the input environmental factor parameter combination is temperature 28°C, humidity 72%, and light 8.5 hours, 100 decision trees output prediction results respectively, and finally the parameter configuration of the grouting rate prediction model is obtained by calculating the average value; After obtaining the current real-time environmental factor parameter values of the imbalanced area, the system inputs the data of temperature 29°C, humidity 68%, and light intensity 9.2 hours into the configured grouting rate prediction model; The model reasoning process converts environmental parameters into standardized values through feature vectorization, and then makes judgments and calculations through each decision tree node in turn; For example, the first decision tree makes branch judgments based on the temperature threshold of 30°C, and the second decision tree selects paths based on the humidity threshold of 70%. Finally, the output results of the 100 decision trees are weighted averaged to obtain a filling rate prediction result of 0.58 mg / grain / day. The technical effect of this real-time prediction is that it can dynamically adjust the filling rate expectations according to current environmental conditions, providing a scientific basis for precision agricultural management.

[0018] Furthermore, in this embodiment, step S3 specifically includes: Based on the predicted filling rate and soil moisture monitoring data, the average daily precipitation and temperature and humidity climate parameters of each imbalanced region were obtained. A dynamic relationship between the filling rate and environmental moisture conditions was established. The daily growth value of grain dry matter mass and the change value of moisture content in each region were obtained. The material accumulation sequence data at continuous time nodes were constructed to determine the accumulation process of grain dry matter mass and the change trajectory of moisture content in each region. Among them, the dynamic relationship between the filling rate and environmental moisture conditions was established using numerical calculation methods. A time series analysis algorithm was used to identify the periodicity and trend characteristics of grain maturity changes, obtain the time series of grain maturity evolution from the filling stage to the mature stage in each region, and obtain the time distribution characteristic parameters of grain maturity. The time distribution characteristic parameters of grain maturity were compared with the pre-set standard harvest window time range to obtain the distribution results of grain maturity compliance in each region; Here is an example: Numerical calculation methods achieve accurate quantification of the grain development process by establishing a quantitative relationship between grain filling rate and environmental factors. When the predicted filling rate is 0.58 mg / grain / day, the soil moisture content is 65%, and the average daily precipitation is 3.2 mm, the system calculates the daily increase in grain dry matter mass to be 0.45 mg / grain / day through the water balance equation; Ambient moisture conditions directly affect the efficiency of material transport within the grain. When soil moisture is sufficient, the grain filling rate approaches the predicted value, while the actual dry matter accumulation rate decreases accordingly when water stress intensifies. Simultaneously, changes in moisture content reflect the dehydration process of the grain, gradually decreasing from 85% at the beginning to 14% at maturity. This dynamic relationship provides basic data support for subsequent maturity assessment. Based on the dry matter and moisture content values, a continuous development trajectory curve is constructed using a time accumulation calculation method; By accumulating the daily increase in grain dry matter mass, the material accumulation sequence data from the beginning of grain filling to the maturity stage is formed; When the daily increase in dry matter mass drops from 0.45 mg to below 0.15 mg for seven consecutive days, the system determines that the grain has entered the physiological maturity stage; The accumulation curve exhibits a typical S-shaped growth pattern, with slow growth in the early stages, rapid accumulation in the middle stages, and a flattening trend in the later stages. The moisture content curve, on the other hand, shows a monotonically decreasing trend of continuous decline. The technical benefit of this dual trajectory is that it comprehensively reflects the grain development status from the two dimensions of material accumulation and water loss, providing a reliable basis for accurately judging maturity. By analyzing 30 days of continuous monitoring data, three typical stages of grain dry matter accumulation were identified: rapid growth, stable and declining stages. By extracting long-term trends, seasonal fluctuations, and random disturbance components through trend decomposition methods, it was found that the evolution of grain maturity follows a specific temporal pattern. When dry matter accumulation reaches a plateau and moisture content drops below 20%, the algorithm identifies the maturity indicator as entering a critical transition period. The technical benefit of this time series analysis is that it can predict future maturity based on historical development patterns, avoiding potential misjudgments that could arise from relying on instantaneous measurements at a single point in time. When the maturity time distribution characteristic parameters show that the grains in a certain area will reach the harvest standard on the 95th day, and the preset standard harvest window period is from the 90th to the 105th day, the system determines that the maturity of the area has reached the standard; The comparison process took into account multiple criteria: the moisture content of the grain was reduced to a safe storage moisture level, the dry matter mass reached a maximum value, and the grain hardness met the requirements for mechanical harvesting.

[0019] For example, when the moisture content of the grains stabilizes within the range of 14%±2% and the accumulation of dry matter stops growing and maintains a stable state, the system confirms that the grains have reached the optimal maturity for harvesting. The technical effect of this multi-dimensional matching is that it can comprehensively consider factors such as quality, yield and harvest suitability to ensure that the harvesting operation is completed within the optimal time window, thereby maximizing grain quality and harvesting efficiency.

[0020] Furthermore, in this embodiment, step S4 specifically includes: Based on the grain maturity status of each area, a threshold comparison is performed with a preset minimum threshold for dry matter mass and a maximum threshold for moisture content. The coordinates of the areas where the dry matter mass is lower than the minimum threshold for dry matter mass or the moisture content is higher than the maximum threshold for moisture content are identified and used as delayed harvest areas, thereby obtaining spatial distribution information of the delayed harvest areas. Obtain the current measured grain moisture content and dry matter accumulation rate monitoring data for each delayed harvest area, establish a grain quality status parameter table for the delayed harvest area, and determine the difference between each delayed harvest area and the storage standard; the grain quality status parameter table for the delayed harvest area is established using a data association method; Based on the gap values in the grain quality status parameter table and pre-established maturity rate reference data, a predictive relationship between the rate of moisture content decline and the rate of dry matter increase was established. The time required for moisture content reduction and dry matter replenishment to meet the storage standards for grains in each delayed harvest area was obtained, and the maturity process time prediction parameters were obtained. Based on the moisture content reduction time and dry matter replenishment time values in the maturity process time prediction parameters, the key limiting factors and corresponding time lengths for each delayed harvest area to meet the storage standards are determined, and the additional maturity time calculation results for each delayed harvest area are obtained; Here is an example: The threshold comparison method enables accurate identification of delayed harvest areas by setting clear quality standard boundaries; When the preset minimum threshold of dry matter is 35mg / grain and the maximum threshold of moisture content is 18%, the system checks the grain quality parameters of each area one by one; The dry matter content of grain in one area was only 32mg / grain, below the standard requirement of 35mg / grain. At the same time, the moisture content was as high as 22%, exceeding the safe storage standard of 18%. Therefore, this area was identified as a delayed harvest area. The technical benefit of this dual-standard assessment is that it can simultaneously consider both yield and quality requirements, avoiding overlooking other key quality issues due to the passing of a single indicator, thus ensuring the comprehensiveness and accuracy of the identification results. When the current moisture content in the delayed harvest area is 22% and the target moisture content is 14%, the moisture content difference is 8 percentage points; when the current dry matter content is 32 mg / grain and the target dry matter content is 35 mg / grain, the dry matter content difference is 3 mg / grain; The data association process establishes a quality status parameter table, which maps the current value, target value, and gap value of each indicator to form a structured data organization. The technical effect of this gap quantification is that it can clearly identify the specific degree of improvement required in each area, providing an accurate calculation basis for subsequent time prediction and avoiding time planning based on fuzzy estimates. Using the grain maturity monitoring data from the past three years as training samples, we analyzed the statistical patterns of the average daily rate of decrease in moisture content and the average daily rate of increase in dry matter. When historical data shows an average daily decrease of 0.8% in moisture content and an average daily increase of 0.6 mg / kernel in dry matter, the system calculates that it will take 10 days to reach the moisture content standard based on the current 8% moisture content gap, and 5 days to reach the dry matter standard based on the 3 mg / kernel dry matter gap. This historically based prediction method is technically effective in fully leveraging the guiding value of empirical data, improving the reliability and practicality of time predictions. When it takes 10 days to reduce the moisture content and 5 days to replenish the dry matter, the system determines that the reduction in moisture content is the limiting factor through numerical comparison. Therefore, the additional ripening time in this area is 10 days. This maximum value selection principle is based on the barrel effect, that is, the grains must meet all quality requirements at the same time to meet the storage standards. If any indicator is not up to standard, they cannot be safely stored.

[0021] For example, even if the dry matter mass meets the standard within 5 days, the moisture content still needs an additional 5 days to drop to a safe level. Therefore, it is necessary to wait 10 days to ensure that all grains meet the standard. The technical effect of this limiting factor identification is that it can accurately grasp the key factors that restrict the harvest time, avoid misjudgment of the harvest timing due to ignoring a certain indicator, and ensure the dual protection of grain quality and storage safety.

[0022] Furthermore, in this embodiment, step S5 specifically includes: Based on the calculation results of the additional maturity time for each delayed harvest area, pre-established historical maturity evolution data for the same region and developmental stage were obtained to establish a cyclical pattern of maturity changes in each region, and the historical maturity benchmark change curve and standard deviation range parameters for the same period were obtained. The cyclical pattern of maturity changes in each region was established using time series statistical methods. Based on the historical maturity benchmark change curve for the same period, real-time temperature, humidity, and precipitation meteorological monitoring data and crop leaf area index growth data for each region are obtained. The deviation distance between the current maturity process and the historical maturity benchmark change curve for the same period is calculated, and a quantitative indicator of the degree of deviation of the maturity process in each region is determined. The deviation distance between the current maturity process and the historical maturity benchmark change curve for the same period is calculated using a correlation analysis method. The similarity between the current year's maturity process and the historical maturity benchmark change curve is calculated by comparing the quantitative index of the degree of deviation of the maturity process with the situation in the same period of each historical year. The similarity assessment results of each region and the historical situation in the same period are obtained. The similarity between the current year's maturity process and the situation in the same period of each historical year are calculated using the cosine similarity algorithm. Based on the similarity assessment results and the calculation results of the additional maturity time for each region, the similarity value is used as a weight coefficient and multiplied by the additional maturity time value to obtain the maturity forecast adjustment parameter of each region revised based on historical patterns, and the calculation result of the maturity forecast correction coefficient is obtained; among them, the similarity value is used as a weight coefficient and the additional maturity time value is multiplied by the weighted average calculation method; Here is an example: Time series statistical methods establish reliable benchmark change curves by analyzing the long-term evolution patterns of historical maturity data; After collecting maturity monitoring data for the same period in the past five years in a certain area, the system calculates the average maturity value of the same development stage each year to form a continuous time series; Historical data shows that the average maturity in this area is 65% on the 20th day after grouting, 85% on the 30th day, and 95% on the 40th day. These data points are used to construct a smooth baseline variation curve. Furthermore, the standard deviation range parameter reflects inter-annual fluctuations. For example, a standard deviation of ±3% for maturity on the 30th day indicates that the maturity range in a normal year is between 82% and 88%. The technical benefit of establishing this historical benchmark is that it provides a reliable comparison standard for current years, avoiding the random influence of data from a single year. For example, when the historical benchmark shows that a certain area should reach 75% maturity on the 25th day after grouting, but the current measured value is only 68%, the deviation is 7 percentage points; Simultaneous analysis of current meteorological conditions revealed that the recent average temperature in the region was 2°C lower than the historical level for the same period, and the sunshine hours decreased by 1.5 hours. These unfavorable meteorological factors are the main reason for the delayed ripening process. The correlation analysis also considered crop growth data. When the leaf area index was 15% lower than the historical period, it further confirmed the rationality of delayed maturity. The technical effectiveness of this multi-dimensional correlation analysis lies in its ability to accurately identify the specific extent and potential causes of deviations from the maturity process. Convert the current maturity process and historical annual process data into a multi-dimensional vector, where each dimension represents the maturity value at a specific time point; The maturity process vector for the current year is [65%, 68%, 72%, 76%], while the historical vector for the same period in 2019 is [66%, 69%, 74%, 78%]. The cosine similarity between the two vectors is 0.98, indicating high similarity. When the similarity exceeds the preset threshold of 0.85, the system determines that the current process is comparable to that historical year and can draw on its subsequent development patterns to make forecast corrections. The technical effect of this similarity assessment is that it provides an objective and accurate historical reference basis, avoiding the errors of subjective judgment. When the similarity of a region with 2018 is 0.92 and the similarity with 2020 is 0.87, the system uses these values as weight coefficients respectively; For example, the current additional ripening time in this area is 8 days, while the actual additional time under similar circumstances in 2018 was 6 days and 7 days in 2020. The revised forecast time is 6.4 days after weighted calculation. The calculation process is (0.92×6+0.87×7)÷(0.92+0.87)=6.4. This weighting process ensures that historical years with higher similarity have a greater impact on the forecast results; The correction coefficient is 6.4÷8=0.8, indicating that the original forecast time should be adjusted to 80% based on historical experience. The technical effect of incorporating this historical experience is that it can fully utilize the guiding value of past data and significantly improve the accuracy and practicality of maturity forecasts.

[0023] Furthermore, in this embodiment, step S6 specifically includes: Based on the maturity prediction correction coefficient and the maturity time difference data of each area, the area with a small correction coefficient is set as a high-priority harvest area, and a harvest priority ranking list of each area and the corresponding priority level division results are obtained; among them, the area with a small correction coefficient is set as a high-priority harvest area by adopting a numerical ranking method; Based on the harvest priority list and the priority level classification results, the pre-configured machinery and human resource configuration information is obtained. Through the priority matching allocation method, more harvesting resources and time quotas are allocated to high-priority areas, and the dynamic resource allocation plan and operation time scheduling plan for each area during the planting cycle are determined; By using the time node information in the operation schedule, historical data on changes in filling rate and real-time temperature, humidity, and precipitation meteorological monitoring data for each region during the corresponding time period were obtained. The time-dependent relationship between filling rate and meteorological factors was established, and a predicted trend curve of grain moisture content over time was obtained. The time-dependent relationship between filling rate and meteorological factors was established using a time series analysis algorithm. According to the grain moisture content prediction trend curve and the harvest time nodes in the operation schedule, the moisture content prediction value corresponding to the scheduled harvest time is obtained, and the grain moisture content prediction results of each region are obtained; wherein, the moisture content prediction value corresponding to the scheduled harvest time is obtained by the time node value matching method; Here is an example: The numerical ranking method establishes a scientific harvest priority decision-making mechanism by quantifying the maturity correction coefficient; When the correction coefficient of area A is 0.75, area B is 0.88, and area C is 0.92, the system sorts the values from small to large and determines that area A is the highest priority, area B is the medium priority, and area C is the low priority; The smaller the correction coefficient, the closer the maturity prediction of the area is to the actual situation and the lower the uncertainty of the maturity time, so the harvesting operation should be arranged first; When the correction coefficient falls below a preset threshold of 0.8, the system automatically marks the area as a priority harvest area, ensuring that the best-quality kernels are processed promptly. This objective numerical ranking technology eliminates the subjectivity of human judgment and achieves standardized and precise harvesting decisions. For example, when a farm has three combine harvesters and 15 operators, the high-priority area A is allocated two harvesters and 10 operators, the medium-priority area B is allocated one harvester and five operators, and the low-priority area C is scheduled to operate during resource idle periods; Time quotas are allocated based on urgency. Area A is scheduled to be completed within the first three days of the optimal harvest window, Area B within the middle five days, and Area C within the later time periods. This differentiated allocation ensures that key areas receive sufficient resources at the most appropriate time, maximizing overall harvest efficiency and grain quality. Analysis of the filling rate change data for the past three years revealed a regular relationship: every 1°C increase in temperature corresponds to a 0.05 mg / grain / day increase in filling rate, and every 10% increase in humidity corresponds to a 0.02 mg / grain / day decrease in filling rate. Current real-time meteorological data indicates that the average temperature over the next week will be 2°C higher than the historical average, and the humidity will be 15% lower. The system predicts that the grain filling rate will increase by 0.07mg / grain / day, and thus, the rate of decline in grain moisture content will accelerate. This dynamic prediction technology, based on historical patterns, is effective in identifying the impact of environmental changes on the ripening process in advance, providing a scientific basis for accurate judgment of harvest timing. When the operation time in area A is scheduled for the 95th day and the predicted trend curve shows that the moisture content at that time is 16%, the system determines that this value meets the safe storage standard of 14% to 18%, confirming that the harvest time is appropriate; The matching process takes into account the comprehensive requirements of kernel quality, mechanical operation efficiency, and storage safety. When the predicted moisture content exceeds 20%, the system recommends delaying harvesting, and when it is below 12%, it recommends early harvesting. For example, area B was originally scheduled to be harvested on the 100th day, but the predicted moisture content was only 11%. The system recommended that the operation be carried out in advance on the 97th day to avoid quality loss due to excessive drying. The technical effect of this precise matching is to achieve the best coordination between the harvest timing and the grain status, ensuring that each area can complete the harvest operation under the most suitable moisture content conditions, thereby maximizing the protection of grain quality and storage stability.

[0024] Furthermore, in this embodiment, step S7 specifically includes: Based on the predicted results of the grain moisture content values in each region and the pre-set safe storage moisture content range standard, the regions with different moisture content ranges were divided into three treatment level areas: high moisture content area, medium moisture content area, and low moisture content area. The moisture content level classification results and the corresponding drying treatment intensity requirements of each region were obtained. Among them, the interval classification method was used to divide the regions with different moisture content ranges; Obtain the difference between the target moisture content and the current moisture content corresponding to each grade area, determine the amount of water removal required for the grains in each area to meet the safe storage standard, and obtain differentiated moisture treatment target parameters for the three treatment grade areas; Based on differentiated moisture treatment target parameters and pre-configured drying equipment performance parameter data, a corresponding relationship between moisture removal and drying time and temperature settings is established. Long-term, high-temperature drying parameters are obtained for high-moisture content areas, medium-time, medium-temperature drying parameters are obtained for medium-moisture content areas, and short-time, low-temperature drying parameters are obtained for low-moisture content areas. This determines differentiated drying pretreatment standards for each grade of area. The corresponding relationship between moisture removal and drying time and temperature settings is established using a cluster analysis algorithm. Based on the differentiated drying pretreatment standards and the pre-arranged post-processing operation schedule, the drying time, temperature setting, and humidity control process parameters are all associated with the grain characteristics of the corresponding region, resulting in the formulation of differentiated drying pretreatment standards for grains in each region. The association and configuration of the drying time, temperature setting, and humidity control process parameters with the grain characteristics of the corresponding region are achieved using a parameter matching method. Here is an example: The interval classification method achieves precise stratification management of grain drying by establishing a scientific moisture content grading system; When the safe storage moisture content standard is set at 12% to 14%, the system classifies areas with moisture content prediction results higher than 18% as high moisture content areas, areas within the range of 15% to 18% as medium moisture content areas, and areas below 15% as low moisture content areas. The moisture content of Area A is predicted to be 22%, which exceeds the upper limit of safe storage by 8 percentage points. It is classified as a high moisture area and marked as requiring intensive drying treatment; The moisture content of area B is 16%, which is classified as a medium moisture area and requires moderate drying treatment; Area C has a moisture content of 13%, which is classified as a low-moisture area and requires only light drying treatment. This hierarchical classification avoids the problems of over-drying or under-drying that may result from a uniform treatment mode, ensuring that each area receives the most appropriate treatment intensity. Based on the classification results, the difference calculation method quantifies the moisture removal requirements, providing an accurate data basis for subsequent process parameter setting; For example, the 22% in the high moisture content area needs to be reduced to the target level of 13%, which requires a moisture removal of 9 percentage points. The 16% in the medium moisture content area needs to be reduced to 13%, which requires a moisture removal of 3 percentage points. The 13% in the low moisture content area is close to the target range, which requires a moisture removal of only 1 percentage point. Difference quantification not only considers the absolute numerical difference, but also comprehensively evaluates the dehydration difficulty coefficient at different moisture content levels. The water removal of kernels with high moisture content is relatively easy, while kernels with moisture content close to the target require more precise control. The cluster analysis algorithm establishes differentiated process control standards by mining the inherent correlation between moisture removal and drying parameters; Analyzing historical drying data, it was determined that kernels with moisture removal rates between 6 and 10 percentage points needed high-temperature drying at 65-70°C for 8-12 hours, kernels with moisture removal rates between 3 and 6 percentage points needed medium-temperature drying at 50-55°C for 4-6 hours, and kernels with moisture removal rates between 1 and 3 percentage points needed only low-temperature drying at 35-40°C for 2-3 hours. Cluster analysis also considers factors such as grain variety characteristics and ambient humidity. Drying time is appropriately extended when ambient humidity is high, and drying temperature is reduced when the grains are more sensitive. This data-mining-based process optimization technology ensures that drying parameters are precisely matched to actual needs, maximizing grain quality. The parameter matching method achieves the full implementation of differentiated treatment standards by coordinating the drying process and operation plan.

[0025] For example, when the high moisture content area is scheduled for drying on the 1st or 2nd day after harvest, the system is configured with process parameters of 70°C temperature, 10 hours of processing time and 45% relative humidity; The medium moisture content area is scheduled for treatment on the 3rd to 4th day, with a temperature of 55°C, a duration of 5 hours, and a humidity of 50%. The low moisture content area was arranged to be treated on the 5th to 6th day, with the parameters of 40°C temperature, 3 hours time and 55% humidity.

[0026] The matching process also takes into account the processing capacity and energy efficiency of the drying equipment, giving priority to high-moisture areas with large processing volumes and rationally allocating equipment usage time. The technical effect of this systematic matching is to achieve the best balance between personalized processing and overall efficiency, ensuring that the grains in each area can complete the drying process under the most suitable conditions and achieve unified safety storage standards.

[0027] In the description of the present invention, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of the present invention.

[0028] Those skilled in the art can make various other corresponding changes and deformations based on the technical solutions and concepts described above, and all of these changes and deformations should fall within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing a grain planting standard cycle, characterized in that: The following steps are involved: S1. Acquire soil nutrient distribution data and crop growth monitoring data, construct a regional nutrient supply distribution map, and identify areas of nutrient supply imbalance in the regional nutrient supply distribution map based on changes in soil nutrient concentration; S2. Calculating a filling rate parameter for the nutrient supply imbalance region based on the regional nutrient supply distribution map, the soil nutrient distribution data, and the crop growth monitoring data, and predicting the filling rate for the nutrient supply imbalance region in combination with real-time meteorological data; S3. Calculating the accumulation process of grain dry matter and the change trajectory of moisture content in each region based on the grain filling rate, soil moisture data, and climate data, and predicting the grain maturity compliance in each region within the standard harvest window; S4. Identify delayed harvest areas where dry matter accumulation is insufficient or moisture content exceeds the standard within the standard harvest window, and calculate the additional maturation time required to meet storage standards; S5. Based on the historical evolution data, combined with the real-time meteorological data and the crop growth monitoring data, calculate the similarity between the degree of deviation of the maturity process in each region and the historical situation during the same period, and determine the maturity prediction correction coefficient; S6. Determine a harvest priority ranking for each region based on the maturity prediction correction coefficient, allocate resources within the planting cycle based on the priority ranking, analyze the filling rate and the real-time meteorological data, and determine a predicted result of the grain moisture content value for each region; S7. Based on the predicted results of the grain moisture content values in each region, formulate differentiated drying pretreatment standards for each region.

2. The method for optimizing the grain planting standard cycle according to claim 1, characterized in that: The step S1 specifically includes: Obtain soil nitrogen, phosphorus, and potassium content data and crop leaf area index monitoring data, and construct a regional nutrient supply distribution map using the Kriging interpolation method; Determining nutrient concentration distribution values in different regions based on changes in nutrient concentration gradients in the regional nutrient supply distribution map; Comparing the nutrient concentration distribution value with a preset nutrient concentration threshold, and identifying the coordinates of a first region where the nutrient concentration is lower than the preset nutrient concentration threshold; Acquire the crop leaf area index monitoring data at a position corresponding to the first regional coordinates, and construct a data table of corresponding relationships between the first regional coordinates and the crop leaf area index monitoring data; According to the quantitative relationship between the crop leaf area index monitoring data and the nutrient concentration distribution values in the corresponding relationship data table, the first regional coordinates are divided into regional categories with different degrees of deficiency, and the distribution map of the nutrient supply imbalance area and the regional category division results in the regional nutrient supply distribution map are determined.

3. The method for optimizing the grain planting standard cycle according to claim 1, characterized in that: The step S2 specifically includes: According to the coordinate information of the imbalanced area in the regional nutrient supply distribution map, the soil nitrogen, phosphorus and potassium content data and the crop leaf area index data at the corresponding location are obtained, a quantitative relationship between the degree of nutrient deficiency and the crop physiological indicators is established, and the basic value of the filling rate parameter of each imbalanced area is obtained; According to the basic value of the grouting rate parameter, meteorological monitoring data of real-time temperature, humidity and light intensity corresponding to the imbalance area are obtained, and the meteorological factor value is combined with the basic value of the grouting rate parameter to determine the combined data of the environmental factor parameters affecting the grouting process; A grouting rate prediction model is constructed by combining the environmental factor parameter combination data with pre-established grouting rate training sample data, a parameter configuration of the grouting rate prediction model is obtained, and a real-time environmental factor parameter value of the current imbalance area is obtained; The real-time environmental factor parameter value is input into the grouting rate prediction model to predict the grouting rate of the unbalanced area under the current environmental conditions.

4. The method for optimizing the grain planting standard cycle according to claim 1, characterized in that: The step S3 specifically includes: Based on the predicted filling rate and soil moisture monitoring data, the average daily precipitation and temperature and humidity climate parameters of each imbalanced area are obtained, and a dynamic relationship between the filling rate and environmental moisture conditions is established. The daily growth value of grain dry matter mass and the change value of moisture content in each area are obtained, and the material accumulation sequence data of continuous time nodes are constructed to determine the accumulation process of grain dry matter mass and the change trajectory of moisture content value in each area; A time series analysis algorithm was used to identify the periodicity and trend characteristics of grain maturity changes, obtain the time series of grain maturity evolution from the filling stage to the mature stage in each region, and obtain the time distribution characteristic parameters of grain maturity. The temporal distribution characteristic parameters of grain maturity were compared and analyzed with the pre-set standard harvest window time range to obtain the distribution results of grain maturity compliance in each region.

5. The method for optimizing the grain planting standard cycle according to claim 1, characterized in that: The step S4 specifically includes: Comparing the grain maturity of each region with a preset minimum dry matter threshold and a maximum moisture content threshold, identifying the coordinates of regions where the dry matter is lower than the minimum dry matter threshold or the moisture content is higher than the maximum moisture content threshold as delayed harvest regions, and obtaining spatial distribution position information of the delayed harvest regions; Obtaining the current measured value of grain moisture content and dry matter accumulation rate monitoring data for each of the delayed harvest areas, establishing a grain quality status parameter table for the delayed harvest areas, and determining the difference between each of the delayed harvest areas and the storage standard; Based on the difference values in the grain quality status parameter table and pre-established maturity rate reference data, a prediction relationship between the moisture content decrease rate and the dry matter increase rate is established, and the moisture content reduction time and dry matter replenishment time required for the grains in each delayed harvest area to meet the storage standard are obtained, thereby obtaining the maturity process time prediction parameters; According to the moisture content reduction time and dry matter replenishment time values in the maturity process time prediction parameters, the key limiting factors and corresponding time lengths for each delayed harvest area to meet the storage standards are determined, and the additional maturity time calculation results of each delayed harvest area are obtained.

6. The method for optimizing the grain planting standard cycle according to claim 5, characterized in that: The step S5 specifically includes: Based on the calculation results of the additional maturity time of each delayed harvest area, the pre-established historical maturity evolution data of the same developmental stage in the same area was obtained, and the periodic regular pattern of maturity change in each area was established to obtain the historical maturity benchmark change curve and standard deviation range parameters for the same period; According to the historical maturity benchmark change curve for the same period, real-time temperature, humidity, and precipitation meteorological monitoring data and crop leaf area index growth data of each region are obtained, the deviation distance between the current maturity process and the historical maturity benchmark change curve for the same period is calculated, and a quantitative index of the degree of deviation of the maturity process in each region is determined; By using the maturity process deviation quantitative index and the maturity benchmark change curve of the same period in history, the similarity value between the maturity process of the current year and the situation of the same period in each historical year is calculated, and the similarity assessment results of each region and the situation of the same period in history are obtained; According to the similarity evaluation results and the calculation results of the additional maturity time of each region, the similarity value is used as a weight coefficient and multiplied by the additional maturity time value to obtain the maturity prediction adjustment parameters of each region corrected based on historical laws, and the calculation results of the maturity prediction correction coefficient are obtained.

7. The method for optimizing the grain planting standard cycle according to claim 1, characterized in that: The step S6 specifically includes: According to the maturity prediction correction coefficient and the maturity time difference data of each area, the area with a smaller correction coefficient is set as a high-priority harvest area, and a harvest priority ranking list of each area and a corresponding priority level division result are obtained; According to the harvest priority sorting list and the priority level classification results, pre-configured machinery and human resource configuration information is obtained, and more harvest resources and time quotas are allocated to high-priority areas through a priority matching allocation method, thereby determining a dynamic resource allocation plan and operation time scheduling plan for each area during the planting cycle; By using the time node information in the operation time scheduling plan, historical change data of the filling rate and real-time temperature, humidity and precipitation meteorological monitoring data of each area in the corresponding time period are obtained, the time dependence relationship between the filling rate and meteorological factors is established, and a prediction trend curve of the grain moisture content value over time is obtained; According to the grain moisture content prediction trend curve and the harvest time node in the operation time arrangement plan, the moisture content prediction value corresponding to the scheduled harvest time is obtained, and the grain moisture content prediction result of each area is obtained.

8. The method for optimizing the grain planting standard cycle according to claim 1, characterized in that: The step S7 specifically includes: Based on the predicted results of the grain moisture content values in each region and the pre-set safe storage moisture content range standard, the regions with different moisture content ranges were divided into three treatment level areas: high moisture content area, medium moisture content area, and low moisture content area. The moisture content level classification results and the corresponding drying treatment intensity requirements of each area were obtained. Obtain the difference between the target moisture content and the current moisture content corresponding to each grade area, determine the amount of water removal required for the grains in each area to meet the safe storage standard, and obtain differentiated moisture treatment target parameters for the three treatment grade areas; Based on the differentiated moisture treatment target parameters and pre-configured drying equipment performance parameter data, a correspondence between moisture removal amount and drying time and temperature settings is established. Long-term high-temperature drying parameters for high-moisture content areas, medium-time medium-temperature drying parameters for medium-moisture content areas, and short-time low-temperature drying parameters for low-moisture content areas are obtained, thereby determining differentiated drying pretreatment standards for each grade of area. According to the differentiated drying pretreatment standards and the pre-arranged post-processing operation time plan, the drying time, temperature setting and humidity control process parameters are associated with the grain characteristics of the corresponding area to obtain the differentiated drying pretreatment standard formulation results for the grains in each area.

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