Intelligent regulation and control system for agricultural water-saving irrigation

Abstract

The invention relates to the technical field of intelligent irrigation, in particular to an agricultural water-saving irrigation intelligent regulation and control system which comprises a growth stage recognition module, a moisture monitoring module, a meteorological data synchronization module, an irrigation strategy scheduling module and a feedback correction module. According to the method, high-precision identification of crop stage states, improvement of stage judgment accuracy, screening of key growth period blocks, focusing of regulation and control targets, improvement of resource utilization efficiency, layered acquisition and trend comparison of humidity data and construction of a deep and shallow layer dynamic response structure are realized through time sequence analysis of rice key growth parameter amplification; the method comprises the following steps of: enhancing moisture change resolution, synchronously fitting a humidity rhythm and weather prediction, improving regulation and control perspectiveness, eliminating a rainfall interference window, combining with a water demand gear, realizing block precise irrigation, feeding back a regulation and control result through double indexes of humidity and leaf chromatic aberration, constructing set mapping, forming a closed-loop regulation and control mechanism, and enhancing adaptability and execution precision.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent irrigation, and particularly to an intelligent control system for agricultural water-saving irrigation. Background Art

[0002] The technical field of intelligent irrigation includes related technologies for precise management and allocation of water resources in agricultural production. The core content of this technical field is to achieve efficient utilization of water resources through real-time monitoring and data analysis of environmental factors, crop water demand characteristics, and the operating status of irrigation systems. An intelligent irrigation system generally combines means such as meteorological monitoring, soil moisture detection, water volume control, and remote communication to build a control system that can automatically make decisions and execute irrigation tasks. The entire technical field covers the collaborative work of the perception layer, control layer, and execution layer, involving data collection of environmental perception devices, data interaction of information transmission technologies, and regulation of irrigation execution devices, etc., with the characteristics of data-driven, dynamic regulation, and system integration.

[0003] Among them, the intelligent control system for agricultural water-saving irrigation refers to an irrigation control system constructed by means of environmental data collection, moisture assessment, crop demand analysis, and water supply scheduling in response to problems such as water resource waste and inaccurate irrigation timing in agricultural water management. This system mainly includes real-time collection of surface and root layer moisture data through soil humidity sensing elements, calculation of water demand based on preset models according to crop types and growth stages, and dynamic correction in combination with meteorological parameters such as rainfall prediction and evapotranspiration intensity. Then, the actuator controls the opening and closing of the valve to allocate water sources. By integrating the geographical location marking function and the original data backtracking algorithm, the moisture distribution trend in a specific area is analyzed and judged, so as to achieve intelligent formulation of irrigation decisions. In the whole process, various control tasks are completed relying on methods such as sampling analysis, parameter determination, and threshold comparison.

[0004] The prior art generally relies on static modeling methods based on crop types and growth periods to evaluate water requirements, lacking dynamic analysis of real-time growth status. As a result, in the context of large actual differences in different regions, it is impossible to accurately correspond to the current stage of crops, causing mis-timing or over-frequency of stage irrigation. Humidity detection generally focuses on the soil surface layer, lacking a response analysis mechanism for multi-layer structures, making the regulation behavior based on a single data dimension and difficult to reflect the real water infiltration state, increasing the risk of water excess or irrigation lag. Although meteorological factors are included in the regulation basis, they are more treated as independent variables and no linkage prediction model with soil humidity is constructed, making it difficult to adjust the irrigation rhythm in a timely manner when precipitation or wind changes violently. In terms of implementation strategies, regional averaging or fixed-cycle irrigation is mostly adopted, lacking block-based intervention and dynamic adjustment strategies, resulting in insufficient accuracy of water resource allocation. Most feedback mechanisms only stay in the inspection of humidity changes after the execution is completed, and no mapping chain between the regulation setting and the feedback state is established, making it impossible to classify and trace the source of abnormal blocks and optimize and correct them, thus limiting the adaptive optimization ability and closed-loop control efficiency of the control. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art and propose an intelligent control system for agricultural water-saving irrigation.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The intelligent control system for agricultural water-saving irrigation includes: The growth stage identification module obtains agricultural irrigation regulation block data, including the leaf width of rice, the internode length of plants, and the tiller density, compares with the crop growth stage benchmark sequence, judges the stage interval to which the current sample set belongs, screens the blocks distributed between the tillering stage and the heading stage, and generates a stage block identification map; The water monitoring module, based on the stage block identification map, collects the humidity readings of the plough layer and the bottom layer of the corresponding block, compares the change directions and amplitude differences of the two-layer data according to the sampling time series, identifies the dynamic humidity change characteristics within the stage block, and generates a stratified humidity distribution state; The meteorological data synchronization module, based on the stratified humidity distribution state, identifies the recent precipitation forecast sequence and wind speed increasing sequence in the region, synchronously matches the humidity change rhythm of the current block with the predicted meteorological elements, calculates the coincidence ratio for the overlapping fluctuation intervals, and generates a water-vapor interaction change trend; The irrigation strategy scheduling module calls the water-vapor interaction change trend, classifies the stage block map, eliminates the areas overlapping with the precipitation forecast window, compares the block humidity readings with the stage water requirement levels, marks the time periods and regions to be regulated, and generates an irrigation intervention interval configuration table.

[0007] As a further solution of the present invention, the stage block recognition map includes growth stage classification labels, block time series indexes, tillering and heading block identification information. The hierarchical humidity distribution state includes the amplitude of the tillage layer humidity change, the bottom layer humidity response delay, and the interlayer humidity difference characteristics. The water-vapor interaction change trend includes the humidity fluctuation coincidence rate, the precipitation prediction matching interval, and the wind speed increasing interference factor. The irrigation intervention interval configuration table includes the regulation block number, the target humidity gear, and the intervention time window.

[0008] As a further solution of the present invention, the growth stage recognition module includes: The broadening parameter extraction sub-module obtains agricultural irrigation regulation block data, identifies the broadening change sequence through the same block time series, calculates the growth rate and compares it with the set reference interval, extracts the irrigation control blocks that meet the broadening dynamic characteristics, and generates a set of broadening feature response block numbers; The internode section determination sub-module calls the set of broadening feature response block numbers, extracts the internode length and tillering density data, identifies the internode growth rate and the tillering density increase value, screens the block numbers and time periods that are simultaneously in the double intervals of internode elongation and density increase, and generates a set of water-saving regulation sensitive section identifiers; The growth block determination sub-module, according to the set of water-saving regulation sensitive section identifiers, compares the tillering density and the growth trend of the time series nodes in the block, matches the stage reference growth range of the tillering stage and the heading stage, extracts the effective blocks and spatial positions within the stage range, and generates a stage block recognition map.

[0009] As a further solution of the present invention, the water monitoring module includes: The tillage layer humidity acquisition sub-module, based on the stage block recognition map, acquires the tillage layer and bottom layer humidity readings, unifies the sampling time series, performs abnormal elimination and missing segment repair on the block data, extracts effective humidity samples, and generates a block humidity monitoring basic set; The humidity change comparison sub-module calls the block humidity monitoring basic set, extracts the tillage layer and bottom layer humidity change values in adjacent time periods, performs a paired comparison on the humidity change directions and increase and decrease amplitudes of the two layers, identifies the coupling characteristics, and generates a set of coupling change coefficients; The hierarchical humidity modeling sub-module calls the set of coupling change coefficients, selects the blocks with stable trends, extracts the tillage layer humidity increase and decrease amplitude and the bottom layer change rate, combines the sampling time interval, sequence position, and number of sample points, calculates the hierarchical humidity regulation index value, and maps it in combination with the spatial range and time node of the irrigation operation block to generate a hierarchical humidity distribution state.

[0010] As a further solution of the present invention, the meteorological data synchronization module includes: Based on the hierarchical humidity distribution status, the humidity recognition sub-module extracts the humidity data of each layer, combines the precipitation forecast and the wind speed sequence, analyzes the humidity change rhythm and the fluctuation amplitude of meteorological elements, determines whether the difference between the two is within the humidity matching threshold range, screens the qualified number sequences, and generates an irrigation intervention warning number group; According to the irrigation intervention warning number group, the overlapping section determination sub-module selects the wind speed increase and precipitation fluctuation information in the corresponding time period, identifies the overlapping time section of the two types of meteorological data, calculates the wind-precipitation coupling deviation degree, screens the time section numbers exceeding the interaction response limit, and obtains the number set of the sections to be regulated and responded; The regulation trend derivation sub-module calls the number set of the sections to be regulated and responded, targets the section humidity and wind-precipitation interaction data, extracts the rhythm overlapping positions, calculates the humidity adjustment differences of each type of interaction combination in groups, classifies them into intervention regulation levels, summarizes the level change trajectories, and generates the water-vapor interaction change trend.

[0011] As a further solution of the present invention, the irrigation strategy scheduling module includes: The trend classification sub-module calls the water-vapor interaction change trend, extracts the meteorological change directions and amplitude values of the regions in the stage block map, divides the trend types according to the judgment of continuity and gradient changes, eliminates the block numbers that overlap with the precipitation forecast window in the time period, and generates a set of adjustable trend blocks; The humidity comparison sub-module calls the set of adjustable trend blocks, collects the current soil humidity readings of the blocks and the standard range of water demand levels in the corresponding stages, compares the difference between the humidity readings and the lower limit of water demand for each block, calculates the irrigation intervention priority value, sorts the block numbers, and obtains the sequence of blocks with priority for regulation; According to the block numbers in the sequence of blocks with priority for regulation, the intervention interval recognition sub-module extracts the corresponding regulation time intervals, sets the intervention levels and irrigation time configurations in grades according to the continuous deviation of humidity, and generates an irrigation intervention interval configuration table.

[0012] As a further solution of the present invention, the system further includes a feedback correction module: According to the irrigation intervention interval configuration table, the feedback correction module detects the change amplitudes of the shallow layer humidity readings and the leaf color difference indicators after irrigation, screens and classifies the humidity recovery amplitudes of the blocks deviating from the regulation expectations, marks the corresponding regulation settings of the deviating blocks, and generates a stage regulation deviation label; The stage regulation deviation label includes the humidity recovery deviation level, the change amplitude of the leaf color difference, and the regulation setting deviation mark.

[0013] As a further solution of the present invention, the feedback correction module includes: The humidity fluctuation screening sub-module extracts the end time of block irrigation according to the irrigation intervention interval configuration table, identifies the shallow layer humidity data at the corresponding time, analyzes the difference between the humidity recovery amplitude and the regulation target value, screens the blocks that exceed the recovery deviation threshold, and generates a humidity regulation deviation block set; The leaf color difference measurement sub-module extracts the color difference readings of the leaves of the corresponding blocks before and after irrigation based on the humidity regulation deviation block set, analyzes the change amplitude of the color difference, and classifies them according to the ratio of the color difference change value to the humidity recovery deviation value, screens the blocks with inconsistent responses, and generates a water-photosynthesis imbalance distribution group; The regulation offset identification sub-module extracts the corresponding regulation parameters according to the water-photosynthesis imbalance distribution group and compares them with the regulation target in the configuration table, marks the blocks and set parameters with set offsets, and generates a stage regulation offset label.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the time-axis sequence analysis of the increase trend of the key growth parameters of rice, the high-precision identification of the crop stage state can be realized, breaking through the problem of stage judgment errors caused by relying on static models or coarse-grained time division in the past. Screening the blocks concentratedly distributed at the key growth nodes can effectively focus on the target range to be regulated, reduce resource waste and redundant judgment interference. The vertical stratified collection and trend comparison of humidity data establish a dynamic response structure based on the difference between the shallow and deep layers, enhance the ability to analyze water changes, avoid misjudgment caused by single-layer indicators, and based on the synchronous fitting mechanism of humidity data rhythm and meteorological prediction elements, strengthen the system's response prediction ability to future short-term fluctuations, and improve the forward-looking and stability of humidity regulation. By removing the overlapping areas of precipitation interference windows, the independence and accuracy of water resource input are guaranteed, and further combined with the stage water demand level, the spatio-temporal accuracy of regulation behavior is limited, realizing the block-based and high-frequency precise matching of irrigation tasks. The regulation results are feedback-evaluated by the double indicators of humidity and leaf color difference, and the traceable set mapping is marked for the deviation situation, enhancing the closed-loop controllability of regulation behavior and the ability to identify the optimization path. The overall process starts from stage identification, runs through hierarchical perception, dynamic prediction, strategy allocation and result feedback, forming a full-cycle closed-loop regulation mechanism with the collaborative characteristics of forward determination and backward verification, and strengthening the comprehensive performance of irrigation execution in terms of accuracy, timeliness and adaptability. Brief Description of the Drawings

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the growth stage identification module in the present invention; Figure 3 is the flow chart of the water monitoring module in the present invention; Figure 4 is the flow chart of the meteorological data synchronization module in the present invention; Figure 5 This is the flowchart of the irrigation strategy scheduling module in the present invention; Figure 6 This is the flowchart of the feedback correction module in the present invention. Detailed implementation manners

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the intelligent control system for agricultural water-saving irrigation includes: The growth stage identification module obtains agricultural irrigation control block data, including the leaf width of rice, the internode length of plants and the tiller density. According to the increase relationship of each data within the time axis, it compares with the crop growth stage reference sequence, determines the stage interval to which the current sample set belongs, screens the blocks distributed between the tillering stage and the heading stage, and generates a stage block identification map; The moisture monitoring module, based on the stage block identification map, collects the humidity readings of the plough layer and the bottom layer of the corresponding block, compares the change direction and amplitude difference of the two-layer data according to the sampling time series, identifies the dynamic change characteristics of the humidity within the stage block, and generates a stratified humidity distribution state; The meteorological data synchronization module, based on the stratified humidity distribution state, identifies the recent precipitation forecast sequence and wind speed increasing sequence in the region, synchronously matches the humidity change rhythm of the current block with the predicted meteorological elements, calculates the coincidence ratio for the overlapping fluctuation intervals, and generates a water-vapor interaction change trend; The irrigation strategy scheduling module invokes the water-vapor interaction change trend, classifies the stage block map, eliminates the regions overlapping with the precipitation forecast window, compares the block humidity readings with the stage water demand levels, marks the time periods and regions to be regulated, and generates an irrigation intervention interval configuration table; The feedback correction module, based on the irrigation intervention interval configuration table, detects the change ranges of the shallow moisture readings and the leaf color difference indicators after irrigation ends, screens and classifies the humidity recovery ranges in the blocks that deviate from the regulation expectation blocks, marks the corresponding regulation settings of the deviated blocks, and generates stage regulation offset labels.

[0019] The stage block identification map includes growth stage classification labels, block time series indexes, tillering and heading block identification information. The layered humidity distribution state includes the change amplitude of the tillage layer humidity, the response delay of the bottom layer humidity, and the interlayer humidity difference characteristics. The water-vapor interaction change trend includes the humidity fluctuation coincidence rate, the precipitation prediction matching interval, and the wind speed increasing interference factor. The irrigation intervention interval configuration table includes the regulation block number, the target humidity gear, and the intervention time window. The stage regulation offset label includes the humidity recovery deviation level, the change amplitude of the leaf color difference, and the regulation setting offset mark.

[0020] Please refer to Figure 2 , the growth stage identification module includes: The broadening parameter extraction sub-module obtains the agricultural irrigation regulation block data, identifies the broadening change sequence through the same block time series, calculates the growth rate and compares it with the set benchmark interval, extracts the irrigation control blocks that meet the broadening dynamic characteristics, and generates a set of broadening feature response block numbers; Analyze the time series of the agricultural irrigation blocks to identify the broadening change sequence. The broadening change refers to the change of irrigation conditions or environmental factors within a certain time period in the irrigation blocks, which affects the growth environment of the irrigation area and causes changes in the control parameters or the size of the area. Specifically, when executing, first collect the original irrigation data of each block, including influencing factors such as soil humidity, temperature, and precipitation, conduct data analysis on the irrigation blocks at each time node, and identify the broadening change pattern of the block by comparing the irrigation areas and related environmental data in different time periods. For example, when the soil humidity drops sharply in a certain time period, the irrigation area will expand. Therefore, it is necessary to calculate the relative growth rate and then determine whether the block meets the broadening dynamic characteristics. The calculation of this growth rate can be obtained by comparing the changes in the irrigation area of the same block in different time periods. For example, if the irrigation area of a certain block increases from 10 hectares to 15 hectares, the growth rate is 50%. Compare the growth rate with the preset benchmark interval, and screen out the blocks that meet the broadening dynamic characteristics. Assume that the preset benchmark interval is 20%, then the blocks with a growth rate exceeding 20% will be selected and included in the set of broadening feature response block numbers.

[0021] The internode section determination sub-module calls the set of broadening feature response block numbers, extracts the internode length and tillering density data, identifies the internode growth rate and the tillering density increase value, screens the block numbers and time periods that are simultaneously in the double intervals of internode elongation and density increase, and generates a set of water-saving regulation sensitive section identification sets; Extract the internode length and tiller density data for each block. The internode length is related to the internode elongation during the plant growth cycle, while the tiller density directly affects the water absorption and distribution. By collecting data for each block at different time nodes, the internode growth rate and the increase value of the tiller density can be calculated. The calculation method of the internode growth rate is the difference between the current internode length and the internode length at the previous time node divided by the internode length at the previous time node. For example, if the internode length of a certain block increases from 5 cm to 6 cm between two measurements, the growth rate is (6 - 5) / 5 = 20%. The calculation method of the tiller density increase value is the difference between the tiller density at the current time node and the previous time node divided by the density at the previous time node. Suppose the tiller density of a certain block increases from 100 plants per square meter to 120 plants per square meter in two consecutive measurements, then the increase is (120 - 100) / 100 = 20%. Screen out the block numbers and corresponding time periods that are simultaneously in the double intervals of internode elongation and increasing tiller density. Set a reasonable interval value. For example, blocks with an internode growth rate greater than 10% and a tiller density increase greater than 15% will be identified as water-saving regulation sensitive sections, and finally form a water-saving regulation sensitive section identification set.

[0022] The growth block determination sub-module compares the increase trend of the tiller density in the block with the time sequence node according to the water-saving regulation sensitive section identification set, matches the stage benchmark increase range of the tillering stage and the heading stage, extracts the effective blocks and spatial positions within the stage range, and generates a stage block identification map; Combine the increase trend of the tiller density in each block at different stages to judge the growth state of the block. According to the water-saving regulation sensitive section identification set, identify the tiller density data of each block at different time periods, and compare according to the time sequence node. The time sequence node is the increase trend during a specific stage (such as the tillering stage, heading stage, etc.) and the target growth process. By comparing the increase trend of the tiller density with the time sequence node, it is judged whether the block has entered the tillering stage or the heading stage. For example, if the increase of the tiller density of a certain block in a specific time period exceeds the set benchmark increase (such as 10%) and conforms to the growth characteristics of this stage, then this block is determined as a growth stage block. It is necessary to define a clear benchmark increase range. For example, set the benchmark increase for the tillering stage to 8% - 12%, and the benchmark increase for the heading stage to 5% - 7%. And compare with the original data to ensure that the data of each block conforms to the stage characteristics. In this way, the blocks that meet the growth characteristics can be effectively screened out, and a stage block identification map can be generated to provide accurate irrigation regulation reference and ensure water saving and yield increase effects during the agricultural production process; Table 1: Example data of internode growth rate and tiller density increase ; As shown in Table 1, the table lists the data of internode lengths and tiller densities of certain blocks, as well as their corresponding growth rates and increase values. Through the data, blocks that meet the water-saving regulation sensitive sections can be further screened out to help accurately identify irrigation regulation strategies.

[0023] Please refer to Figure 3 , the moisture monitoring module includes: The tillage layer humidity acquisition sub-module acquires the humidity readings of the tillage layer and the bottom layer based on the stage block identification map, unifies the sampling time series, eliminates anomalies and repairs missing segments of the block data, extracts effective humidity samples, and generates the basic set of block humidity monitoring; Partition operations are carried out on the acquisition links of the tillage layer and the bottom layer humidity. The boundary coordinate values are called one by one according to the boundaries of the agricultural tillage blocks identified in the map, and in combination with the embedded soil humidity sensors, 4 tillage layer monitoring points are arranged in the range of 0–20 cm and 4 bottom layer monitoring points are arranged in the range of 20–50 cm in each block. The unified sampling period is set to 5 minutes. Data identification and synchronization are carried out based on the acquisition timestamp and block code. Outliers are eliminated from the original humidity data under each time period by setting filtering conditions. When the readings of two sampling points in the same block and the same layer exceed the set interval [the set humidity interval for the tillage layer is 5%–40%, and the set humidity interval for the bottom layer is 10%–45%], it is marked as an anomaly. The missing values in the anomaly segment are repaired by the linear interpolation method of the front and back points. The remaining valid data after elimination is archived in the order of sampling time. The archived data is grouped and sorted by block ID and layer to generate a distribution rate. The humidity distribution rate curve is obtained by calculating the ratio of the sampling frequency to the total number in each humidity value interval. For example, in a certain sampling period, the tillage layer humidity values are successively 15%, 16%, 15%, 14%, and the bottom layer is 28%, 27%, 29%, 27%. Combined with the total sample size and the distribution interval, it is divided into 5%–10%, 10%–15%, 15%–20%, etc. The corresponding humidity distribution rate is obtained by statistical frequency, and finally the basic set of block humidity monitoring is obtained.

[0024] The humidity change comparison sub-module calls the basic set of block humidity monitoring, extracts the humidity change values of the tillage layer and the bottom layer in adjacent periods, compares and matches the humidity change directions and increase and decrease amplitudes of the two layers, identifies the coupling characteristics, and generates a set of coupling change coefficients; Extract the humidity readings of the plough layer and the bottom layer in the same block between two consecutive periods. Use the increment and decrement method to calculate the change value of the plough layer humidity, which is the value obtained by subtracting the previous period from the current period. The same applies to the bottom layer. Record the increase and decrease amplitudes of the plough layer and the bottom layer respectively, and judge whether their change directions are consistent. The same direction means both are positive or both are negative, and the different direction means one is positive and the other is negative. Based on this setting, construct the direction consistency index, denoted as 0 or 1. If the directions are consistent, it is 1; otherwise, it is 0. Take the absolute value after subtracting the change values of the plough layer and the bottom layer as the humidity change difference value between the two layers. Set the coupling threshold to 5%, that is, when the change difference value is less than 5% and the directions are consistent, it is judged as a strong coupling state; otherwise, it is a weak coupling state. For example, in a certain block, the humidity of the plough layer changes from 20% to 23% in two periods t1 and t2, and the bottom layer changes from 30% to 31%. Then the direction consistency is 1, and the difference value is 3% - 1% = 2%, which is less than 5%, and it is recorded as a strong coupling. Then calculate the proportion of strong coupling for this block according to all times. If the proportion of the number of strong coupling state periods in the total number of periods is higher than 0.7, it is marked as a trend-stable block, and finally generate the coupling change coefficient set.

[0025] The stratified humidity modeling sub-module calls the coupling change coefficient set, selects the trend-stable blocks, extracts the increase and decrease amplitudes of the plough layer humidity and the change rate of the bottom layer, and combines the sampling time interval, sequence position, and number of sample points. Use the formula: ; Calculate the stratified humidity regulation index value, and map it in combination with the spatial range and time node of the irrigation operation block to generate the stratified humidity distribution state; Among them, represents the stratified humidity regulation index value, represents the amplitude of the change in the humidity of the plough layer in the th period of the th block, represents the change rate of the humidity of the bottom layer in the th period of the th block, represents the sum of the squares of the sampling time intervals of the th block, represents the average value of the sampling sequence positions of the th block, represents the number of valid sample points in the th block; The hierarchical humidity regulation index value is a numerical index that measures the coordination degree between the humidity changes of the agricultural plough layer and the underlying soil in a time series. This value is obtained through the weighted operation of the change range of the plough layer humidity and the change rate of the underlying humidity, and is normalized by combining structural parameters such as the sampling time interval, sequence position, and sample quantity. It reflects the conduction consistency and response synchronization of soil moisture in the vertical layer. The higher this value, the more consistent the humidity fluctuations in the plough layer and the underlying layer in the same period, and the more coupled the dynamic trend. It can be used as one of the bases for preferential water supply in zoning in intelligent irrigation. If the value is low, it indicates that the water response between the upper and lower layers is not synchronous, and the water allocation and irrigation intensity need to be adjusted to achieve precise control under the agricultural water-saving irrigation strategy; Extract the change range of the plough layer humidity and the change rate of the underlying humidity in each cycle of humidity samples. The change range of the plough layer humidity is obtained by subtracting the humidity value of the previous cycle from the plough layer humidity value of the current sampling cycle, taking the absolute value and retaining two decimal places. The humidity unit is percentage %, for example, the plough layer humidity readings of a certain block in the t1 - t5 cycles are 21.0%, 23.1%, 24.8%, 26.7%, 29.0% respectively, then the change ranges are 2.1%, 1.7%, 1.9%, 2.3% in sequence; The change rate of the underlying humidity is the difference between the underlying humidity of the current cycle and the previous cycle divided by the time interval , with the unit of % / h. Assume the underlying humidity is 30.0%, 30.8%, 32.2%, 34.1%, 36.0% in sequence, and the sampling time is once every 2 hours; Then the rates are in sequence: ; The obtained rate sequence is 0.4, 0.7, 0.95, 0.95% / h. To ensure the dimensional consistency and influence equivalence of each participating item, it is necessary to normalize each parameter, that is, subtract the minimum value from the change range of the plough layer and the rate of the underlying layer respectively and then divide by the range, so that the normalized data is distributed in the [0, 1] interval. For example, the range of the plough layer sequence is 2.3 - 1.7 = 0.6, and the minimum value is 1.7; After normalization, they are in sequence: ; The range of the underlying rate is 0.95 - 0.4 = 0.55, and the minimum value is 0.4. After normalization: ; Finally, substitute into the formula: ; Among them, is the normalized hierarchical humidity regulation index value, representing the dynamic coordination score of the tillage layer-bottom layer humidity. The value range is [0, 1]. The higher the value, the stronger the matching of the humidity change between the upper and lower layers, and it adapts to the water volume priority allocation area in the intelligent irrigation quantitative strategy. If the score is lower than 0.3, it is marked as a poor coordination area and local coupling optimization is required. Finally, the index value of all blocks is in one-to-one correspondence with their spatial positions to construct a water-saving irrigation regulation distribution map; : hierarchical humidity regulation index value (dimensionless, after normalization); : tillage layer humidity change range, unit %, after normalization; : bottom layer humidity change rate, unit % / h, after normalization; : sum of squares of the sampling time intervals of the block, unit h²; : mean value of the sampling sequence positions (i.e., the average of the sampling numbers); : number of effective sampling points (unitless); By coupling the synchronous dynamics of the tillage layer and bottom layer humidity and considering factors such as time and sample size, a block priority level mapping for guiding agricultural water-saving irrigation regulation can be reasonably generated. This result indicates that the current block has low coordination and it is advisable to make adjustments or re-lay sampling points.

[0026] Please refer to Figure 4 , and the meteorological data synchronization module includes: The humidity identification sub-module extracts the humidity data of each layer based on the hierarchical humidity distribution state, combines the precipitation forecast and wind speed sequence, analyzes the humidity change rhythm and the fluctuation range of meteorological elements, judges whether the difference between the two is within the humidity matching threshold interval, screens the qualified number sequences, and generates an irrigation intervention warning number group; Measured moisture data is collected from different soil layers, which can be set to three depths: 0-20cm, 20-40cm, and 40-60cm. Capacitive soil moisture sensors are deployed to regularly record the area's daily average humidity. Hourly precipitation forecast data for the next 72 hours, as well as wind speed change sequences, are retrieved from the meteorological department. During execution, the fluctuation trends of each layer's moisture data are compared with the precipitation forecast data on a daily basis to determine whether the humidity change rhythm is linked to precipitation or wind speed trends. The absolute value of the difference is calculated, and the humidity matching threshold is set to ±2%. When the difference in the change amplitude per unit time between the two sequences is less than 2%, the rhythm is considered consistent. Time points that meet the criteria are marked with numbers. For example, the humidity in a particular area varied by 32%, 34%, and 37% from May 10th to 12th, while the predicted precipitation was 0mm, 2mm, and 4mm, and the wind speed was 3m / s, 3.5m / s, and 4.2m / s, showing the same trend. The calculated differences were 2%, 2%, and 3%, respectively. The difference for the first two days was within the set threshold, so the number was assigned to the humidity rhythm matching number for the corresponding time period of the area. Similarly, all segment numbers that met the rhythm difference judgment criteria were aggregated and labeled. The ±2% threshold was constructed based on the difference in average response to evaporation loss between soil layers and wind speed disturbances. Based on years of field experimental records in the Hebei Plain, humidity changes of less than 2% do not cause crop water stress. Therefore, it has high adaptability as a synchronization judgment standard, ultimately resulting in an irrigation intervention warning number group.

[0027] The overlapping section determination submodule selects the wind speed increase and precipitation fluctuation information of the corresponding time period according to the irrigation intervention warning number group, identifies the overlapping time section of the two types of meteorological data, and uses the formula: ; Calculate the wind drop coupling offset, filter the time period numbers that exceed the interactive response limit, and obtain the response section number set that needs to be regulated; in, represents the windfall coupling offset, It represents the total amplitude of the wind speed sequence within the segment. Indicates the precipitation variation range value, is the fluctuation frequency of humidity rhythm, is the crossover value difference between wind speed and precipitation, is the humidity synchronization benchmark difference, is the segment superposition offset intensity factor; The wind-precipitation coupling deviation degree is a comprehensive index to measure the intensity of the combined influence of wind speed and precipitation on soil moisture change within a specific time period. Its core lies in quantifying and integrating the wind speed variation range, precipitation change amount, humidity fluctuation frequency, and their differences from the original humidity baseline to form a unified measurement value, which is used to reflect the necessity of meteorological disturbances on agricultural irrigation during this period. The higher the value, the stronger the disturbance of the combined action of wind and precipitation on humidity during this period, and the higher the irrigation response demand. This index ensures the consistency of dimensions during the calculation process by normalizing different physical quantities and superimposing them into a unified calculation model, facilitating comprehensive comparison between different meteorological elements, and providing a highly recognizable reference basis for subsequent irrigation decisions; Select the meteorological data for the time period corresponding to the marker number, extract the fluctuation data of its wind speed sequence and precipitation sequence, set the observation period corresponding to each group number as 24 hours, and respectively obtain the maximum and minimum values of wind speed and precipitation within each period and calculate their variation ranges, which serve as the input value sources for parameters a and b. For example, in the time period numbered 20240511_03, the wind speed increased from 2.8 m / s to 5.2 m / s, and the precipitation increased from 0 mm to 4.5 mm. The corresponding variation ranges are a = 2.4 m / s and b = 4.5 mm. The variance c of the humidity rhythm fluctuation frequency is obtained by extracting the hourly soil moisture change data in this numbered section and calculating the variance value of the fluctuation frequency. Assume that the humidity data sequence recorded during this period is: 31%, 33%, 35%, 36%, 38%, 37%, 36%, 34%. There are a total of 7 change points, and the frequency is 7 times. After being processed by the standard calculation formula, the variance is c = 2.1; Parameter d is the sequence difference at the intersection of wind speed and precipitation, and it needs to be processed after unifying the dimensions. Since the wind speed is in m / s and the precipitation is in mm / hour, normalization is required for cross-comparison. Set the normalization standard, normalize the wind speed to a maximum of 5 m / s, and normalize the precipitation to a maximum of 10 mm / h. Then, after normalization, the wind speed of 2.8 m / s and the precipitation of 2.4 mm / h are 0.56 and 0.24 respectively, and the corresponding intersection difference d = 0.32. The humidity synchronization baseline difference e is set to 0.15 with reference to the original sample. This value is obtained from the comparative analysis of the humidity fluctuations and meteorological parameters of regional spring wheat plots over 10 years, and the median range is between 0.12 and 0.18. Take its average value to determine; The superposition offset intensity factor f is not directly assigned as a constant, but is determined by the daily change rate of wind-precipitation fluctuations. If the change rate of wind speed or precipitation exceeds 50% within 6 hours, it is defined as a "strong disturbance", and its corresponding factor is set to 0.8. The medium disturbance (30% - 50%) is 0.6, and the weak disturbance (<30%) is 0.4. In this example, the wind speed changes from 2.8 to 5.2, and the change rate within 6 hours is 85.7%, which belongs to a strong disturbance. Therefore, f = 0.8; Substitute into the formula: ; The obtained wind-precipitation coupling offset is 59.62, which is much higher than the interactive response threshold value of 6.0. This result indicates that the co-variation of soil moisture caused by wind-precipitation changes during the corresponding time period of this number is strong. It needs to be marked as an irrigation response area during regulation. By introducing the sum term of wind speed and precipitation variation amplitude and combining with the frequency variation structure of humidity rhythm, the dimensional normalization and amplitude integration of the non-linear interaction relationship between different source terms are carried out, so that complex meteorological factors form a unified response evaluation scale in soil moisture regulation. The result value far exceeds the response threshold, indicating that active irrigation operation should be triggered in this area, and thus the set of section numbers that need to be regulated and responded to is obtained.

[0028] The regulation trend derivation sub-module calls the set of section numbers that need to be regulated and responded to, compares the humidity and wind-precipitation interaction data in the corresponding sections, extracts the rhythm overlapping positions, calculates the humidity adjustment differences of each type of interaction combination in groups, classifies them into intervention regulation levels, summarizes the level change trajectories, and generates the water-vapor interaction change trend. During the time period covered by the number, the corresponding humidity node values are retrieved, the numerical relationship between their time positions corresponding to the wind-precipitation interaction indicators is statistically analyzed, the rhythm overlapping points are further divided, and they are summarized according to different types of combined sections. Taking numbers 101, 103, and 106 as examples, their humidity node values are 35%, 38%, and 42% respectively, the corresponding wind speed changes are 4.0 m / s, 4.6 m / s, and 5.1 m / s, and the precipitation values are 3 mm, 4 mm, and 5 mm, which are classified as the humidity-wind speed increase-precipitation enhancement type combination. The repeated occurrence frequency of this type of combination is statistically analyzed under multiple numbers. If it repeats more than 10 times within 30 days, it can be initially designated as a significant interaction trend section. Then, the humidity difference values under this combination are calculated respectively, and the difference between the maximum and minimum humidity values within the group is used as the reference basis for the regulation level. For example, 42% - 35% = 7%, which belongs to the medium regulation level. The reference interval for level division is set as: 0 - 3% is weak, 3 - 7% is medium, and above 7% is strong. All level combinations are arranged in chronological order to generate the humidity regulation trend line, and the key change intervals are marked according to the fluctuation sections on the trend line, and finally the water-vapor interaction change trend is generated.

[0029] Please refer to Figure 5 , the irrigation strategy scheduling module includes: The trend classification sub-module calls the water-vapor interaction change trend, extracts the meteorological change direction and amplitude values in the regional phase block map, divides the trend types according to the judgment of continuity and gradient change, eliminates the section numbers where the time period coincides with the precipitation forecast window, and generates the set of adjustable trend blocks. The temperature, humidity and wind speed sequence data of different time periods are extracted from the block map, and a time series curve is formed for each block. The trend of the median value change of the data is used to determine whether the block has continuous fluctuations. For example, if the humidity of a block in the map shows an increasing trend within 48 hours and the amplitude value reaches 12%, it is recorded as a trend rising segment. Then, it is screened according to the set change amplitude threshold. The threshold is set to 10%, that is, if the difference between the maximum and minimum values of a block in a continuous time period exceeds this value, it is demarcated as a fluctuation segment. If it is less than the threshold, it is a stable segment. The change amplitude threshold is determined by the original precipitation response data. It is set to 10%. Taking the experimental results of a certain farmland as an example, blocks with humidity fluctuations above 10% are more susceptible to water vapor interference and produce irrigation responses, so this benchmark is set; after completing the preliminary trend judgment, based on the short-term precipitation forecast of the National Meteorological Center, the precipitation forecast time window of the next 24-72 hours for each block is extracted to determine whether it overlaps with the block map time period. If there is a time overlap, the corresponding block number is removed from the trend classification set, and finally a complete trend classification table is formed. After statistics, 18 controllable area numbers that do not overlap with the precipitation time window are screened out, and the controllable trend block set is obtained.

[0030] The humidity comparison submodule calls the adjustable trend block set, collects the current soil moisture reading of the block and the standard range of the water demand level in the corresponding stage, compares the difference between the humidity reading and the water demand lower limit by block, and uses the formula: ; Calculate the irrigation intervention priority value, sort the block numbers, and obtain the regulation priority block sequence; in, represents the humidity reading of the jth block, Represents the humidity reference value of the water demand level corresponding to the jth block, Represents the time span value in the j-th block graph, Represents the volatility value in the i-th block stage, is the number of blocks included in the comparison, is the irrigation intervention priority value; The irrigation intervention priority value is a quantitative scoring indicator calculated based on the difference between the current soil moisture and crop water requirements of each agricultural block, the normalized trend span, and the humidity fluctuation amplitude. It is used to measure whether a block is currently in urgent need of irrigation intervention. The larger the value, the more urgency of intervention is higher when the humidity is far below the water requirement limit and the fluctuation is severe. The priority value is calculated through steps such as unit unification, trend normalization, and volatility weighting. It is comparable and ranked. Ultimately, it is used in irrigation regulation to automatically identify and prioritize areas requiring water-saving irrigation control. Collect the current soil moisture sensor readings for the included areas in sequence. Such readings are uploaded regularly by sensors deployed 10 cm underground in the central area of the field plot, with the data unit being percentage (%). For example, the measured value of a certain block is 23.5%. Before data processing, the units and dimensions need to be unified. Since some records are expressed in volumetric water content (unit: cm³ / cm³), they are first uniformly converted to mass water content percentage (%). After unifying the format, it can be used as for participation in the operation; Call the standard water requirement level value of the corresponding crop in the current growth stage from the planting management platform. For example, the lower limit of water requirement for corn at the jointing stage is set at 30%, and this value is used as an item for comparison; Each block in the map is bound with time span information, recorded as item L, representing the sampling interval of the humidity change trend, with the unit being hours. The current sampling intervals are set as 48 hours, 72 hours, and 60 hours respectively; Volatility It is obtained by jointly evaluating the ratio of the maximum and minimum differences of the 48-hour humidity curve of each block and its standard deviation. For example, if the maximum humidity is 28% and the minimum is 20%, and the standard deviation is 3%, then the volatility is (28 - 20) / 3 = 0.22, and the unit is also percentage; After organizing all items, substitute them into the formula for normalization processing. The square root of the denominator is used to avoid the offset enhancement effect caused by different time scales, and the following calculation is carried out: ; The unit explanations of each parameter are as follows: : The measured humidity reading of the th block (unit: %), which is uploaded in real time by the sensor; : The lower limit standard value of the humidity in the current water requirement stage of the crop in the th block (unit: %), which is retrieved from the planting stage configuration database; : The trend sampling time period of the map of the th block (unit: hours), which is used for normalization and standardization processing; : The humidity change volatility of the th block (unit: %), which is obtained from the ratio of difference / standard deviation; : The calculated irrigation intervention priority value, which is a dimensionless scoring index for sorting; : The total number of blocks participating in the comparison, which is 3 in this example; The final result is 0.5342, which is used as the intervention priority score value to be input into the subsequent modules for sorting. This value ranks second among all current blocks and enters the first-level regulation queue, thus obtaining the regulation priority block sequence. This formula introduces the degree of water demand deviation while standardizing the time impact and considering the humidity fluctuation factor, providing a quantifiable reference for water-saving irrigation strategies.

[0031] The intervention interval identification submodule extracts the corresponding control period interval according to the block number in the control priority block sequence, sets the intervention level and irrigation period configuration according to the continuous humidity deviation, and generates an irrigation intervention interval configuration table; According to the top-ranked areas in the regulation priority block sequence, the regulation time period identifier of the block in the atlas is extracted, and the humidity change curve is called back for judgment. If the humidity deviation exceeds 10% in two consecutive time periods, it is judged as a continuous interference area. Based on this, the intervention time period is delineated and the irrigation level is assigned. The level is set according to the irrigation intervention priority value. If the priority is greater than 0.5, it is judged as a first-level intervention, otherwise it is a second-level intervention. Taking block 3 as an example, the volatility is 0.22% and the humidity deviation value is -10.6%. It is judged as a continuous deviation area with a level one level. Combined with the current irrigation calendar, the intervention time period is configured to be once every two hours, for a total of three times. The intervention block number, irrigation period and level are integrated to generate an irrigation intervention interval configuration table.

[0032] See also Figure 6 , the feedback correction module includes: The humidity fluctuation screening submodule extracts the block irrigation end time according to the irrigation intervention interval configuration table, identifies the shallow humidity data after the corresponding time, analyzes the difference between the humidity recovery amplitude and the control target value, screens the blocks that exceed the recovery deviation threshold, and generates a humidity control deviation block set; The irrigation end time for each block is obtained based on the irrigation intervention interval configuration table. The irrigation end time is extracted by searching the block's corresponding time stamp and matching it with the humidity change record in the data log to determine the block's irrigation end time. For example, if irrigation ends at 10:00 AM for a block, the shallow humidity data corresponding to the irrigation end time is extracted. Humidity data is recorded continuously. The magnitude of humidity recovery is calculated by comparing the humidity value after irrigation with the predetermined target value. For example, if the target humidity is 30% and the actual humidity is 25%, the humidity recovery magnitude is a 5% difference. This recovery magnitude is then compared with the set control target value. If the recovery magnitude exceeds a set threshold (for example, 3%), the block is identified as exceeding the recovery deviation. All blocks exceeding the threshold are aggregated to form a humidity control deviation block set, which is then used for subsequent humidity control analysis. The final result is a series of blocks with excessive humidity recovery deviations.

[0033] The leaf color difference measurement sub-module extracts the color difference readings of the leaves in the corresponding blocks before and after irrigation based on the humidity regulation deviation block set, analyzes the amplitude of the color difference change, and classifies them according to the ratio of the color difference change value to the humidity recovery deviation value, screening out the blocks with inconsistent responses to generate a water-photosynthesis imbalance distribution group; Extract the corresponding leaf color difference data from each block in the humidity regulation deviation block set. The color difference is obtained by analyzing the images of the leaves before and after irrigation. The color difference of the leaves before and after irrigation can be measured through image processing techniques (such as RGB color space difference or LAB color difference model). Suppose the color of the leaf before irrigation is RGB(45, 65, 32) and after irrigation is RGB(50, 70, 35), then the color difference is calculated as the change amount in the RGB space, and the amplitude of the color difference change is calculated. The amplitude reflects the response of the leaf to the water change. Suppose the amplitude of the color difference change is 5 units (RGB difference). Conduct a ratio analysis of the amplitude of the color difference change and the humidity recovery deviation value of the corresponding block, that is, the ratio of the amplitude of the color difference change to the humidity recovery deviation value. For example, suppose the amplitude of the color difference change is 5 and the humidity recovery deviation is 3, then the ratio is 5 / 3 = 1.67. Classify each block according to this ratio. If the ratio is large, it indicates that the response of the color difference change is relatively obvious, but the proportion is inconsistent with the humidity recovery amplitude. Such blocks will be screened as blocks with inconsistent responses and finally classified to generate a water-photosynthesis imbalance distribution group.

[0034] The regulation offset identification sub-module extracts the corresponding regulation parameters according to the water-photosynthesis imbalance distribution group and compares them with the regulation targets in the configuration table, marking the blocks with set offsets and the set parameters to generate stage regulation offset labels; Extract the regulation parameters of this block. The regulation parameters generally include factors such as water supplement amount, light intensity, temperature, etc. The regulation parameters need to be compared with the regulation targets in the configuration table. The configuration table contains preset standard values or target ranges. For example, the target water supplement amount is 50L and the target light intensity is 500lux. Compare the regulation parameters of each block with the target values in the configuration table. If the water supplement amount of this block is 60L and the light intensity is 450lux, exceeding the set water target or not meeting the light intensity standard, it is a regulation offset. By marking the blocks with set offsets and the corresponding parameters, this label can attach offset information to each block. For example, mark the water offset as +10L and the light offset as -50lux. The finally formed label can help analyze the stage regulation offset situation in the subsequent decision-making process. The final result is to generate the corresponding stage regulation offset label.

[0035] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent control system for agricultural water-saving irrigation, characterized in that, The system includes: The growth stage recognition module obtains agricultural irrigation regulation block data, including the leaf width of rice, the internode length of plants, and the tiller density, compares with the crop growth stage benchmark sequence, determines the stage interval to which the current sample set belongs, screens the blocks distributed between the tillering stage and the heading stage, and generates a stage block recognition map; The water monitoring module, based on the stage block recognition map, collects the humidity readings of the plough layer and the bottom layer of the corresponding block, compares the change directions and amplitude differences of the two-layer data according to the sampling time series, identifies the dynamic change characteristics of the humidity in the stage block, and generates a stratified humidity distribution state; The meteorological data synchronization module, based on the stratified humidity distribution state, identifies the recent precipitation forecast sequence and wind speed increasing sequence in the region, synchronously matches the humidity change rhythm of the current block with the predicted meteorological elements, calculates the coincidence ratio for the overlapping fluctuation interval, and generates a water-vapor interaction change trend; The irrigation strategy scheduling module calls the water-vapor interaction change trend, classifies the stage block map, eliminates the area overlapping with the precipitation forecast window, compares the block humidity readings with the water demand levels in the stage, marks the time periods and regions to be regulated, and generates an irrigation intervention interval configuration table.

2. The intelligent control system for agricultural water-saving irrigation according to claim 1, characterized in that, The stage block recognition map includes growth stage classification labels, block time series indexes, tillering and heading block identification information. The stratified humidity distribution state includes the change amplitude of the plough layer humidity, the response delay of the bottom layer humidity, and the humidity difference characteristics between layers. The water-vapor interaction change trend includes the humidity fluctuation coincidence rate, the precipitation prediction matching interval, and the wind speed increasing interference factor. The irrigation intervention interval configuration table includes the regulated block number, the target humidity level, and the intervention time window.

3. The intelligent control system for agricultural water-saving irrigation according to claim 1, wherein The growth stage recognition module includes: The width parameter extraction sub-module obtains agricultural irrigation regulation block data, identifies the width change sequence through the same block time series, calculates the growth rate and compares it with the set benchmark interval, extracts the irrigation control blocks that meet the width dynamic characteristics, and generates a set of width characteristic response block numbers; The internode section determination sub-module calls the set of width characteristic response block numbers, extracts the internode length and tiller density data, identifies the internode growth rate and the tiller density increase value, screens the block numbers and time periods that are simultaneously in the double intervals of internode elongation and density increase, and generates a set of water-saving regulation sensitive section identifiers; The growth block determination sub-module, according to the set of water-saving regulation sensitive section identifiers, compares the increase trends of the tiller density and the time sequence nodes in the block, matches the stage benchmark increase ranges of the tillering stage and the heading stage, extracts the effective blocks and spatial positions within the stage range, and generates a stage block recognition map.

4. The intelligent control system for agricultural water-saving irrigation according to claim 3, characterized in that The water monitoring module includes: The plough layer humidity collection sub-module, based on the stage block recognition map, collects the humidity readings of the plough layer and the bottom layer, unifies the sampling time series, eliminates anomalies and repairs missing segments in the block data, extracts effective humidity samples, and generates a basic set of block humidity monitoring; The humidity change comparison sub-module calls the basic set of block humidity monitoring, extracts the humidity change values of the plough layer and the bottom layer in adjacent time periods, compares and matches the change directions and increase and decrease amplitudes of the two-layer humidity, identifies the coupling characteristics, and generates a set of coupling change coefficients; The hierarchical humidity modeling sub-module calls the set of coupling change coefficients, selects blocks with stable trends, extracts the increase and decrease range of the tillage layer humidity and the change rate of the bottom layer, combines the sampling time interval, sequence position, and the number of sample points, calculates the hierarchical humidity regulation index value, and maps it in combination with the spatial range and time nodes of the irrigation operation block to generate the hierarchical humidity distribution state.

5. The agricultural water-saving irrigation intelligent regulation system according to claim 4, characterized in that The meteorological data synchronization module includes: The humidity identification sub-module, based on the hierarchical humidity distribution state, extracts the humidity data of each layer, combines the precipitation forecast and the wind speed sequence, analyzes the humidity change rhythm and the fluctuation range of meteorological elements, determines whether the difference between the two is within the humidity matching threshold range, screens the qualified number sequences, and generates an irrigation intervention warning number group; The overlapping section determination sub-module, according to the irrigation intervention warning number group, selects the wind speed increase and precipitation fluctuation information in the corresponding time period, identifies the overlapping time section of the two types of meteorological data, calculates the wind-precipitation coupling deviation degree, screens the time period numbers exceeding the interaction response limit, and obtains the set of numbers of the sections that need to be regulated and responded; The regulation trend derivation sub-module calls the set of numbers of the sections that need to be regulated and responded, compares the humidity and wind-precipitation interaction data in the target section, extracts the rhythm overlapping positions, calculates the humidity adjustment differences of each type of interaction combination in groups, classifies them into intervention regulation levels, summarizes the trajectory of level changes, and generates the water-vapor interaction change trend.

6. The intelligent control system for agricultural water-saving irrigation according to claim 5, characterized in that The irrigation strategy scheduling module includes: The trend classification sub-module calls the water-vapor interaction change trend, extracts the meteorological change direction and amplitude values of the regions in the stage block map, divides the trend types according to the judgment of continuity and gradient changes, eliminates the block numbers that overlap with the precipitation forecast window in the time period, and generates a set of adjustable trend blocks; The humidity comparison sub-module calls the set of adjustable trend blocks, collects the current soil humidity readings of the blocks and the standard range of water demand levels in the corresponding stage, compares the difference between the humidity readings and the lower limit of water demand for each block, calculates the irrigation intervention priority value, sorts the block numbers, and obtains the sequence of blocks with priority for regulation; The intervention interval identification sub-module, according to the block numbers in the sequence of blocks with priority for regulation, extracts the corresponding regulation time period interval, sets the intervention level and irrigation time period configuration in grades according to the continuous deviation of humidity, and generates an irrigation intervention interval configuration table.

7. The intelligent control system for agricultural water-saving irrigation according to claim 1, characterized in that, The system also includes a feedback correction module: The feedback correction module, according to the irrigation intervention interval configuration table, detects the change amplitude of the shallow layer humidity reading and the leaf color difference index after irrigation ends, screens and classifies the humidity recovery amplitude of the blocks that deviate from the regulation expectation, marks the corresponding regulation settings of the deviated blocks, and generates a stage regulation deviation label; The stage regulation deviation label includes the humidity recovery deviation level, the change amplitude of the leaf color difference, and the regulation setting deviation mark.

8. The intelligent control system for agricultural water-saving irrigation according to claim 7, wherein The feedback correction module includes: The humidity fluctuation screening sub-module, according to the irrigation intervention interval configuration table, extracts the irrigation end time of the block, identifies the shallow layer humidity data after the corresponding time, analyzes the difference between the humidity recovery amplitude and the regulation target value, screens the blocks that exceed the recovery deviation threshold, and generates a set of humidity regulation deviation blocks; The leaf color difference measurement sub-module extracts the color difference readings of the leaves in the corresponding blocks before and after irrigation based on the humidity regulation deviation block set, analyzes the amplitude of the color difference change, and classifies them according to the ratio of the color difference change value to the humidity recovery deviation value, screening out the blocks with inconsistent responses to generate a water-photosynthesis imbalance distribution group. The regulation offset identification sub-module extracts the corresponding regulation parameters according to the water-photosynthesis imbalance distribution group and compares them with the regulation targets in the configuration table, marks the blocks with set offsets and the set parameters, and generates stage regulation offset labels.

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