Crop partition accurate management method and system based on intelligent water and fertilizer integration

Abstract

The invention relates to the technical field of crop management, and discloses a crop zoning accurate management method and system based on intelligent water and fertilizer integration, and the method comprises the steps: dividing a planting region into cells with consistent arrangement of ground pipelines and valves, collecting original data, and carrying out the preprocessing of the original data, and obtaining target monitoring data; comparing the soil volume moisture content data, combining evapotranspiration trend and salinity correction, and then combining with a fertilization comparison result to generate water and fertilizer state classification data; dividing a cell set according to a classification result, carrying out aggregation processing, calculating a water and fertilizer regulation parameter value, comparing the water and fertilizer regulation parameter value with a historical value, and carrying out monitoring data correction to obtain a target water and fertilizer regulation parameter value; and comparing community distribution differences to generate correction marks, performing statistics to form correction parameter values, and integrating the correction parameter values with the target water and fertilizer regulation and control parameter values to obtain the partition water and fertilizer management method. According to the invention, precise management of water and fertilizer is realized through closed-loop self-adaptive partition regulation and control, and the utilization rate is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of crop management, in particular to a crop zoned precision management method and system based on intelligent water and fertilizer integration. BACKGROUND

[0002] With the development of precision agriculture, water and fertilizer integration technology has gradually become an important direction of modern agricultural production. Existing researches are mostly focused on crop growth diagnosis, nutrient diagnosis and water diagnosis, etc. The state information of crops at different stages can be obtained based on remote sensing images, ground sensors and experimental analysis, and water and fertilizer management prescription maps can be generated accordingly. However, most of these studies are still in the stage of experimental demonstration or theoretical verification, and there is no mature method that matches the conditions of large-scale field production. Especially in the aspects of variable irrigation and variable fertilization, the existing researches generally have the problems of single data source, insufficient control precision and lack of connection with hardware devices.

[0003] Taking the northwest region as an example, the region has sufficient sunlight but less precipitation and large evaporation, and agricultural production highly depends on drip irrigation systems and water and fertilizer integration facilities. Crops often need dozens of irrigation and fertilization operations during the whole growth period, and the number of irrigation and fertilization operations in some areas even exceeds 40 times. Most of the existing water and fertilizer management methods rely on manual experience or simple timing and quantification mode, and cannot fully consider the differences of soil water and salt conditions, weather conditions and crop growth stages, which may easily cause problems such as excessive irrigation, nutrient loss or insufficient nutrient supply, and directly affect the water and fertilizer utilization efficiency and crop growth consistency. SUMMARY

[0004] Therefore, the present application provides a crop zoned precision management method and system based on intelligent water and fertilizer integration, which aims to solve the problem of low resource utilization efficiency caused by lack of objective basis in the control of water and fertilizer input relying on human experience in the prior art.

[0005] In one aspect, the present application provides a crop zoned precision management method based on intelligent water and fertilizer integration, comprising: dividing a planting area into a plurality of ground pipeline and valve arranged consistent plots, collecting a plurality of original data in each ground pipeline and valve arranged consistent plot to obtain initial monitoring data; preprocessing the initial monitoring data to obtain target monitoring data; comparing the target soil volume water content data with the target water content range to obtain a water content comparison result, correcting the water content comparison result according to the evapotranspiration trend obtained from the target meteorological data and the comparison result of the target salt and electrical conductivity data, obtaining a water correction result, comparing the target fertilization concentration data with the target fertilization curve to obtain a fertilization comparison result, and obtaining water and fertilizer state classification data according to the water correction result and the fertilization comparison result; The water and fertilizer state classification data corresponding ground pipeline and valve arrangement consistent plot is divided into water and fertilizer deficient plot, water deficient and fertilizer adequate plot, adequate water and fertilizer deficient plot, adequate water and fertilizer adequate plot, and over-wet and over-fertilized plot, to obtain a corresponding set, and the corresponding set is aggregated to obtain water and fertilizer control parameter values of each ground pipeline and valve arrangement consistent plot; The water and fertilizer control parameter values are compared with historical water and fertilizer control parameter values, and the water and fertilizer control parameter values are corrected according to the comparison result and target meteorological data, target growth period data, and target fertilization concentration data to obtain target water and fertilizer control parameter values; The differences between the distribution of each plot in the corresponding set and the water and fertilizer state classification data are counted to obtain correction parameter markers, including irrigation deficiency correction parameter markers, fertilization concentration deviation correction parameter markers, and drainage delay correction parameter markers; The occurrence of each type of correction parameter marker in all ground pipeline and valve arrangement consistent plots is counted to obtain correction parameter values of the target water and fertilizer control parameter values; The partition water and fertilizer is managed based on the target water and fertilizer control parameter values and the correction parameter values.

[0006] Further, when collecting the initial monitoring data, it includes: Each ground pipeline and valve arrangement consistent plot is set with a monitoring point according to a preset spatial layout rule, and the spatial layout rule is to set a soil volume water content monitoring point and a root zone salt and conductivity monitoring point inside each ground pipeline and valve arrangement consistent plot, and to set a check monitoring point at the plot boundary; The soil volume water content raw data, root zone salt and conductivity raw data, meteorological raw data, crop growth period raw data, and fertilization concentration raw data are collected according to a sampling period, and the sampling period is determined according to the key growth stage of the target crop, and the sampling period is divided into intensive sampling and regular sampling, the intensive sampling is to shorten the sampling interval to the hour level and acquire data within a continuous period, and the regular sampling is to acquire data at daily intervals; The intensive sampling is triggered according to the period after the rainfall process or irrigation is completed, the sampling interval is shortened, and the above-mentioned various types of raw data are continuously collected until the soil volume water content and evapotranspiration index return to the stable change range; The collected various types of raw data are transmitted wirelessly to the Internet of Things master control for time stamp calibration and ground pipeline and valve arrangement consistent plot number matching to obtain initial monitoring data.

[0007] Further, when the initial monitoring data is preprocessed, it includes: The records exceeding the sensor range limit value in the original soil volume water content data and the records with a difference value exceeding the maximum allowed change amplitude determined based on the statistical distribution of historical monitoring data in adjacent time periods are removed, missing entries are interpolated, and target soil volume water content data is obtained; The records exceeding the sensor range limit value in the original meteorological data are removed or the records with inconsistent units are converted, the maximum allowed change amplitude of the adjacent time period difference value is used for time series smoothing of abnormal jumps, and target meteorological data is obtained; The records with negative values, exceeding the sensor range limit value, or a difference value exceeding the maximum allowed change amplitude in the original root zone salt and conductivity data are removed, and the target salt and conductivity data is obtained by smoothing using a sliding average with a window length of not less than three sampling intervals; The sequences with baseline drift in the original fertilization concentration data are corrected by aligning the baseline of the stable section, and the sequences with observation interruptions are segmented and spliced according to the timestamp order, and the target fertilization concentration data is obtained; The original crop growth period data is corresponded to the plot number according to the timestamp and the ground pipeline and valve arrangement, and the asynchronous records are resampled according to the sampling period, and the target growth period data is obtained; The target soil volume water content data, target meteorological data, target salt and conductivity data, target fertilization concentration data, and target growth period data are aligned at a unified time granularity and corresponded to the plot number according to the ground pipeline and valve arrangement, and the target monitoring data is obtained.

[0008] Further, when comparing and correcting, it includes: The target soil volume water content data is compared point by point with the minimum and maximum values of the target water content range, and the water comparison result is obtained, which is marked as low water if it is lower than the minimum value, as appropriate water if it is between the minimum and maximum values, and as high water if it is higher than the maximum value; When the evapotranspiration consumption trend of the target meteorological data shows an upward trend and reaches the maximum upward amplitude determined according to the historical change distribution, the low water is incrementally corrected, and the evapotranspiration correction result is obtained; When the evapotranspiration consumption trend of the target meteorological data shows a downward trend and reaches the maximum downward amplitude determined according to the historical change distribution, the high water is decrementally corrected, and the evapotranspiration correction result is obtained; The evapotranspiration correction result is compared with the target salt and conductivity data, and when the salt and conductivity are in the high salt interval, the appropriate water is adjusted to low water, and when the salt and conductivity are in the low salt interval, the appropriate water is adjusted to high water, and the water correction result is obtained; The target fertilization concentration data is compared with the minimum value and the maximum value of the target fertilization curve point by point to obtain a fertilization comparison result, wherein a value lower than the minimum value is marked as a low concentration, a value between the minimum value and the maximum value is marked as an appropriate concentration, and a value higher than the maximum value is marked as a high concentration; The water-fertilizer state classification data is obtained according to the correspondence between the water correction result and the fertilization comparison result.

[0009] Further, when the water-fertilizer state classification data is divided, it includes: The ground pipeline and valve arrangement consistent zone marked as low water content and low concentration in the water-fertilizer state classification data is divided into a water and fertilizer deficient zone; The ground pipeline and valve arrangement consistent zone marked as low water content and appropriate concentration in the water-fertilizer state classification data is divided into a water deficient and appropriate fertilizer zone; The ground pipeline and valve arrangement consistent zone marked as appropriate water content and low concentration in the water-fertilizer state classification data is divided into an appropriate water and fertilizer deficient zone; The ground pipeline and valve arrangement consistent zone marked as appropriate water content and appropriate concentration in the water-fertilizer state classification data is divided into an appropriate water and fertilizer zone; The ground pipeline and valve arrangement consistent zone marked as high water content or high concentration in the water-fertilizer state classification data is divided into an over-wet and over-fertilized zone; The same type of zones are summarized into corresponding zone sets, including a water and fertilizer deficient zone set, a water deficient and appropriate fertilizer zone set, an appropriate water and fertilizer deficient zone set, an appropriate water and fertilizer zone set, and an over-wet and over-fertilized zone set, and the five types of zone sets are collectively referred to as corresponding sets; When the corresponding sets are obtained, the number distribution difference of the corresponding sets is calculated to obtain a set distribution characteristic value, and the set distribution characteristic value is attached to the corresponding sets.

[0010] Further, when the corresponding sets are aggregated, it includes: The number of zones in the water and fertilizer deficient zone set, the water deficient and appropriate fertilizer zone set, the appropriate water and fertilizer deficient zone set, the appropriate water and fertilizer zone set, and the over-wet and over-fertilized zone set in the corresponding sets is counted, and the statistical number is multiplied by the water and fertilizer regulation parameter benchmark value set according to the crop growth period demand curve to obtain the initial regulation parameter value of each set; The set distribution characteristic value is compared with the initial regulation parameter value one by one, and when the number proportion of a certain corresponding set exceeds the benchmark distribution of the set distribution characteristic value, the initial regulation parameter value of the corresponding set is marked as a deviation state; When there is a deviation state, the initial regulation parameter value of the corresponding set is adjusted by a preset proportion, and the initial regulation parameter values of other sets are scaled by the proportion to obtain the corrected regulation parameter values; The corrected control parameter values ​​are assigned to the corresponding communities according to the community numbers with consistent ground pipeline and valve layouts, thus obtaining the water and fertilizer control parameter values.

[0011] Furthermore, when comparing and correcting the water and fertilizer regulation parameter values ​​with historical water and fertilizer regulation parameter values, the following steps are included: The water and fertilizer control parameter values ​​of communities with consistent ground pipeline and valve layouts are compared with historical water and fertilizer control parameter values ​​item by item to obtain the comparison results. When the comparison result is lower than the range, incremental correction is made based on the magnitude of the upward trend of evapotranspiration in the target meteorological data. Another incremental correction is made when the plant growth or leaf area increases as reflected in the target growth period data. Incremental correction is maintained when the target fertilizer concentration data is lower than the minimum value of the target fertilizer curve. When the comparison result is higher than the interval, the reduction correction is made according to the magnitude of the downward trend of evapotranspiration consumption in the target meteorological data. Another reduction correction is made when the plant growth or leaf area is reduced as reflected in the target growth period data. The reduction correction is maintained when the target fertilizer concentration data is higher than the maximum value of the target fertilizer curve. When the comparison results are within the range, keep the water and fertilizer regulation parameter values ​​unchanged; The revised comparison results are used as the target water and fertilizer regulation parameter values.

[0012] Furthermore, when statistically analyzing the differences between the distribution of each community in the corresponding set and the water and fertilizer status classification data, the following steps are taken: The distribution numbers of water-deficient and fertilizer-deficient plots, water-deficient and fertilizer-appropriate plots, water-appropriate and fertilizer-deficient plots, water-appropriate and fertilizer-appropriate plots, and overly wet and overly fertilizer-appropriate plots are compared with the overall distribution numbers of water and fertilizer status classification data to obtain the distribution difference results. When the distribution difference results show that the proportion of water-deficient and fertilizer-deficient plots or water-deficient and fertilizer-appropriate plots is higher than the overall water-deficient proportion, the corresponding plot is marked with an irrigation insufficiency correction parameter. When the distribution difference results show that the proportion of water-deficient and fertilizer-deficient plots or well-watered and fertilizer-deficient plots is higher than the proportion of low concentration in the overall distribution, the fertilizer concentration deviation correction parameter is marked for the corresponding plot. When the distribution difference results show that the proportion of plots with suitable water and fertilizer or overly wet and overly fertile areas is higher than the proportion of high water content in the overall distribution, drainage hysteresis correction parameters are marked for the corresponding plots.

[0013] Furthermore, when obtaining the correction parameter values ​​for the target water and fertilizer regulation parameters and determining the zoning water and fertilizer management method, the following steps are included: The frequency of occurrence of the irrigation deficiency correction parameter marker, fertilizer concentration deviation correction parameter marker, and drainage lag correction parameter marker in communities with consistent ground pipeline and valve layouts is statistically analyzed. The results are then converted into quantitative correction coefficients according to the statistical proportions to obtain the correction parameter values ​​for the target water and fertilizer regulation parameters. The target water and fertilizer regulation parameter value is superimposed and corrected with the correction parameter value to obtain the corrected target water and fertilizer regulation parameter value; The target water and fertilizer regulation parameter values ​​are integrated according to the type of plot to form a zonal water and fertilizer management method. The zonal water and fertilizer management method includes: irrigation and fertilization parameters for water- and fertilizer-deficient plots, irrigation parameters for water- and fertilizer-appropriate plots, fertilization parameters for water- and fertilizer-deficient plots, maintenance parameters for water- and fertilizer-appropriate plots, and drainage parameters for overly wet and overly fertile plots.

[0014] Compared with existing technologies, the advantages of this invention are as follows: By dividing the planting area into small plots with uniformly arranged ground pipes and valves and setting up monitoring points, the problem of relying on manual experience for large-scale extensive management is avoided, allowing water and fertilizer conditions to be subdivided and monitored at the plot scale; by preprocessing the initial monitoring data to remove outliers and correct drift signals, the accuracy and stability of the data basis for subsequent judgments are ensured; by comprehensively judging multiple factors such as moisture content comparison, evapotranspiration trend correction, salinity conductivity comparison, and fertilizer concentration comparison, deviations caused by single indicators are avoided, and dynamic correction of water and nutrient status is achieved; by classifying water and fertilizer status into water and fertilizer deficiency, water and fertilizer deficiency, and adequate water and fertilizer conditions, the invention provides a comprehensive assessment of water and fertilizer conditions. Five types of plots—those lacking fertilizer, those with adequate water and fertilizer, and those that are excessively wet or over-fertilized—are aggregated to generate targeted water and fertilizer regulation parameters for each plot. By comparing these parameters with historical values ​​and incorporating meteorological, growth stage, and fertilization curve data, dynamic adaptive adjustments are achieved. Correction parameter markers are generated by statistically analyzing plot distribution differences, and these correction parameter values ​​are further developed, effectively reducing the interference of single-cycle or single-regional anomalies on the overall regulation strategy. Finally, based on the target water and fertilizer regulation parameters and the correction parameter values, a regional water and fertilizer management method is generated, quantifying irrigation, fertilization, and drainage operations for different plot types into specific parameters, enabling precise, traceable, and actionable control of water and fertilizer inputs. Therefore, this invention improves water and fertilizer utilization efficiency, reduces biases caused by human subjectivity, and enhances the system's adaptability to complex environmental conditions.

[0015] On the other hand, this application also provides a crop zoning precision management system based on intelligent water and fertilizer integration, used to implement the above-mentioned crop zoning precision management method based on intelligent water and fertilizer integration, including: The data acquisition module divides the planting area into several small areas with consistent ground pipe and valve layouts, and collects some raw data in each small area to obtain initial monitoring data. The data preprocessing module preprocesses the initial monitoring data to obtain the target monitoring data; The state determination module compares the target soil volumetric moisture content data with the target moisture content range to obtain the moisture content comparison result. Based on the evapotranspiration consumption trend obtained from the target meteorological data and the comparison results of the target salinity and electrical conductivity data, the moisture content comparison result is corrected to obtain the moisture correction result. The target fertilizer concentration data is compared with the target fertilizer curve to obtain the fertilizer comparison result. Based on the moisture correction result and the fertilizer comparison result, water and fertilizer state classification data is obtained. The community division and aggregation module divides the communities with consistent ground pipe and valve layouts corresponding to the water and fertilizer status classification data into water-deficient and fertilizer-deficient communities, water-deficient and fertilizer-appropriate communities, water-appropriate and fertilizer-deficient communities, water-appropriate and fertilizer-appropriate communities, and overly wet and overly fertilizer-appropriate communities, obtaining corresponding sets. The corresponding sets are then aggregated to obtain the water and fertilizer control parameter values ​​for each community with consistent ground pipe and valve layouts. The historical comparison and correction module compares the water and fertilizer regulation parameter values ​​with historical water and fertilizer regulation parameter values. Based on the comparison results, target meteorological data, target growth period data, and target fertilizer concentration data, the water and fertilizer regulation parameter values ​​are corrected to obtain the target water and fertilizer regulation parameter values. The difference statistics module statistically analyzes the differences between the distribution of each community in the corresponding set and the water and fertilizer status classification data to obtain correction parameter labels. The correction parameter labels include irrigation insufficiency correction parameter labels, fertilizer concentration deviation correction parameter labels, and drainage lag correction parameter labels. The correction value generation module statistically analyzes the occurrence of various correction parameters in all communities with consistent ground pipeline and valve layouts to obtain the correction parameter values ​​for the target water and fertilizer regulation parameters. The prescription set generation module manages water and fertilizer in different zones based on target water and fertilizer regulation parameter values ​​and correction parameter values.

[0016] It is understandable that the above-mentioned method and system for precise crop zoning management based on intelligent water and fertilizer integration has the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a crop zoning precision management method based on intelligent water and fertilizer integration, provided for an embodiment of the present invention; Figure 2This is a functional block diagram of a crop zoning precision management system based on intelligent water and fertilizer integration, provided for an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] In some embodiments of this application, see Figure 1 As shown, a method for precise crop zoning management based on intelligent water and fertilizer integration includes: S100: Divide the planting area into several plots with consistent ground pipe and valve layouts, and collect some raw data in each plot to obtain initial monitoring data. S200: Preprocess the initial monitoring data to obtain the target monitoring data; S300: Compare the target soil volumetric moisture content data with the target moisture content range to obtain the moisture content comparison result. Correct the moisture content comparison result based on the evapotranspiration consumption trend obtained from the target meteorological data and the comparison result of the target salinity and electrical conductivity data to obtain the moisture correction result. Compare the target fertilizer concentration data with the target fertilizer curve to obtain the fertilizer comparison result. Based on the moisture correction result and the fertilizer comparison result, obtain the water and fertilizer status classification data. S400: Divide the areas with consistent ground pipe and valve layouts corresponding to the water and fertilizer status classification data into water-deficient and fertilizer-deficient areas, water-deficient and fertilizer-appropriate areas, water-appropriate and fertilizer-deficient areas, water-appropriate and fertilizer-appropriate areas, and overly wet and overly fertilizer-appropriate areas to obtain corresponding sets. Aggregate the corresponding sets to obtain the water and fertilizer control parameter values ​​of each area with consistent ground pipe and valve layouts. S500: Compare the water and fertilizer regulation parameter values ​​with historical water and fertilizer regulation parameter values, and correct the water and fertilizer regulation parameter values ​​based on the comparison results, target meteorological data, target growth period data, and target fertilizer concentration data to obtain the target water and fertilizer regulation parameter values. S600: Statistically analyze the differences between the distribution of each cell in the corresponding set and the water and fertilizer status classification data to obtain correction parameter labels. The correction parameter labels include irrigation insufficiency correction parameter labels, fertilizer concentration deviation correction parameter labels, and drainage lag correction parameter labels. S700: Statistically analyze the occurrence of various correction parameters in all areas with consistent ground pipeline and valve layouts to obtain the correction parameter values ​​for the target water and fertilizer regulation parameters. S800: Manages water and fertilizer in zones based on target water and fertilizer regulation parameter values ​​and correction parameter values.

[0020] Specifically, the planting area is first divided into several plots with uniformly arranged ground pipes and valves according to a pre-defined spatial division method, ensuring that differences in soil and crop conditions in different areas can be captured individually. Within each plot, raw data is collected using sensors or monitoring devices. This raw data covers relevant values ​​reflecting soil moisture, crop growth status, and fertilizer application, and is then aggregated to form initial monitoring data. Since initial monitoring data often contains measurement errors, incomplete timestamps, and some missing values, preprocessing is required. The preprocessing process involves filtering, calibrating, and interpolating the data to obtain target monitoring data at a uniform time granularity. After obtaining the target monitoring data, the soil volumetric moisture content is compared point-by-point with the target moisture content range to obtain the moisture comparison results. To avoid the bias caused by simple comparison, the comparison results are also corrected by incorporating the evapotranspiration trend reflected in the target meteorological data, as well as changes in salinity and conductivity, thus obtaining the moisture correction results. Simultaneously, the target fertilizer concentration data is compared point-by-point with the pre-defined target fertilizer curve to obtain the fertilizer comparison results. By combining the water correction results and fertilization comparison results, water and fertilizer status classification data reflecting the current state can be obtained. Based on this, according to the combination characteristics of the water and fertilizer status classification data, areas with consistent ground pipe and valve layouts are divided into water- and fertilizer-deficient areas, water- and fertilizer-appropriate areas, water- and fertilizer-appropriate areas, water- and fertilizer-appropriate areas, and overly wet and overly fertile areas, forming corresponding sets. These corresponding sets are then aggregated. The aggregation logic compares the distribution of the sets with predetermined reference requirements to obtain the water and fertilizer regulation parameter values ​​corresponding to each area with consistent ground pipe and valve layouts. These water and fertilizer regulation parameter values ​​are then compared item by item with historical water and fertilizer regulation parameter values. During the comparison, target meteorological data, target growth stage data, and target fertilizer concentration data are referenced, and the current crop growth trend is considered to form a comparatively corrected result, obtaining the target water and fertilizer regulation parameter value. Based on the target water and fertilizer regulation parameter value, the distribution of each area in the corresponding set is statistically analyzed against the overall classification data. If deviations occur, corresponding correction parameter labels are generated, including labels for insufficient irrigation, fertilizer concentration deviation, and drainage lag. The occurrence of various calibration parameter markers in all areas with consistent ground pipeline and valve layouts was statistically analyzed and quantified to obtain calibration parameter values ​​for correction. Finally, the target water and fertilizer control parameter values ​​were combined with the calibration parameter values ​​to form a zoned water and fertilizer management method for production guidance, thereby achieving precise regional and differentiated water and fertilizer application.

[0021] Understandably, preprocessing the initial monitoring data improves the completeness and consistency of the target monitoring data. This involves comparing the target soil volumetric moisture content data with the target moisture range, and correcting it by incorporating the evapotranspiration trend of the target meteorological data and the target salinity and conductivity data. This reduces misjudgments caused by single comparisons. The target fertilizer concentration data is compared with the target fertilizer curve, and together with the moisture correction results, water and fertilizer status classification data is formed, enhancing the accuracy of the classification results. Based on the water and fertilizer status classification data, corresponding sets are formed and aggregated to obtain water and fertilizer regulation parameter values, enabling quantitative decision-making at the community level for consistent ground pipeline and valve layout. The water and fertilizer regulation parameter values ​​are compared with historical water and fertilizer regulation parameter values ​​and corrected to stabilize parameter fluctuations and enhance temporal continuity. Correction parameter labels are obtained through difference statistics, and correction parameter values ​​are generated. These are then used to uniformly correct the target water and fertilizer regulation parameter values, reducing the impact of spatial imbalances. Finally, based on the target water and fertilizer regulation parameter values ​​and the correction parameter values, a zoned water and fertilizer management method is determined, achieving differentiated water and fertilizer input in different zones and improving resource utilization efficiency and management accuracy.

[0022] In some embodiments of this application, the initial monitoring data collection includes: For each area with uniform ground pipes and valves, monitoring points are set up according to the preset spatial layout rules. The spatial layout rules are as follows: soil volumetric moisture content monitoring points and root zone salinity and conductivity monitoring points are set up inside each area with uniform ground pipes and valves, and check monitoring points are set up at the boundary of the area. The original data of soil volumetric water content, root zone salinity and electrical conductivity, meteorological data, crop growth period data and fertilizer concentration were collected according to the sampling cycle. The sampling cycle was determined according to the key growth stage of the target crop. Intensive sampling shortened the sampling interval to the hour level and acquired data in a continuous period of time, while regular sampling acquired data at daily intervals. Intensive sampling is triggered based on the time period after rainfall or irrigation, the sampling interval is shortened and the above-mentioned raw data are collected continuously until the soil volumetric moisture content and evapotranspiration index return to a stable range of change. The collected raw data are wirelessly transmitted to the IoT master controller, where timestamp calibration is performed and the data is matched with the community number to ensure consistency with the layout of ground pipelines and valves, thus obtaining the initial monitoring data.

[0023] Specifically, the monitoring points within the same plot of ground pipelines and valves are arranged according to spatial layout rules. Based on local experiments or historical data to determine the root distribution depth and the location of the main absorption zone, soil volumetric moisture content monitoring points are vertically arranged in the middle of the main absorption zone, with one layer at each of its upper and lower boundaries to cover the main root water intake zone. Based on differences in soil texture and permeability, the horizontal spacing of monitoring points within the same plot of ground pipelines and valves is set in descending order of permeability, with appropriate spacing for sandy soils and appropriate spacing for clay soils to avoid spatial redundancy. Based on the location of irrigation pipelines and drippers, monitoring points are placed relative to the drip zone in two types of locations: within the typical wetting front and at the boundary of the wetting front, to simultaneously characterize high water content areas and transition zones. Based on the characteristics of salt migration and accumulation, the salt content in the root zone is... Electrical conductivity monitoring points are placed near the lower boundary of the root system and in potential salt-sensitive layers to capture salt peaks during upward or downward leaching. Based on micro-topography and drainage direction, checkpoints are set up at the upwind end or catchment side of the plot boundary to compare with internal monitoring points and identify abnormal deviations. This rule, based on the combination of vertical positions, horizontal spacing, and checkpoints, is determined according to the depth of crop root distribution and soil texture characteristics, resulting in a spatial layout rule for each plot with consistent ground pipe and valve arrangement. This ensures that soil volumetric moisture content monitoring points cover the main water intake area of ​​the root zone, while also ensuring that salt and electrical conductivity monitoring points in the root zone reflect fertilizer migration and accumulation. Checkpoints are set up at the plot boundary to verify the reliability of the data collected from internal monitoring points. Comparison of data from checkpoints with internal points identifies abnormal deviations. The sampling period is determined based on the key growth stages of the target crop. For example, during water and fertilizer sensitive stages such as seedling, jointing, flowering, and grain-filling stages, shorter sampling intervals are needed to capture rapid changes. Regular sampling is defined as continuous data acquisition at daily intervals, primarily applicable to non-sensitive periods. Intensive sampling, on the other hand, shortens the sampling interval to the hourly level, automatically triggered after rainfall or irrigation to ensure complete recording of drastic changes in soil moisture and fertility. In this application, stable variation range refers to a state where soil volumetric moisture content changes gradually, defined as a moisture content change rate of less than 5% over two consecutive hours, while evapotranspiration remains within the range consistent with historical averages for the same period. Intensive sampling ceases and regular sampling resumes when this condition is met. This setting ensures consistency in sampling duration, avoiding over- or under-sampling due to environmental differences. All collected raw data are transmitted wirelessly to the IoT master controller. The master controller timestamps the data and matches it with the cell numbers corresponding to the layout of ground pipelines and valves, ensuring all data correspond under a unified time reference, thus forming complete initial monitoring data.

[0024] In some embodiments of this application, the preprocessing of initial monitoring data includes: Records that exceed the sensor's range limit in the raw soil volumetric moisture content data, as well as records whose difference from adjacent time periods exceeds the maximum allowable variation determined based on the statistical distribution of historical monitoring data, are removed. Missing entries are then interpolated to complete the data, thus obtaining the target soil volumetric moisture content data. Records exceeding the sensor's range limit in the raw meteorological data are removed or records with inconsistent units are converted. The abnormal jumps are smoothed over time based on the maximum allowable variation of the difference between adjacent time periods to obtain the target meteorological data. Records with negative values, exceeding the sensor's range limit, or exceeding the maximum allowable variation range in the raw data of salinity and conductivity in the root region are removed. The data are then smoothed using a moving average with a window length of not less than three sampling intervals to obtain the target salinity and conductivity data. The sequences with baseline drift in the raw fertilizer concentration data are corrected by aligning them with the baseline of the stable segment. The sequences with observation interruptions are spliced ​​together in time stamp order to obtain the target fertilizer concentration data. The raw crop growth period data is matched with the community number that is consistent with the layout of ground pipelines and valves based on the timestamp. Asynchronous records are resampled according to the sampling period to obtain the target growth period data. The target soil volumetric moisture content data, target meteorological data, target salinity and electrical conductivity data, target fertilizer concentration data, and target growth period data are aligned at a uniform time granularity and corresponded to the community number with the layout of ground pipelines and valves to obtain the target monitoring data.

[0025] Specifically, when preprocessing the initial monitoring data, the raw soil volumetric moisture content data first needs to be verified. Records exceeding the sensor's range limit are discarded, and records where the difference between adjacent time periods exceeds the maximum allowable variation range are identified as abnormal and deleted. The maximum allowable variation range refers to the threshold calculated based on the statistical distribution of historical monitoring data, i.e., the normal fluctuation range of soil volumetric moisture content within the same or adjacent time periods. It is determined by statistically analyzing monitoring sequences over at least two crop growth cycles in the past three years, calculating the distribution range of the difference between adjacent time periods, and using the upper limit at a 95% confidence level as the allowable variation range. Missing entries are repaired using interpolation, which can be linear interpolation or interpolation based on the mean of adjacent intervals to ensure the continuity of the time series and obtain the target soil volumetric moisture content data. In the processing of raw meteorological data, records exceeding the sensor's range limit are discarded, and records with unit differences are uniformly converted to the same physical quantity system. For abnormal jumps in the time series, if the difference between adjacent time periods exceeds the maximum allowable variation range, a smoothing algorithm is used for correction. The maximum permissible variation range in meteorological data is defined as the upper limit calculated based on the variation range of factors such as daily average temperature, relative humidity, and wind speed in historical observation sequences. Distribution models are established for different seasons and regions to ensure consistency with actual meteorological conditions. The smoothing algorithm uses a moving average method, where the moving average refers to taking the arithmetic mean of several sampling points within a continuous window as the representative value of the current point. This application specifies that the window length should not be less than three sampling intervals to ensure the stability of the correction results. The target meteorological data is obtained after this processing. In the processing of the raw data for salinity and conductivity in the root zone, records with negative values, exceeding the sensor's range limit, or with differences from adjacent time periods exceeding the maximum permissible variation range are discarded. The maximum permissible variation range in salinity and conductivity data is defined as the upper limit obtained from the statistical distribution of the rate of change of salinity content and conductivity in adjacent time periods in historical monitoring sequences. It is usually taken as the upper limit of the 95% confidence interval to ensure the removal of abrupt or distorted data. The remaining sequences were then smoothed using a moving average method with a window length of no less than three sampling intervals to reduce transient interference fluctuations and obtain target salinity and conductivity data, making them more accurate in reflecting the fertilizer migration and accumulation process. In the processing of raw fertilizer concentration data, for sequences with baseline drift, alignment correction was performed based on the average baseline of the stable segment to eliminate measurement drift. For data gaps caused by observation interruptions, adjacent segments were spliced ​​together in timestamp order to restore continuity and obtain the target fertilizer concentration data. In the processing of raw crop growth period data, the timestamps were first matched with the cell numbers consistent with the layout of ground pipelines and valves to ensure consistency between records and cell locations. For asynchronous records caused by different sampling periods, resampling was performed to unify them to the same time granularity, obtaining the target growth period data.Finally, the target soil volumetric moisture content data, target meteorological data, target salinity and electrical conductivity data, target fertilizer concentration data, and target growth period data are aligned at a uniform time granularity and associated with them through the cell number to obtain complete target monitoring data.

[0026] In some embodiments of this application, the comparison and correction process includes: The target soil volumetric moisture content data is compared point by point with the minimum and maximum values ​​of the target moisture content range to obtain the moisture content comparison results. The values ​​below the minimum value are marked as low moisture content, the values ​​between the minimum and maximum values ​​are marked as suitable moisture content, and the values ​​above the maximum value are marked as high moisture content. When the evapotranspiration trend of the target meteorological data shows an upward trend and reaches the maximum increase determined based on the historical change distribution, the low water content is incrementally corrected to obtain the evapotranspiration correction result. When the evapotranspiration loss trend of the target meteorological data shows a downward trend and reaches the maximum decrease determined according to the historical change distribution, the high water content is reduced to obtain the evapotranspiration correction result. The evapotranspiration correction results are compared with the target salinity and conductivity data. When the salinity and conductivity are in the high salinity range, the suitable water content is adjusted to low water content, and when the salinity and conductivity are in the low salinity range, the suitable water content is adjusted to high water content, thus obtaining the moisture correction results. The target fertilizer concentration data is compared point by point with the minimum and maximum values ​​of the target fertilizer curve to obtain the fertilizer comparison results. The values ​​below the minimum value are marked as low concentration, the values ​​between the minimum and maximum value are marked as appropriate concentration, and the values ​​above the maximum value are marked as high concentration. Water and fertilizer status classification data were obtained based on the correspondence between the moisture correction results and the fertilizer comparison results.

[0027] Specifically, during the comparison and correction, the target soil volumetric water content data is first compared point by point with the target water content range. The minimum and maximum values ​​of the target water content range are determined as follows: based on the field capacity, wilting coefficient, and saturated water content of the target crop variety, combined with the results of local soil texture tests, the available water range between the field capacity and the wilting coefficient is calculated. Then, referring to the distribution of historical monitoring data of three years or more, 25% is taken as the minimum value and 75% as the maximum value, thus obtaining the dynamically adapted water content range.

[0028] In the correction process, evapotranspiration trend and salinity distribution are used as parallel correction factors in the calculation. The evapotranspiration trend is calculated using meteorological data to determine potential evapotranspiration, and compared with distribution curves from historical climate data to identify the maximum increase and decrease. The salinity range is determined by statistically analyzing long-term root conductivity monitoring data, with areas above 80% classified as high-salinity, below 20% as low-salinity, and the middle portion as suitable salinity. The water content comparison results, evapotranspiration trend correction factor, and salinity correction factor are integrated using a weighted scoring method. The weights are set based on the root depth of the target crop and the results of salinity sensitivity experiments; for example, the weight of the salinity factor is increased for crops with low salt tolerance, and the weight of the meteorological factor is increased for crops sensitive to evapotranspiration. The merged scoring results directly output moisture-corrected results, avoiding excessive bias caused by sequential corrections. Incremental correction: When the target soil volumetric moisture content is below the lower limit of the target moisture range, and meteorological data indicates a continuous increase in evapotranspiration, the system will increase the correction magnitude based on the original comparison results, further confirming the low moisture content state towards water shortage, and calculating the additional irrigation or water regulation required to prevent further water loss from crops due to excessive evapotranspiration. Decreasing correction: When the target soil volumetric moisture content is above the upper limit of the target moisture range, and meteorological data indicates a continuous decrease in evapotranspiration, the system will decrease the correction magnitude based on the original comparison results, further confirming the high moisture content state towards excessive moisture, and calculating the amount of irrigation to be reduced or the extent of drainage regulation required to prevent waterlogging or nutrient loss due to water retention.

[0029] The fertilization comparison results are obtained by comparing the target fertilization concentration data with the target fertilization curve point by point. The target fertilization curve is obtained by fitting the relationship between historical fertilization amount and yield. For example, the method for constructing the target fertilization curve is as follows: First, collect historical fertilization records and corresponding yield data of the target crop for the past five years or more. After removing outliers, use piecewise regression to fit the relationship curve between fertilization amount and yield. Determine the basic required concentration at the inflection point where the yield growth rate changes from increasing to decreasing, and take the lowest effective concentration of the corresponding curve as the minimum value. Determine the fertilizer damage boundary concentration at the inflection point where the yield growth rate tends to zero or even decreases as the curve continues to increase fertilization amount, and take the maximum allowable concentration of the corresponding curve as the maximum value. This range is dynamically adjusted according to the growth stage: set at 80% to 90% of the inflection point concentration during the seedling and jointing stages to prevent fertilizer damage in the seedling stage; set at 95% to 100% of the inflection point concentration during the flowering and grain-filling stages to ensure sufficient nutrient supply. The final fertilization curve is indexed by the growth stage, forming a set of stage-specific minimum and maximum values, which are used for comparison and determination of fertilization concentration. The minimum value is defined as the lowest concentration required to maintain basic growth, and the maximum value is defined as the upper limit concentration at which fertilizer damage occurs. The values ​​are dynamically adjusted according to the crop growth stage: the lower limit of the fitted curve is used during the seedling and jointing stages, and the upper limit of the fitted curve is used during the flowering and grain-filling stages, so as to ensure that the nutrient supply meets the growth requirements.

[0030] Finally, the corrected water status was correlated with the fertilization comparison results to form water and fertilizer status classification data, which provides the basic input for subsequent plot division and water and fertilizer regulation calculation.

[0031] In some embodiments of this application, the process of dividing water and fertilizer status classification data includes: The areas marked as having low water content and low concentration in the water and fertilizer status classification data with consistent ground pipe and valve layouts are classified as water- and fertilizer-deficient areas. The areas marked as having low water content and suitable concentration in the water and fertilizer status classification data with consistent ground pipe and valve layouts are classified as water-scarce and fertilizer-suitable areas. The plots marked as having suitable water content and low concentration in the water and fertilizer status classification data with consistent ground pipe and valve layouts are classified as suitable water and fertilizer deficient plots. The areas marked as having suitable water content and suitable concentration in the water and fertilizer status classification data are divided into suitable water and suitable fertilizer areas with consistent ground pipe and valve layout. The areas marked as having high water content or high concentration of ground pipes and valves in the water and fertilizer status classification data are classified as overly wet and overly fertile areas. Similar communities are grouped into corresponding community sets, including sets of communities lacking water and fertilizer, communities lacking water but with adequate fertilizer, communities with adequate water but lacking fertilizer, communities with adequate water and fertilizer, and communities that are too wet and too fertile. These five types of community sets are collectively referred to as the corresponding sets. When the corresponding set is obtained, the difference in the quantity distribution of the corresponding set is calculated to obtain the set distribution characteristic value, and the set distribution characteristic value is appended to the corresponding set.

[0032] Specifically, when classifying water and fertilizer status data, the first step is to determine the correspondence between the moisture correction results and the fertilizer comparison results. Each area with consistent ground pipe and valve layout is then marked as low water content, adequate water content, or high water content, as well as a combination of low concentration, adequate concentration, or high concentration. Therefore, areas with low water content and low concentration are classified as water- and fertilizer-deficient areas; areas with low water content and adequate concentration are classified as water- and fertilizer-adequate areas; areas with adequate water content and low concentration are classified as adequate water and fertilizer-deficient areas; areas with adequate water content and adequate concentration are classified as adequate water and fertilizer-adequate areas; and areas with high water content or high concentration are uniformly classified as overly wet and overly fertilizer-adequate areas.

[0033] After the neighborhoods are divided, neighborhoods belonging to the same category are grouped by their numbers to form five sets: water- and fertilizer-deficient neighborhoods, water- and fertilizer-sufficient neighborhoods, water- and fertilizer-sufficient neighborhoods, water- and fertilizer-sufficient neighborhoods, and overly wet and overly fertilizer-sufficient neighborhoods. These five sets are collectively referred to as the corresponding sets. Constructing corresponding sets not only facilitates subsequent aggregation calculations but also reduces the bias caused by fluctuations in single-point data.

[0034] In obtaining the corresponding sets, the differences in the quantity distribution of each type of set within the overall population are further calculated. For example, the proportion of water- and fertilizer-deficient community sets is compared with the proportion of the total number of communities to obtain set distribution characteristic values. These set distribution characteristic values, as indicators describing the degree of balance in the distribution of different community categories, are added to the corresponding sets and serve as important input conditions for subsequent aggregation processing and parameter correction.

[0035] In some embodiments of this application, the aggregation process for the corresponding set includes: The number of plots in the water-deficient and fertilizer-deficient plot set, water-deficient and fertilizer-appropriate plot set, water-appropriate and fertilizer-deficient plot set, water-appropriate and fertilizer-appropriate plot set, and overly wet and overly fertilizer plot set in the corresponding set is counted. The counted number is multiplied by the water and fertilizer regulation parameter benchmark value set according to the crop growth period demand curve for each set to obtain the initial regulation parameter value for each set. The distribution characteristic value of the set is compared with the initial control parameter value one by one. When the proportion of the number of a certain set exceeds the baseline distribution of the distribution characteristic value of the set, the initial control parameter value of the corresponding set is marked as an offset state. When an offset state exists, the initial control parameter value of the corresponding set is adjusted by a preset ratio, and the initial control parameter values ​​of other sets are scaled proportionally to obtain the corrected control parameter value. The corrected control parameter values ​​are assigned to the corresponding communities according to the community numbers with consistent ground pipeline and valve layouts, thus obtaining the water and fertilizer control parameter values.

[0036] Specifically, when aggregating the corresponding sets, the number of sets of water- and fertilizer-deficient plots, water- and fertilizer-appropriate plots, water- and fertilizer-deficient plots, water- and fertilizer-appropriate plots, and overly wet and overly fertile plots is first counted. The counted numbers are then multiplied by the baseline values ​​of the water and fertilizer regulation parameters for each set to obtain the initial regulation parameter values ​​for each set. The baseline values ​​of the water and fertilizer regulation parameters are derived from the water and fertilizer requirement curves of the target crop at different growth stages. These curves are constructed using long-term field trial data and agronomic recommendations. Specifically, baseline values ​​are set based on field capacity and basal fertilization during the seedling stage; based on root water uptake intensity and nitrogen fertilizer absorption efficiency during the jointing stage; based on peak evapotranspiration and potassium fertilizer demand during the flowering stage; and based on grain filling rate and phosphorus and potassium nutrient supply curves during the grain-filling stage. These baseline values ​​are statistically derived from historical data of the same crop and region, and their multi-year average is used as the baseline water and fertilizer parameters to ensure stability and comparability. Subsequently, the ensemble distribution characteristic value is calculated and the baseline distribution is defined: the baseline distribution is obtained through historical data of the same crop at the same growth stage, with a time span of no less than three years. In each historical sample, the proportion of the five types of plots in all plots is statistically analyzed to form a historical proportion sequence. The center position of this sequence is taken as the baseline proportion, and the high and low quantiles are used as the upper and lower limits of the allowable interval. The ensemble distribution characteristic value is defined as the correspondence between the proportion of the five types of plots in the current batch and the baseline proportion and its allowable interval. If it is higher than the upper limit, it is marked as exceeding the upper limit; if it falls within the interval, it is marked as within the interval; if it is lower than the lower limit, it is marked as lower than the lower limit, and the deviation from the nearest boundary is recorded. During the comparison phase, when the proportion of a certain ensemble is higher than the upper limit of the baseline distribution, its initial control parameter value is marked as an offset state; if it falls within the interval, it is not offset; if it is lower than the lower limit, it is marked according to the reverse judgment. When a deviation exists, the initial control parameter values ​​of the deviation set are first amplified or reduced by a preset ratio. This ratio is set based on the sensitivity classification of the crop growth period demand curve. When the water-deficient set exceeds the limit, water-related control parameters are preferentially increased; when the adequate water and fertilizer-deficient set exceeds the limit, nutrient-related control parameters are preferentially increased; and when the excessively wet and excessively fertilizer-rich set exceeds the limit, water-related control parameters are preferentially decreased. Secondly, the initial control parameter values ​​of the non-deviation sets are synchronously scaled proportionally, in the opposite direction to the deviation set, with the scaling magnitude determined by the degree of deviation. When multiple sets deviate simultaneously, they are corrected one by one in order of magnitude of deviation, and the corrected results serve as the baseline for subsequent corrections. Finally, the corrected control parameter values ​​are distributed to each cell according to the cell numbering consistent with the ground pipeline and valve layout, resulting in water and fertilizer control parameter values. This ensures clear correction criteria between different sets and avoids parameter adjustment deviations caused by ambiguous baseline distribution.

[0037] In some embodiments of this application, the process of comparing and correcting water and fertilizer regulation parameter values ​​with historical water and fertilizer regulation parameter values ​​includes: The water and fertilizer control parameter values ​​of communities with consistent ground pipeline and valve layouts are compared with historical water and fertilizer control parameter values ​​item by item to obtain the comparison results. When the comparison result is lower than the range, incremental correction is made based on the magnitude of the upward trend of evapotranspiration in the target meteorological data. Another incremental correction is made when the plant growth or leaf area increases as reflected in the target growth period data. Incremental correction is maintained when the target fertilizer concentration data is lower than the minimum value of the target fertilizer curve. When the comparison result is higher than the interval, the reduction correction is made according to the magnitude of the downward trend of evapotranspiration consumption in the target meteorological data. Another reduction correction is made when the plant growth or leaf area is reduced as reflected in the target growth period data. The reduction correction is maintained when the target fertilizer concentration data is higher than the maximum value of the target fertilizer curve. When the comparison results are within the range, keep the water and fertilizer regulation parameter values ​​unchanged; The revised comparison results are used as the target water and fertilizer regulation parameter values.

[0038] Specifically, the historical water and fertilizer regulation parameter values ​​were constructed based on monitoring results from multiple years, the same region, the same crop, and the same growth stage. First, the historical water and fertilizer regulation parameter values ​​underwent anomaly removal and normalization to eliminate outliers caused by sensor failure or extreme weather. Then, the sample distribution was calculated, using the mean of the multi-year samples as the center value, and taking a range of one standard deviation above and below the center value as the reference interval. Values ​​falling between the center value minus one standard deviation and the center value plus one standard deviation were considered within the interval; values ​​below the center value minus one standard deviation were considered below the interval; and values ​​above the center value plus one standard deviation were considered above the interval. When making corrections, if the comparison result is below the interval, an incremental correction is made based on the magnitude of the upward trend in evapotranspiration in the target meteorological data. Another incremental correction is made when the target growth stage data reflects an increase in plant growth or leaf area. The incremental correction is maintained when the target fertilizer concentration data is below the minimum value of the target fertilizer curve. If the comparison result is above the interval, a decremental correction is made based on the magnitude of the downward trend in evapotranspiration in the target meteorological data. Another decremental correction is made when the target growth stage data reflects a decrease in plant growth or leaf area. The decremental correction is maintained when the target fertilizer concentration data is above the maximum value of the target fertilizer curve. When the comparison result is within the interval, the water and fertilizer control parameters remain unchanged. For situations where historical data is insufficient or environmental conditions change, a dynamic correction mechanism is introduced: when the historical sample is less than three years or the deviation of the current year's meteorological parameters from the historical average exceeds a set threshold, monitoring results of similar crops in neighboring areas or parameters based on field trials are used as supplements to ensure the stability of the interval definition and the reliability of the comparison. Finally, each corrected comparison result is used as the target water and fertilizer control parameter value.

[0039] In some embodiments of this application, when statistically analyzing the differences between the distribution of each cell in the corresponding set and the water and fertilizer status classification data, the following steps are included: The distribution numbers of water-deficient and fertilizer-deficient plots, water-deficient and fertilizer-appropriate plots, water-appropriate and fertilizer-deficient plots, water-appropriate and fertilizer-appropriate plots, and overly wet and overly fertilizer-appropriate plots are compared with the overall distribution numbers of water and fertilizer status classification data to obtain the distribution difference results. When the distribution difference results show that the proportion of water-deficient and fertilizer-deficient plots or water-deficient and fertilizer-appropriate plots is higher than the overall water-deficient proportion, the corresponding plot is marked with an irrigation insufficiency correction parameter. When the distribution difference results show that the proportion of water-deficient and fertilizer-deficient plots or well-watered and fertilizer-deficient plots is higher than the proportion of low concentration in the overall distribution, the fertilizer concentration deviation correction parameter is marked for the corresponding plot. When the distribution difference results show that the proportion of plots with suitable water and fertilizer or overly wet and overly fertile areas is higher than the proportion of high water content in the overall distribution, drainage hysteresis correction parameters are marked for the corresponding plots.

[0040] Specifically, the proportions of water shortage, low concentration, and high water content in the overall distribution are determined by combining current monitoring data with historical baseline statistics. First, within each monitoring cycle, the water and fertilizer status classification results of all plots with consistent ground pipe and valve layouts are globally summarized. The proportions of water- and fertilizer-deficient plots and water- and fertilizer-appropriate plots within the total number of plots in that cycle are calculated as the current overall water shortage proportion. Similarly, the proportions of water- and fertilizer-deficient plots and water- and fertilizer-appropriate plots are used as the current overall low concentration proportion, and the proportions of water- and fertilizer-appropriate plots and overly wet and overly fertilizer-appropriate plots are used as the current overall high water content proportion. Second, using at least three years of historical monitoring data, the proportions of various plots under different seasons and growth stages are statistically analyzed over a long period to obtain the historical averages and fluctuation ranges of the water shortage, low concentration, and high water content proportions, which are then used as the historical baseline distribution. Finally, the current overall distribution proportions are compared with the historical baseline distribution. If the current proportions simultaneously exceed 20% of the historical average and exceed 15% in the global statistics of the current monitoring cycle, it is considered an abnormal distribution. For example, when the ratio of water- and fertilizer-deficient plots to water- and fertilizer-appropriate plots exceeds the aforementioned dual standards, an irrigation deficiency correction parameter marker is generated; when the ratio of water- and fertilizer-deficient plots to water- and fertilizer-appropriate plots meets the dual exceedance conditions, a fertilizer concentration deviation correction parameter marker is generated; when the ratio of water- and fertilizer-appropriate plots to overly wet and overly fertile plots simultaneously exceeds both historical and current judgment standards, a drainage lag correction parameter marker is generated. This joint judgment method, combining current global statistics with historical benchmark distribution, clarifies the source and correction conditions of the overall distribution ratio. This ensures that short-term dynamic changes can be captured while avoiding misjudgments caused by environmental fluctuations or single anomalies, thereby improving the scientific rigor and consistency of the generated correction parameter markers.

[0041] In some embodiments of this application, when obtaining the correction parameter value of the target water and fertilizer regulation parameter value and determining the zoning water and fertilizer management method, the following steps are included: The frequency of occurrence of the irrigation deficiency correction parameter marker, fertilizer concentration deviation correction parameter marker, and drainage lag correction parameter marker in communities with consistent ground pipeline and valve layouts is statistically analyzed. The results are then converted into quantitative correction coefficients according to the statistical proportions to obtain the correction parameter values ​​for the target water and fertilizer regulation parameters. The target water and fertilizer regulation parameter value is superimposed and corrected with the correction parameter value to obtain the corrected target water and fertilizer regulation parameter value; The target water and fertilizer regulation parameters are integrated according to the type of plot to form a zoned water and fertilizer management method. The zoned water and fertilizer management method includes: adjusting the irrigation and fertilization parameters of water-deficient and fertilizer-deficient plots, adjusting the irrigation parameters of water-deficient and fertilizer-appropriate plots, adjusting the fertilization parameters of water-appropriate and fertilizer-deficient plots, adjusting the maintenance parameters of water-appropriate and fertilizer-appropriate plots, and adjusting the drainage parameters of overly wet and overly fertile plots.

[0042] Specifically, before formulating a zoned water and fertilizer management method, it is necessary to first statistically analyze the irrigation under-correction parameter markers, fertilizer concentration deviation correction parameter markers, and drainage lag correction parameter markers for all zones with consistent ground pipeline and valve layouts. To ensure that the statistical results accurately reflect the overall distribution characteristics, a dual-reference method based on historical monitoring data and current cycle data is adopted for analysis. The proportion of occurrence of each correction parameter marker in all zones not only represents the degree of deviation in water and fertilizer status in the current cycle, but can also be compared with the data distribution of the same growth period in history to eliminate random deviations in a single cycle. When converting the proportion to a quantitative correction coefficient, a segmented progressive conversion logic is adopted: when the statistical proportion is within the median range of the historical reference distribution, it is converted to a standard correction coefficient of 1.0; when the proportion exceeds the upper limit of the reference distribution, it is increased segmentally according to the extent of the exceedance, increasing by 0.1 for every 10% exceedance, until it does not exceed the preset maximum correction value; when the proportion is below the lower limit of the reference distribution, it is decreased segmentally, decreasing by 0.1 for every 10% decrease, with a minimum of 0.5. This segmented proportional mapping method stably converts the proportions of the calibration parameter markers into quantitative correction coefficients, avoiding imbalances in correction amounts due to differences in the distribution of different plots. The obtained quantitative correction coefficients serve as calibration parameter values ​​for the target water and fertilizer control parameters. These coefficients are then progressively superimposed on the target water and fertilizer control parameters to form the calibrated target water and fertilizer control parameter values. Subsequently, the calibrated target water and fertilizer control parameter values ​​are integrated according to plot type to form a zoned water and fertilizer management method. This includes: irrigation and fertilization parameters for water- and fertilizer-deficient plots, irrigation parameters for water- and fertilizer-appropriate plots, fertilization parameters for water- and fertilizer-deficient plots, maintenance parameters for water- and fertilizer-appropriate plots, and drainage parameters for overly wet and overly fertile plots. This enables differentiated and precise management of different plot types. The irrigation parameters encompass target irrigation depth or volume, frequency, upper and lower limits for single water supply, shortest and longest intervals, and amplitude adjustment rules, used for... The system ensures soil moisture content returns to the target range. Fertilizer application parameters include target fertilizer concentration range, single application dosage, nutrient ratio, application time period, and adjustment buffer settings to ensure nutrient supply matches the crop's growth stage. Maintenance parameters define allowable fluctuation ranges, upper limits for fine-tuning, and fine-tuning rules under abnormal triggers to maintain stable water and fertilizer conditions. Drainage parameters include target drainage intensity, duration, upper limit for drainage frequency, conditions for stopping drainage, and fertilization linkage restrictions to promptly reduce soil moisture content and fertilizer accumulation risks under excessively wet or fertile conditions. Consistency constraints are established between these parameters to ensure coordination of irrigation, fertilization, and drainage strategies. When drainage parameters are activated, fertilization parameters automatically enter a reduced or suspended state to avoid system conflicts. Furthermore, each parameter has single-application upper limit, periodic upper limit, and minimum step size to prevent frequent fluctuations or excessive one-time adjustments. Periodic updates maintain consistency with target water and fertilizer control parameters and correction parameters, achieving precise water and fertilizer management for crop zones.

[0043] In another preferred embodiment based on the above embodiments, see [reference] Figure 2 As shown, this embodiment provides a crop zoning precision management system based on intelligent water and fertilizer integration, used to apply the above-mentioned crop zoning precision management method based on intelligent water and fertilizer integration, including: The data acquisition module divides the planting area into several small areas with consistent ground pipe and valve layouts, and collects some raw data in each small area to obtain initial monitoring data. The data preprocessing module preprocesses the initial monitoring data to obtain the target monitoring data; The state determination module compares the target soil volumetric moisture content data with the target moisture content range to obtain the moisture content comparison result. Based on the evapotranspiration consumption trend obtained from the target meteorological data and the comparison results of the target salinity and electrical conductivity data, the moisture content comparison result is corrected to obtain the moisture correction result. The target fertilizer concentration data is compared with the target fertilizer curve to obtain the fertilizer comparison result. Based on the moisture correction result and the fertilizer comparison result, water and fertilizer state classification data is obtained. The community division and aggregation module divides the communities with consistent ground pipe and valve layouts corresponding to the water and fertilizer status classification data into water-deficient and fertilizer-deficient communities, water-deficient and fertilizer-appropriate communities, water-appropriate and fertilizer-deficient communities, water-appropriate and fertilizer-appropriate communities, and overly wet and overly fertilizer-appropriate communities, obtaining corresponding sets. The corresponding sets are then aggregated to obtain the water and fertilizer control parameter values ​​for each community with consistent ground pipe and valve layouts. The historical comparison and correction module compares the water and fertilizer regulation parameter values ​​with historical water and fertilizer regulation parameter values. Based on the comparison results, target meteorological data, target growth period data, and target fertilizer concentration data, the water and fertilizer regulation parameter values ​​are corrected to obtain the target water and fertilizer regulation parameter values. The difference statistics module statistically analyzes the differences between the distribution of each community in the corresponding set and the water and fertilizer status classification data to obtain correction parameter labels. The correction parameter labels include irrigation insufficiency correction parameter labels, fertilizer concentration deviation correction parameter labels, and drainage lag correction parameter labels. The correction value generation module statistically analyzes the occurrence of various correction parameters in all communities with consistent ground pipeline and valve layouts to obtain the correction parameter values ​​for the target water and fertilizer regulation parameters. The prescription set generation module manages water and fertilizer in different zones based on target water and fertilizer regulation parameter values ​​and correction parameter values.

[0044] Understandably, the system uses a modular structural design to connect key aspects of crop zoning management, such as data collection, processing, judgment, correction, and prescription generation, forming a complete intelligent water and fertilizer regulation chain. The data acquisition and preprocessing modules ensure the comprehensiveness and reliability of the input data, avoiding distorted judgments due to missing monitoring or noise interference. The status determination module outputs water and fertilizer status classification data based on multi-source information fusion, providing accurate basis for subsequent plot division. The plot division and aggregation module combines crop needs and actual differences to map different status plots to corresponding sets, and then generates water and fertilizer regulation parameter values ​​through aggregation processing, ensuring the refinement of zoning management. The historical comparison and correction module uses historical parameter trajectories and current weather, growth stage, and fertilizer concentration information for comparative analysis to achieve dynamic parameter correction, improving the timeliness and adaptability of regulation results. The difference statistics module and correction value generation module further correct the impact of single-cycle deviations on the overall strategy by analyzing the differences in plot distribution and quantifying parameter labels, enhancing the robustness of the system. The prescription set generation module integrates the finally corrected parameters into a zoning water and fertilizer management method, clarifying the irrigation, fertilization, or drainage operation instructions for different status plots, and obtaining control basis that can directly guide field operations. Thus, the system realizes automated decision-making and refined regional control of water and fertilizer input, which not only improves water and fertilizer utilization efficiency, but also reduces fluctuations caused by human subjective factors, ensuring that the water and fertilizer supply of crops at different growth stages is more in line with actual needs, thereby promoting stable and high crop yields and improving resource utilization efficiency.

[0045] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, executed by the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that, when performed on the computer or other programmable apparatus, provide for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A crop zoning precision management method based on intelligent water and fertilizer integration, characterized in that, The method comprises the following steps: dividing the planting area into a plurality of ground pipes and valve arrangement consistent plots, collecting a plurality of original data in each ground pipe and valve arrangement consistent plot, and obtaining initial monitoring data; preprocessing the initial monitoring data to obtain target monitoring data; comparing the target soil volume water content data with the target water content range to obtain a water content comparison result, correcting the water content comparison result according to the evapotranspiration consumption trend obtained from the target meteorological data and the comparison result of the target salt and conductivity data, obtaining a water correction result, comparing the target fertilization concentration data with the target fertilization curve to obtain a fertilization comparison result, and obtaining water and fertilizer state classification data according to the water correction result and the fertilization comparison result; dividing the ground pipe and valve arrangement consistent plot corresponding to the water and fertilizer state classification data into a water and fertilizer shortage plot, a water shortage and suitable fertilizer plot, a suitable water and fertilizer shortage plot, a suitable water and fertilizer plot, and an overwatering and overfertilization plot, obtaining a corresponding set, and performing aggregation processing on the corresponding set to obtain water and fertilizer control parameter values of each ground pipe and valve arrangement consistent plot; comparing the water and fertilizer control parameter values with historical water and fertilizer control parameter values, correcting the water and fertilizer control parameter values according to the comparison result and the target meteorological data, target growth period data and target fertilization concentration data, and obtaining target water and fertilizer control parameter values; statistically analyzing the difference between the distribution of each plot in the corresponding set and the water and fertilizer state classification data to obtain correction parameter markers, wherein the correction parameter markers include irrigation deficiency correction parameter markers, fertilization concentration deviation correction parameter markers and drainage lag correction parameter markers; statistically analyzing the occurrence of each type of correction parameter marker in all ground pipe and valve arrangement consistent plots to obtain correction parameter values of the target water and fertilizer control parameter values; managing the partitioned water and fertilizer based on the target water and fertilizer control parameter values and the correction parameter values. 2.The crop zoning precision management method based on smart water and fertilizer integration according to claim 1, characterized in that, When collecting the initial monitoring data, the method comprises the following steps: setting monitoring points in each ground pipe and valve arrangement consistent plot according to a preset spatial layout rule, wherein the spatial layout rule is to set soil volume water content monitoring points and root zone salt and conductivity monitoring points inside each ground pipe and valve arrangement consistent plot, and to set check monitoring points on the plot boundary; collecting soil volume water content original data, root zone salt and conductivity original data, meteorological original data, crop growth period original data and fertilization concentration original data according to a sampling period, wherein the sampling period is determined according to the key growth stage of the target crop, the sampling period is divided into intensive sampling and regular sampling, the intensive sampling is to shorten the sampling interval to the hour level and to obtain data within a continuous time period, and the regular sampling is to obtain data at a daily interval; triggering intensive sampling according to the time period after the rainfall process or irrigation is completed, shortening the sampling interval and continuously collecting the above-mentioned types of original data until the soil volume water content and evapotranspiration index return to the stable change range; transmitting the collected types of original data to an Internet of Things master control through wireless transmission, performing time stamp calibration and ground pipe and valve arrangement consistent plot number matching, and obtaining initial monitoring data. 3.The crop zoning precision management method based on smart water and fertilizer integration according to claim 2, characterized in that, When preprocessing the initial monitoring data, the method comprises the following steps: The records exceeding the sensor range limit value in the soil volume water content original data, and the records with a difference value exceeding the maximum allowed change amplitude determined based on the statistical distribution of historical monitoring data in adjacent time periods are removed, missing entries are interpolated, and target soil volume water content data is obtained; The records exceeding the sensor range limit value in the meteorological original data are removed or the records with inconsistent units are converted, the maximum allowed change amplitude of the adjacent time period difference value is used for time series smoothing of abnormal jumps, and target meteorological data is obtained; The records with negative values, exceeding the sensor range limit value, or exceeding the maximum allowed change amplitude in the root zone salt and conductivity original data are removed, and the sliding average with a window length not less than three sampling intervals is used for smoothing, and target salt and conductivity data is obtained; The sequences with baseline drift in the fertilization concentration original data are corrected according to the baseline alignment of the stable section, and the sequences with observation interruption are segmented and spliced according to the timestamp order, and target fertilization concentration data is obtained; The crop growth period original data is corresponded according to the timestamp and the ground pipeline and valve arrangement consistent plot number, and asynchronous records are resampled according to the sampling period, and target growth period data is obtained; The target soil volume water content data, target meteorological data, target salt and conductivity data, target fertilization concentration data, and target growth period data are aligned at a unified time granularity, and corresponded to the ground pipeline and valve arrangement consistent plot number, and target monitoring data is obtained.

4. The crop zoning precision management method based on intelligent water and fertilizer integration according to claim 3, characterized in that, In the comparison and correction, including: The target soil volume water content data is compared with the minimum and maximum values of the target water content range point by point, and the water content comparison result is obtained, which is marked as low water content below the minimum value, as appropriate water content between the minimum value and the maximum value, and as high water content above the maximum value; When the evapotranspiration consumption trend of the target meteorological data shows an upward trend and reaches the maximum upward amplitude determined according to the historical change distribution, the low water content is incrementally corrected, and the evapotranspiration correction result is obtained; When the evapotranspiration consumption trend of the target meteorological data shows a downward trend and reaches the maximum downward amplitude determined according to the historical change distribution, the high water content is decrementally corrected, and the evapotranspiration correction result is obtained; The evapotranspiration correction result is compared with the target salt and conductivity data, the appropriate water content is adjusted to low water content when the salt and conductivity are in the high salt interval, and the appropriate water content is adjusted to high water content when the salt and conductivity are in the low salt interval, and the water correction result is obtained; The target fertilization concentration data is compared with the minimum and maximum values of the target fertilization curve point by point, and the fertilization comparison result is obtained, which is marked as low concentration below the minimum value, as appropriate concentration between the minimum value and the maximum value, and as high concentration above the maximum value; The water and fertilizer state classification data is obtained according to the corresponding relationship of the water correction result and the fertilization comparison result.

5. The crop zoning precision management method based on intelligent water and fertilizer integration according to claim 4, characterized in that, In the division of the water and fertilizer state classification data, including: The ground pipeline and valve arrangement consistent plot with low water content and low concentration in the water and fertilizer state classification data is divided into a water and fertilizer deficient plot. The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; 6.The crop zoning precision management method based on smart water and fertilizer integration according to claim 5, characterized in that, The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; 7.The crop zoning precision management method based on smart water and fertilizer integration according to claim 6, characterized in that, The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; 8.The crop zoning precision management method based on smart water and fertilizer integration according to claim 7, characterized in that, The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided into a water-deficient and fertilizer-sufficient zone according to the ground pipeline and valve arrangement; The low-water and low-fertilizer zone marked in the water and fertilizer state classification data is divided The distribution difference result is obtained by comparing the number of the water and fertilizer deficient area, the water deficient and suitable fertilizer area, the suitable water and fertilizer deficient area, the suitable water and fertilizer area, and the over wet and over fertilizer area with the overall distribution number of the water and fertilizer state classification data; When the distribution difference result shows that the number proportion of the water and fertilizer deficient area or the water deficient and suitable fertilizer area is higher than the water deficient proportion of the overall distribution, the irrigation deficiency correction parameter mark is obtained for the corresponding area; When the distribution difference result shows that the number proportion of the water and fertilizer deficient area or the suitable water and fertilizer deficient area is higher than the low concentration proportion of the overall distribution, the fertilizer concentration deviation correction parameter mark is obtained for the corresponding area; When the distribution difference result shows that the number proportion of the suitable water and fertilizer area or the over wet and over fertilizer area is higher than the high water content proportion of the overall distribution, the drainage delay correction parameter mark is obtained for the corresponding area. 9.The crop zoning precision management method based on smart water and fertilizer integration according to claim 8, characterized in that, In the correction parameter value of the target water and fertilizer regulation parameter value and the determination of the partition water and fertilizer management method, it includes: The irrigation deficiency correction parameter mark, the fertilizer concentration deviation correction parameter mark and the drainage delay correction parameter mark of each ground pipeline and valve arrangement consistent area are counted, and the quantitative correction coefficient is converted according to the statistical proportion, so as to obtain the correction parameter value of the target water and fertilizer regulation parameter value; The target water and fertilizer regulation parameter value and the correction parameter value are superimposed and corrected to obtain the corrected target water and fertilizer regulation parameter value; The corrected target water and fertilizer regulation parameter value is integrated according to the area type to obtain the partition water and fertilizer management method, which includes adjusting the irrigation amount and fertilizer amount parameters of the water and fertilizer deficient area, adjusting the irrigation amount parameters of the water deficient and suitable fertilizer area, adjusting the fertilizer amount parameters of the suitable water and fertilizer deficient area, adjusting the maintenance parameters of the suitable water and fertilizer area, and adjusting the drainage amount parameters of the over wet and over fertilizer area.

10. A crop zoned precision management system based on intelligent water and fertilizer integration, used to realize the crop zoned precision management method based on intelligent water and fertilizer integration according to any one of claims 1-9, characterized in that, It includes: The data acquisition module divides the planting area into a plurality of ground pipeline and valve arrangement consistent areas, and acquires a plurality of original data in each ground pipeline and valve arrangement consistent area to obtain initial monitoring data; The data preprocessing module pre-processes the initial monitoring data to obtain target monitoring data; The state determination module compares the target soil volume water content data with the target water content range to obtain a water content comparison result, corrects the water content comparison result according to the evapotranspiration consumption trend obtained from the target meteorological data and the comparison result of the target salinity and conductivity data, and compares the target fertilizer concentration data with the target fertilizer curve to obtain a fertilizer comparison result, and obtains the water and fertilizer state classification data according to the water content correction result and the fertilizer comparison result; The area division and collection module divides the ground pipeline and valve arrangement consistent area corresponding to the water and fertilizer state classification data into a water and fertilizer deficient area, a water deficient and suitable fertilizer area, a suitable water and fertilizer deficient area, a suitable water and fertilizer area, and an over wet and over fertilizer area, obtains a corresponding collection, and aggregates the corresponding collection to obtain the water and fertilizer regulation parameter value of each ground pipeline and valve arrangement consistent area; The area division and collection module divides the ground pipeline and valve arrangement consistent area corresponding to the water and fertilizer state classification data into a water and fertilizer deficient area, a water deficient and suitable fertilizer area, a suitable water and fertilizer deficient area, a suitable water and fertilizer area, and an over wet and over fertilizer area, obtains a corresponding collection, and aggregates the corresponding collection to obtain the water and fertilizer regulation parameter value of each ground pipeline and valve arrangement consistent area; The history comparison correction module compares the water and fertilizer regulation parameter value with a historical water and fertilizer regulation parameter value, corrects the water and fertilizer regulation parameter value according to a comparison result and target meteorological data, target growth period data and target fertilization concentration data, and obtains a target water and fertilizer regulation parameter value; The difference statistics module statistically analyzes differences between distributions of each plot in a corresponding set and water and fertilizer state classification data, and obtains correction parameter markers, including irrigation deficiency correction parameter markers, fertilization concentration deviation correction parameter markers and drainage delay correction parameter markers; The correction value generation module statistically analyzes occurrence conditions of each type of correction parameter marker in all ground pipe and valve arrangement consistent plots, and obtains correction parameter values of the target water and fertilizer regulation parameter value; The prescription set generation module manages partitioned water and fertilizer based on the target water and fertilizer regulation parameter value and the correction parameter values.

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