A method and system for integrated water, fertilizer and pesticide control based on drip irrigation heads

Abstract

This invention proposes an integrated water, fertilizer, and pesticide control method and system based on the drip irrigation head. By collecting real-time data on growing days and soil moisture, combined with dual-threshold cross-validation logic and a dynamic calibration mechanism, this invention accurately determines crop growth needs and water status, thereby determining the proportioning adjustment scheme and water volume adjustment values. Simultaneously, it utilizes a pressure regulating device and correlation weight analysis to ensure stable delivery pressure. Finally, through a flow control unit and a feedback closed-loop mechanism, it dynamically optimizes the mixed delivery formula to achieve precise distribution. The core innovation of this invention lies in the deep integration of growing days, soil moisture, and pressure control to form an adaptive adjustment closed loop. This not only improves resource utilization efficiency but also significantly enhances the stability of the crop growth environment, providing an efficient and intelligent control method for modern agricultural irrigation.

Description

Technical Field

[0001] This invention relates to the fields of agricultural fertilization, pesticide application and irrigation technology, and in particular to an integrated water, fertilizer and pesticide control method and system based on a drip irrigation head. Background Technology

[0002] In the scenario of precise regulation of integrated water, fertilizer and pesticide application in greenhouse drip irrigation, crop growth days serve as the core time axis driving parameter, which together with real-time soil moisture data constitutes the basis for dual-parameter decision-making. However, in actual operation, there are several interrelated technical contradictions.

[0003] First, the threshold grading table for the number of growing days is statically preset. However, the actual development of crops is affected by light, temperature, and variety differences, which often leads to deviations in the number of days. This causes the humidity requirement threshold range within the same growth stage to be misaligned with the actual requirement, triggering incorrect water and fertilizer regulation signals.

[0004] Secondly, soil moisture sensor data is affected by uneven spatial distribution and short-term fluctuations. A single moisture reading is difficult to accurately reflect the true moisture status of the crop root zone, which can easily lead to frequent misjudgments and excessive fine-tuning.

[0005] Furthermore, although dual-threshold cross-validation aims to improve reliability, there is a lack of effective dynamic coordination between the growth day ratio setting and the humidity abnormal fluctuation filtering mechanism. When the growth stage is critically switched, the judgment results of the two thresholds often conflict, resulting in unstable adjustment demand signals.

[0006] Furthermore, the fixed setting of the ratio adjustment step size and time delay parameters cannot adapt to the differences in the response speed of water and fertilizer at different growth stages, resulting in contradictions such as slow response in the early stage or excessive adjustment in the later stage.

[0007] Finally, although the pressure stabilization control operates independently, its output directly affects the accuracy of flow distribution. When pressure fluctuations and proportioning adjustment commands occur simultaneously, the actual delivery ratio of each component in the mixing device deviates from the target value, thereby amplifying the adjustment error of the humidity feedback closed loop.

[0008] These problems are nested in layers, ultimately manifesting as overall system regulation lag, ratio drift, and the coexistence of crop water stress and nutrient accumulation risks. There is an urgent need to build a closer logical integration mechanism between dynamic calibration of growing days and dual-threshold humidity. Summary of the Invention

[0009] The purpose of this invention is to solve the problems of lag, drift and mismatch of water, fertilizer and pesticide ratio adjustment caused by static preset of crop growth days and fluctuation of soil moisture data in greenhouse drip irrigation integrated water, fertilizer and pesticide systems. It proposes an integrated water, fertilizer and pesticide control method and system based on the drip irrigation head.

[0010] This invention is achieved through the following technical solution: This invention proposes an integrated water, fertilizer, and pesticide control method based on the drip irrigation head, the method comprising: S101, through the mixing device built into the drip irrigation head, acquires real-time growth day segment interval data and soil moisture sensor accuracy level data. Combined with the growth day stage switching trigger conditions and humidity data acquisition frequency, it initially compares the growth day threshold grading table with the dynamic range of soil moisture threshold to determine whether the current crop is in a specific growth stage and whether the soil moisture is insufficient, and obtains the initial adjustment demand signal. S102. Based on the initial adjustment demand signal, the dual threshold cross-validation logic is used to determine the current growth period ratio setting based on the growth days threshold classification table. At the same time, the water volume increment parameter is calculated by combining the dynamic range of soil moisture threshold and the moisture abnormal fluctuation filtering mechanism. The ratio adjustment step size parameter and ratio adjustment time delay mechanism are applied simultaneously to determine the specific ratio adjustment scheme and water volume adjustment value. S103: The pressure regulating device obtains real-time monitoring data of the current conveying pressure. If the detected conveying pressure deviates from the preset constant pressure range, the output power of the pressure control module is adjusted, and the influence of the growth days and humidity correlation weight on the pressure demand is combined to stabilize the conveying pressure and obtain a stable pressure output state. S104, based on the proportional adjustment scheme and water volume adjustment value, combined with the stable pressure output state, adjusts the water-fertilizer-pesticide ratio parameters in the mixing device, distributes the proportion of each component in real time through the built-in flow control unit, and at the same time refers to the historical data of the ratio parameters and the spatial distribution analysis results of humidity data to obtain the final mixing and conveying formula. S105 obtains the final mixed delivery formula through the delivery pipe of the drip irrigation head and transmits it to the target area under stable pressure. At the same time, it uses environmental response sensors to continuously monitor soil moisture and crop growth status. Combined with threshold over-limit alarm triggering conditions and dual threshold adjustment feedback closed-loop mechanism, it determines whether to trigger the ratio fine adjustment to obtain dynamically adapted delivery results. S106. Based on the dynamic adaptation delivery results, the current day deviation data is corrected using a dynamic calibration mechanism for growth days. The growth day threshold grading table and the dynamic range of soil moisture threshold are updated synchronously. At the same time, the latest historical data of the ratio parameters are stored to determine the reference parameters for the next adjustment.

[0011] Further, step S101 specifically includes: By using the built-in mixing device in the drip irrigation head, the crop growth days and soil moisture data are acquired in real time. Combined with the preset growth stage switching conditions and collection frequency, the current growth stage and water status are preliminarily determined. By comparing the acquired growth days data with the preset segmentation intervals and threshold grading table, it is determined whether the crop has entered a new growth stage, and the stage division results are obtained. Based on the stage division results, combined with soil moisture data and sensor accuracy information, if the detected moisture value is lower than the lower limit of the dynamic range, it is determined that the water is insufficient and a preliminary adjustment signal is generated. Based on the initial adjustment signal and acquisition frequency, adjust the data update cycle. If the humidity remains below the preset threshold during the update cycle, confirm the priority of the adjustment signal. The support vector machine algorithm is used to classify the confirmed regulatory signals and growth stage data to determine the specific category and intensity of the regulatory signals. By combining the categorized adjustment signals with water status and dynamic range data, targeted irrigation scheduling instructions are generated to determine the timing of irrigation execution. According to the irrigation scheduling instructions, the drip irrigation system is linked to perform water replenishment operations, and the changes in soil moisture during the execution process are recorded in real time to determine the feedback data of the regulation effect.

[0012] Further, step S102 specifically includes: By adjusting the demand signal and combining it with dual threshold verification logic, the current growth period ratio data is obtained from the preset growth days table to determine the phased ratio setting result. Based on the phased ratio setting results, soil moisture values ​​and dynamic interval limit data are obtained. A humidity anomaly filtering mechanism is applied to determine whether there are abnormal fluctuations. If abnormal fluctuations are detected, the humidity data is smoothed to obtain a corrected humidity baseline value. Based on the corrected humidity baseline value and combined with the dynamic interval limit, the water volume increment is calculated, and a preset increment mapping table is used to determine the initial water volume adjustment direction. From the initial water volume adjustment direction, combined with the ratio adjustment step parameters, the adjustment step length constraint is obtained. If the step length exceeds the preset range, the step length is trimmed to obtain the optimized adjustment step length value. Based on the optimized adjustment step size value and combined with the adjustment time delay mechanism, time delay constraint data is obtained, and it is determined whether the delay period meets the execution conditions. If the conditions are met, the final proportional adjustment plan is generated. By combining the final proportional adjustment plan with the water volume increment and water volume adjustment value, a specific irrigation ratio instruction is generated, and the combination of execution parameters is determined. For each combination of execution parameters, the linkage system devices send out parameters, record real-time feedback data during the execution process, and obtain the final execution status confirmation.

[0013] Further, step S103 specifically includes: The pressure regulating device, combined with the real-time monitoring system, continuously collects and transmits pressure data. The collected data stream is then processed into a time series to obtain an orderly pressure monitoring record. Based on the orderly pressure monitoring records and the preset constant pressure range, if the pressure value is detected to deviate from the range, the response mechanism of the pressure control module is triggered to determine the direction of the pressure deviation that needs to be adjusted. Given the determined direction of pressure deviation, the constraints for output power adjustment are obtained. Combined with the preset rules of the pressure stabilization mechanism, the specific magnitude of power adjustment is calculated, and the power adjustment command is obtained. The power adjustment command triggers the pressure control module to adjust the output power, while simultaneously collecting real-time feedback at the data acquisition frequency to determine whether the power adjustment has achieved the expected pressure stability. Based on the feedback results of the pressure steady state, combined with the weight of growth days and humidity correlation, the potential interference of environmental factors on pressure is analyzed, and comprehensive environmental correction parameters are obtained. Based on the comprehensive environmental correction parameters, the dynamic threshold of the pressure stabilization mechanism is adjusted, the fine-tuning range of the constant pressure range is updated synchronously, and the final pressure output state is determined.

[0014] Further, step S104 specifically includes: By using a proportional adjustment scheme and combining water volume adjustment data, the initial proportion parameters of each component in the mixing device are obtained. Based on the deviation between the initial proportion parameters and the preset threshold, the direction and magnitude of the proportion adjustment are determined to obtain a preliminary proportion scheme. Based on the preliminary mixing scheme, the linkage flow control unit allocates the component ratio of water, fertilizer and pesticide in real time, and at the same time obtains the stability data of the current delivery environment from the pressure output status to determine whether the conditions for mixing execution are met. If the real-time allocation data of the flow control unit matches the stability data, the mixing device performs dynamic adjustment of the component ratio, while collecting humidity distribution information to obtain an environmentally adapted mixing ratio. Based on the environmental adaptation of the formulation results, combined with spatial data analysis, the adaptability differences of the delivery formula in different regions are obtained, and regionalized formulation optimization schemes are determined. Based on the regionalized proportion optimization scheme and referring to the proportion adjustment data in the historical records, the support vector machine algorithm is used to predictively adjust the proportion parameters to obtain the optimized proportion parameter set; By optimizing the set of proportioning parameters, the final configuration of the conveying formula is updated. Combined with real-time distribution data, it is determined whether the output of the mixing device has reached the expected proportioning balance state, and the final mixing and conveying scheme is obtained.

[0015] Further, step S105 specifically includes: The mixed formula is delivered to the target area through the drip irrigation head and delivery pipeline. The pressure status data fluctuation is monitored in real time. Combined with the preset pressure threshold range, the stability of the delivery process is judged, and the preliminary results of pressure monitoring are obtained. Based on the preliminary results of pressure monitoring, the environmental sensors are linked to collect soil moisture information in the target area. The deviation between the collected moisture data and the preset moisture range is analyzed to determine the environmental adaptability and the adaptability of the moisture distribution. Based on the adaptation status of humidity distribution, data indicators related to crop growth are obtained. Real-time changes are recorded by environmental sensors and combined with preset growth requirement thresholds to determine whether crop growth conditions are met, thus obtaining growth adaptation assessment data. Based on the growth adaptation assessment data and the triggering conditions of the threshold alarm, if the soil moisture or growth index exceeds the preset range, the adjustment feedback mechanism is used to calculate the range of ratio fine-tuning and determine the adjustment scheme of the fine-tuning parameters. For the adjustment scheme of fine-tuning parameters, the support vector machine algorithm is used to predictively optimize the magnitude of the ratio fine-tuning. Combined with historical environmental data and crop growth records, the optimized ratio adjustment value is obtained. By optimizing the mixing ratio adjustment value, updating the delivery configuration of the mixed formula, and linking the control unit of the drip irrigation head, the mixing ratio in the delivery pipeline is adjusted in real time to obtain a dynamically adapted final delivery result. Based on the final delivery results, the soil moisture and crop growth status of the target area are continuously monitored through environmental sensors. Combined with the closed-loop control of the adjustment feedback mechanism, it is determined whether further fine-tuning is needed to obtain continuously adaptive delivery feedback data.

[0016] Further, step S106 specifically includes: The corrected day deviation information is obtained through the growth day calibration mechanism. The deviation information is compared with the preset growth stage standard. If the deviation exceeds the preset threshold, the grading table update process is triggered to obtain the adjusted growth day grading data. Based on the adjusted growth days grading data, the corresponding dynamic range of soil moisture is obtained. Combined with the humidity information collected by the current environmental sensors, it is determined whether the humidity requirements of the current growth stage are met, and the matching status of the humidity range is determined. Based on the matching status of the humidity range, historical backtracking records of the ratio parameters are obtained, and the support vector machine algorithm is used to perform trend analysis on the historical records to obtain the predicted adjustment direction of the ratio parameters; By predicting and adjusting the direction of the proportioning parameters, and combining the real-time feedback data of the current delivery results, it is determined whether the delivery configuration needs to be corrected. If there is a deviation between the predicted direction and the real-time feedback, a new adjustment benchmark value is generated. Based on the new adjustment benchmark value, update the storage record of the proportioning parameters, and synchronously adjust the control logic of the conveying system to obtain the updated proportioning configuration scheme. Obtain the updated ratio and configuration scheme, continuously monitor soil moisture changes in conjunction with environmental sensors, and determine whether further optimization is needed based on the phased requirements of the growth day grading data, and determine the final delivery adjustment parameters. By transmitting the final adjustment parameters, updating the historical backtracking records within the system, and synchronously refreshing the reference data of the adjustment benchmark, the initialization conditions for the next adjustment are obtained.

[0017] This invention also proposes an integrated water, fertilizer, and pesticide control system based on the drip irrigation head, the system comprising: Acquisition Module: Through the built-in mixing device in the drip irrigation head, it acquires real-time data on the segmented intervals of the growing days and the accuracy level data of the soil moisture sensor. Combined with the triggering conditions for the switching of the growing days stage and the frequency of moisture data acquisition, it initially compares the growing days threshold grading table with the dynamic range of the soil moisture threshold to determine whether the current crop is in a specific growth stage and whether the soil moisture is insufficient, and obtains the initial adjustment demand signal. Adjustment module: Based on the initial adjustment demand signal, the module uses a dual-threshold cross-validation logic to determine the current growth period ratio setting based on the growth days threshold grading table. At the same time, it calculates the water volume increment parameter by combining the dynamic range of soil moisture threshold and the moisture abnormal fluctuation filtering mechanism. Simultaneously, it applies the ratio adjustment step size parameter and the ratio adjustment time delay mechanism to determine the specific ratio adjustment scheme and water volume adjustment value. Pressure regulation module: The pressure regulation device acquires real-time monitoring data of the current delivery pressure. If the detected delivery pressure deviates from the preset constant pressure range, the output power of the pressure control module is adjusted. The influence of the growth days and humidity correlation weight on the pressure demand is combined to stabilize the delivery pressure and obtain a stable pressure output state. Delivery module: Based on the proportional adjustment scheme and water volume adjustment value, combined with the stable pressure output state, the water-fertilizer-pesticide ratio parameters in the mixing device are adjusted. The proportion of each component is distributed in real time through the built-in flow control unit. At the same time, the final mixing and delivery formula is obtained by referring to the historical data of the ratio parameters and the spatial distribution analysis results of humidity data. Fine-tuning module: The final mixed delivery formula is obtained through the delivery pipe of the drip irrigation head and delivered to the target area under stable pressure. At the same time, the environmental response sensor continuously monitors soil moisture and crop growth status. Combined with threshold over-limit alarm triggering conditions and dual threshold adjustment feedback closed-loop mechanism, it determines whether to trigger the ratio fine-tuning to obtain dynamically adapted delivery results. Update module: Based on the dynamically adapted delivery results, the current day deviation data is corrected using a dynamic calibration mechanism for growth days. The growth day threshold grading table and the dynamic range of soil moisture threshold are updated synchronously. At the same time, the latest historical data of the ratio parameters are stored to determine the benchmark parameters for the next adjustment.

[0018] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the integrated water, fertilizer and pesticide control method based on the drip irrigation head.

[0019] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the integrated water, fertilizer, and pesticide control method based on a drip irrigation head.

[0020] The beneficial effects of this invention are: This invention proposes an integrated water, fertilizer, and pesticide control method and system based on the drip irrigation head. It addresses the complex operational challenges of dynamically matching crop growth stages with soil moisture, maintaining stable delivery pressure, and precisely adjusting the water, fertilizer, and pesticide ratios in drip irrigation systems, offering a systematic solution. This invention accurately determines crop growth needs and water status by real-time acquisition of growth days and soil moisture data, combined with dual-threshold cross-validation logic and a dynamic calibration mechanism, thereby determining the ratio adjustment scheme and water volume adjustment values. Simultaneously, it utilizes a pressure regulation device and correlation weight analysis to ensure stable delivery pressure output. Finally, through a flow control unit and a feedback closed-loop mechanism, it dynamically optimizes the mixed delivery formula to achieve precise distribution. The core innovation of this invention lies in the deep integration of growth days, soil moisture, and pressure control to form an adaptive adjustment closed loop. This not only improves resource utilization efficiency but also significantly enhances the stability of the crop growth environment, providing an efficient and intelligent control method for modern agricultural irrigation. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a flowchart of an integrated water, fertilizer, and pesticide control method based on a drip irrigation head as described in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Specifically, in combination Figure 1 This invention proposes an integrated water, fertilizer, and pesticide control method based on the drip irrigation head, the method comprising: S101, through the mixing device built into the drip irrigation head, acquires real-time growth day segment interval data and soil moisture sensor accuracy level data. Combined with the growth day stage switching trigger conditions and humidity data acquisition frequency, it initially compares the growth day threshold grading table with the dynamic range of soil moisture threshold to determine whether the current crop is in a specific growth stage and whether the soil moisture is insufficient, and obtains the initial adjustment demand signal. Further, step S101 specifically includes: By using the built-in mixing device in the drip irrigation head, the crop growth days and soil moisture data are acquired in real time. Combined with the preset growth stage switching conditions and collection frequency, the current growth stage and water status are preliminarily determined. By comparing the acquired growth days data with the preset segmentation intervals and threshold grading table, it is determined whether the crop has entered a new growth stage, and the stage division results are obtained. Based on the stage division results, combined with soil moisture data and sensor accuracy information, if the detected moisture value is lower than the lower limit of the dynamic range, it is determined that the water is insufficient and a preliminary adjustment signal is generated. Based on the initial adjustment signal and acquisition frequency, adjust the data update cycle. If the humidity remains below the preset threshold during the update cycle, confirm the priority of the adjustment signal. The support vector machine algorithm is used to classify the confirmed regulatory signals and growth stage data to determine the specific category and intensity of the regulatory signals. By combining the categorized adjustment signals with water status and dynamic range data, targeted irrigation scheduling instructions are generated to determine the timing of irrigation execution. According to the irrigation scheduling instructions, the drip irrigation system is linked to perform water replenishment operations, and the changes in soil moisture during the execution process are recorded in real time to determine the feedback data of the regulation effect.

[0025] In one embodiment, the crop growth days are obtained as 45 days through the built-in device of the drip irrigation head, and the current soil moisture is collected as 18.5%.

[0026] Specifically, the 45-day period is compared with a pre-defined segmented interval table. If the interval of 30 to 60 days is defined as the vigorous growth period, then the crop is determined to have entered that specific stage.

[0027] It should be noted that, based on the sensor's 0.2% accuracy information, if the detected humidity value of 18.5% is lower than the lower limit of the dynamic range of 22.0% for this stage, it is determined that there is insufficient moisture, and a preliminary adjustment signal is generated.

[0028] For example, based on the initial adjustment signal, the data acquisition frequency is increased from once every 120 minutes to once every 20 minutes.

[0029] If the humidity value remains below the preset threshold of 20.0% for three consecutive update cycles, the priority of the adjustment signal is confirmed to be high.

[0030] In one embodiment, a support vector machine algorithm is used for classification.

[0031] Using growth stage code 2, humidity deviation value of 3.5%, and current ambient temperature of 28.5 degrees as input vectors, spatial mapping is performed through a preset radial basis kernel function. The specific category of the output adjustment signal is deep irrigation, and the irrigation intensity coefficient is determined to be 0.85.

[0032] Specifically, the generated irrigation scheduling instructions will combine water status and dynamic interval data to determine the best time to execute irrigation.

[0033] If the current period is between 12:00 and 14:00 when sunlight is intense, the instruction can be set to be delayed until 16:00 to reduce water loss through evaporation.

[0034] In one embodiment, after the drip irrigation system performs a water replenishment operation, it records in real time the change curve of soil moisture from 18.5% to 24.5%.

[0035] If the humidity increase rate meets the expected target of 3.0% per hour by analyzing the feedback data, the adjustment effect is considered to be excellent.

[0036] This closed-loop control method can significantly improve water use efficiency and ensure that crops are in the optimal water environment at each growth stage.

[0037] S102. Based on the initial adjustment demand signal, the dual threshold cross-validation logic is used to determine the current growth period ratio setting based on the growth days threshold classification table. At the same time, the water volume increment parameter is calculated by combining the dynamic range of soil moisture threshold and the moisture abnormal fluctuation filtering mechanism. The ratio adjustment step size parameter and ratio adjustment time delay mechanism are applied simultaneously to determine the specific ratio adjustment scheme and water volume adjustment value. Further, step S102 specifically includes: By adjusting the demand signal and combining it with dual threshold verification logic, the current growth period ratio data is obtained from the preset growth days table to determine the phased ratio setting result. Based on the phased ratio setting results, soil moisture values ​​and dynamic interval limit data are obtained. A humidity anomaly filtering mechanism is applied to determine whether there are abnormal fluctuations. If abnormal fluctuations are detected, the humidity data is smoothed to obtain a corrected humidity baseline value. Based on the corrected humidity baseline value and combined with the dynamic interval limit, the water volume increment is calculated, and a preset increment mapping table is used to determine the initial water volume adjustment direction. From the initial water volume adjustment direction, combined with the ratio adjustment step parameters, the adjustment step length constraint is obtained. If the step length exceeds the preset range, the step length is trimmed to obtain the optimized adjustment step length value. Based on the optimized adjustment step size value and combined with the adjustment time delay mechanism, time delay constraint data is obtained, and it is determined whether the delay period meets the execution conditions. If the conditions are met, the final proportional adjustment plan is generated. By combining the final proportional adjustment plan with the water volume increment and water volume adjustment value, a specific irrigation ratio instruction is generated, and the combination of execution parameters is determined. For each combination of execution parameters, the linkage system devices send out parameters, record real-time feedback data during the execution process, and obtain the final execution status confirmation.

[0038] In one embodiment, the number of growth days is analyzed using dual-threshold verification logic.

[0039] If the current crop growth period is 52 days and the total growth period is set to 150 days, then the current growth period percentage is calculated to be 34.7%.

[0040] Specifically, this ratio data is directly linked to the phased ratio setting results and is used to determine whether the current stage is one of high water demand.

[0041] If the real-time data transmitted by the sensor fluctuates drastically between 19.2% and 22.5% when acquiring soil moisture values, the abnormal moisture wave filtering mechanism will be activated.

[0042] An outlier removal algorithm was used to process five consecutive data collection points, removing outlier fluctuations that deviated from the mean by more than 1.2%, resulting in a corrected humidity baseline value of 19.6%.

[0043] In one embodiment, the water volume increment parameter of 3.9% is calculated by taking into account the deviation between the lower limit of the dynamic range of 23.5% and the benchmark value of 19.6%.

[0044] Using a pre-defined incremental mapping table, the initial direction of water volume adjustment is determined to be an increase in irrigation volume.

[0045] Specifically, retrieve the step size parameter for adjusting the ratio, and set the upper limit of the single adjustment step size constraint condition to 12%.

[0046] If the initial calculated adjustment requirement is 18%, then step size pruning is triggered, locking the final step size within the 12% threshold to obtain the optimized adjustment step size value.

[0047] In one embodiment, time delay constraint data is introduced for secondary determination of the optimized adjustment step size value.

[0048] If the current environment is in the high-temperature period from 11:00 to 14:00, then the conditions for immediate execution are not met.

[0049] Specifically, the system will trigger a time delay mechanism, postponing the execution time to after 4 PM, thereby generating the final proportional adjustment plan.

[0050] In one embodiment, based on the final proportional adjustment scheme, a specific irrigation ratio instruction is generated by combining the aforementioned 3.9% water volume increment parameter with the optimized adjustment step value of 12%.

[0051] A linear weighted algorithm is used, with the optimized adjustment step size (12%) and water volume increment parameter (3.9%) as inputs. The comprehensive adjustment index is obtained by weighting and summing the values ​​with preset weights of 0.6 and 0.4. Then, based on the index, the preset execution parameter mapping table is consulted to set the main pipeline water pressure to 0.25 MPa and the opening degree of each branch valve to 65%, thereby determining the combination of execution parameters.

[0052] Specifically, for this combination of execution parameters, the solenoid valves and variable frequency water pumps of the drip irrigation network are linked to send parameters.

[0053] During the process, the pressure value at the end of the pipeline and the flow meter reading are recorded every 5 minutes.

[0054] If the flow feedback data for four consecutive cycles remains stable at 2.5 cubic meters per hour and the terminal pressure fluctuation is less than 0.02 MPa, then the instruction issuance is determined to be consistent with the physical execution, and the final execution status is confirmed.

[0055] S103: The pressure regulating device obtains real-time monitoring data of the current conveying pressure. If the detected conveying pressure deviates from the preset constant pressure range, the output power of the pressure control module is adjusted, and the influence of the growth days and humidity correlation weight on the pressure demand is combined to stabilize the conveying pressure and obtain a stable pressure output state. Further, step S103 specifically includes: The pressure regulating device, combined with the real-time monitoring system, continuously collects and transmits pressure data. The collected data stream is then processed into a time series to obtain an orderly pressure monitoring record. Based on the orderly pressure monitoring records and the preset constant pressure range, if the pressure value is detected to deviate from the range, the response mechanism of the pressure control module is triggered to determine the direction of the pressure deviation that needs to be adjusted. Given the determined direction of pressure deviation, the constraints for output power adjustment are obtained. Combined with the preset rules of the pressure stabilization mechanism, the specific magnitude of power adjustment is calculated, and the power adjustment command is obtained. The power adjustment command triggers the pressure control module to adjust the output power, while simultaneously collecting real-time feedback at the data acquisition frequency to determine whether the power adjustment has achieved the expected pressure stability. Based on the feedback results of the pressure steady state, combined with the weight of growth days and humidity correlation, the potential interference of environmental factors on pressure is analyzed, and comprehensive environmental correction parameters are obtained. Based on the comprehensive environmental correction parameters, the dynamic threshold of the pressure stabilization mechanism is adjusted, the fine-tuning range of the constant pressure range is updated synchronously, and the final pressure output state is determined.

[0056] In one embodiment, the pressure regulating device continuously collects pressure data of the main pipeline network at a frequency of twice per second using a high-precision sensor.

[0057] Specifically, for the collected raw data stream, a sliding window algorithm is used to process the time series, transforming discrete pressure points into continuous pressure fluctuation curves.

[0058] For example, when the pressure value is detected to fluctuate between 0.42 MPa and 0.48 MPa, the mean and variance of that time period will be automatically recorded to form an orderly pressure monitoring record, which will serve as the benchmark input for subsequent regulation.

[0059] In one embodiment, the preset constant pressure range is set to 0.45 MPa, with an allowable fluctuation range of ±0.03 MPa.

[0060] If the real-time monitoring record shows that the pressure drops to 0.38 MPa, the response mechanism of the pressure control module is triggered, and the deviation direction is determined to be insufficient pressure.

[0061] Specifically, constraints for adjusting output power are obtained, such as limiting the maximum frequency increment of the inverter to within 5 Hz.

[0062] Combined with the pressure stabilization mechanism, an incremental PID algorithm is used to calculate the specific magnitude of power adjustment, such as increasing the water pump output power by 8.5%, generating a specific power adjustment command and sending it to the execution terminal.

[0063] In one embodiment, after power adjustment is performed, the pressure feedback value is obtained in real time through data acquisition frequency to determine whether it has returned to the preset range of 0.45 MPa.

[0064] Specifically, a comprehensive analysis is conducted by introducing weights based on the number of growing days and humidity correlation.

[0065] For example, when the crop is in its peak water demand period on the 85th day of growth, the weight of the number of growing days is set to 0.7, and the corresponding pressure correction coefficient for the number of growing days is 0.8; while when the real-time soil moisture is 22%, the weight of the moisture correlation is set to 0.3, and the corresponding pressure correction coefficient for the soil moisture is 0.3.

[0066] The comprehensive environmental correction parameter, calculated using a weighted summation model, is 0.65, which is used for secondary fine-tuning of the current pressure output state.

[0067] In one embodiment, the threshold of the pressure stabilization mechanism is dynamically adjusted based on the calculated comprehensive environmental correction parameters.

[0068] Specifically, if the correction parameters show that the environmental evaporation is large, the upper limit of the constant pressure range will be slightly adjusted to 0.52 MPa to compensate for the pressure loss at the end of the pipeline network.

[0069] For example, by using the updated dynamic threshold, the final pressure output state is determined to be 0.49 MPa, ensuring that the irrigation system can maintain a relatively stable delivery pressure under complex environmental disturbances, and achieving precise pressure closed-loop control.

[0070] S104, based on the proportional adjustment scheme and water volume adjustment value, combined with the stable pressure output state, adjusts the water-fertilizer-pesticide ratio parameters in the mixing device, distributes the proportion of each component in real time through the built-in flow control unit, and at the same time refers to the historical data of the ratio parameters and the spatial distribution analysis results of humidity data to obtain the final mixing and conveying formula. Further, step S104 specifically includes: By using a proportional adjustment scheme and combining water volume adjustment data, the initial proportion parameters of each component in the mixing device are obtained. Based on the deviation between the initial proportion parameters and the preset threshold, the direction and magnitude of the proportion adjustment are determined to obtain a preliminary proportion scheme. Based on the preliminary mixing scheme, the linkage flow control unit allocates the component ratio of water, fertilizer and pesticide in real time, and at the same time obtains the stability data of the current delivery environment from the pressure output status to determine whether the conditions for mixing execution are met. If the real-time allocation data of the flow control unit matches the stability data, the mixing device performs dynamic adjustment of the component ratio, while collecting humidity distribution information to obtain an environmentally adapted mixing ratio. Based on the environmental adaptation of the formulation results, combined with spatial data analysis, the adaptability differences of the delivery formula in different regions are obtained, and regionalized formulation optimization schemes are determined. Based on the regionalized proportion optimization scheme and referring to the proportion adjustment data in the historical records, the support vector machine algorithm is used to predictively adjust the proportion parameters to obtain the optimized proportion parameter set; By optimizing the set of proportioning parameters, the final configuration of the conveying formula is updated. Combined with real-time distribution data, it is determined whether the output of the mixing device has reached the expected proportioning balance state, and the final mixing and conveying scheme is obtained.

[0071] In one embodiment, the ratio adjustment scheme first establishes a baseline ratio of the three components: water, fertilizer, and pesticide.

[0072] Specifically, the initial ratio parameters of each component in the mixing device are obtained through sensors, for example, the initial water-to-fertilizer ratio is set to 500:1.

[0073] If the detected fertilizer solution concentration deviation exceeds the preset 3% threshold, it is determined to be an imbalance in the ratio.

[0074] For example, when the real-time ratio of fertilizer solution drops to 480:1, the adjustment direction is determined to be either reducing the amount of fertilizer solution pumped in or increasing the amount of water, with the magnitude set to make up for the missing 4% flow rate, thus obtaining a preliminary mixing scheme.

[0075] In one embodiment, the initial proportioning scheme needs to be precisely allocated by the flow control unit.

[0076] Specifically, the pressure output status of the main pipeline network needs to be retrieved simultaneously during the allocation process.

[0077] For example, when the pressure is stable at 0.45 MPa and the fluctuation is less than 0.02 MPa, the conditions for the proportioning are considered met.

[0078] If the pressure fluctuates drastically, the opening degree of the flow valve is adjusted to maintain a constant component ratio and ensure the physical stability of the mixture during transportation.

[0079] In one embodiment, the execution results are verified by collecting humidity distribution information of the irrigation area.

[0080] Specifically, if the soil moisture content in a certain area is detected to be below the warning value of 20%, the output of the mixing device will be dynamically adjusted to increase the moisture content.

[0081] For example, the water-to-fertilizer ratio can be temporarily adjusted from 500:1 to 550:1 to adapt to the current drought level and obtain an environmentally suitable ratio.

[0082] In one embodiment, spatial data analysis is used to differentiate the processing of different plots of land.

[0083] Specifically, it is necessary to understand the compatibility differences between slope and plain areas. For example, due to the faster runoff on slopes, it is necessary to increase the adhesion ratio of the agent.

[0084] By determining regionalized formulation optimization schemes, the delivery formula can accurately cover work surfaces with different geographical features.

[0085] In one embodiment, a support vector machine algorithm is used to predictively adjust the matching parameters.

[0086] Specifically, input historical crop ratio data and crop growth rate over the past 7 days, and establish a prediction model through kernel function mapping.

[0087] For example, if it is predicted that the crop's demand for phosphate fertilizer will increase by 10% in the next 24 hours, the optimized ratio parameter set can be updated in advance.

[0088] In one embodiment, the final configuration is updated using an optimized set of parameters.

[0089] Specifically, the conductivity and flow rate data at the outlet of the mixing device are compared in real time to determine whether the output has reached the expected proportion balance.

[0090] For example, when the deviation between the measured conductivity and the target value is reduced to within 0.1 millisiemens per centimeter, the final hybrid transport scheme is confirmed, achieving closed-loop optimization of the entire process.

[0091] S105 obtains the final mixed delivery formula through the delivery pipe of the drip irrigation head and transmits it to the target area under stable pressure. At the same time, it uses environmental response sensors to continuously monitor soil moisture and crop growth status. Combined with threshold over-limit alarm triggering conditions and dual threshold adjustment feedback closed-loop mechanism, it determines whether to trigger the ratio fine adjustment to obtain dynamically adapted delivery results. Further, step S105 specifically includes: The mixed formula is delivered to the target area through the drip irrigation head and delivery pipeline. The pressure status data fluctuation is monitored in real time. Combined with the preset pressure threshold range, the stability of the delivery process is judged, and the preliminary results of pressure monitoring are obtained. Based on the preliminary results of pressure monitoring, the environmental sensors are linked to collect soil moisture information in the target area. The deviation between the collected moisture data and the preset moisture range is analyzed to determine the environmental adaptability and the adaptability of the moisture distribution. Based on the adaptation status of humidity distribution, data indicators related to crop growth are obtained. Real-time changes are recorded by environmental sensors and combined with preset growth requirement thresholds to determine whether crop growth conditions are met, thus obtaining growth adaptation assessment data. Based on the growth adaptation assessment data and the triggering conditions of the threshold alarm, if the soil moisture or growth index exceeds the preset range, the adjustment feedback mechanism is used to calculate the range of ratio fine-tuning and determine the adjustment scheme of the fine-tuning parameters. For the adjustment scheme of fine-tuning parameters, the support vector machine algorithm is used to predictively optimize the magnitude of the ratio fine-tuning. Combined with historical environmental data and crop growth records, the optimized ratio adjustment value is obtained. By optimizing the mixing ratio adjustment value, updating the delivery configuration of the mixed formula, and linking the control unit of the drip irrigation head, the mixing ratio in the delivery pipeline is adjusted in real time to obtain a dynamically adapted final delivery result. Based on the final delivery results, the soil moisture and crop growth status of the target area are continuously monitored through environmental sensors. Combined with the closed-loop control of the adjustment feedback mechanism, it is determined whether further fine-tuning is needed to obtain continuously adaptive delivery feedback data.

[0092] In one embodiment, the mixed formula is pumped into the main delivery pipeline via a drip irrigation head, and a pressure transmitter is installed at the end node of the pipeline network.

[0093] Specifically, the water pressure data in the pipeline is collected in real time, and the normal transmission pressure threshold range is set to 0.25 MPa to 0.35 MPa.

[0094] For example, when the terminal pressure is monitored to drop to 0.21 MPa for 5 consecutive minutes, it is determined that there is a risk of pressure loss in the transmission process, generating a preliminary monitoring result of low pressure and triggering the subsequent linkage detection mechanism.

[0095] In one embodiment, an environmental sensor buried 20 centimeters underground in the target irrigation area is activated based on preliminary results from pressure monitoring.

[0096] Specifically, the current volumetric moisture content of the soil is collected and compared with the preset suitable humidity range of 45% to 55%.

[0097] For example, if the actual humidity data collected is 38%, which is lower than the preset lower limit, the analysis concludes that the current environment is in a state of mild water shortage, and the humidity distribution is determined to be suitable for water replenishment.

[0098] In one embodiment, real-time growth data indicators of the crop are further obtained for the adaptation state that requires water replenishment.

[0099] Specifically, the leaf area index and relative chlorophyll content of crops are obtained through a canopy analyzer.

[0100] For example, if the leaf area index threshold for the current growth stage is set to 3.5, and the actual measured value is 3.1, combined with the changes in cumulative sunlight recorded by environmental sensors, it is determined that the current water and fertilizer supply does not meet the growth requirements of the crop during the jointing stage, and the growth adaptation assessment data is output.

[0101] In one embodiment, a threshold alarm is triggered when soil moisture or leaf area index exceeds a preset range, based on growth adaptation assessment data.

[0102] Specifically, the adjustment feedback mechanism is activated to calculate the fine-tuning range of the proportions based on the deviation.

[0103] Based on the 7% humidity gap and the 0.4 leaf area index difference, and using the preset humidity-water mapping coefficient of 2.14 and leaf area index-nitrogen fertilizer mapping coefficient of 12.5, it is calculated that the total irrigation water volume needs to be increased by 7%×2.14≈15%, and the nitrogen fertilizer injection ratio needs to be increased by 0.4×12.5=5%.

[0104] In one embodiment, a support vector machine algorithm is used to predictively optimize the initial fine-tuning scheme.

[0105] Specifically, historical soil temperature and humidity data from the past 30 days and average daily crop growth rate are used as input feature vectors, and nonlinear mapping is performed through radial basis kernel functions.

[0106] For example, the algorithm outputs an optimized ratio adjustment value of increasing water volume by 12% and increasing nitrogen fertilizer by 4.5%.

[0107] Subsequently, the optimized value is sent to the control unit of the drip irrigation head, the opening of the solenoid valve is adjusted, and soil moisture is continuously collected in 10-minute cycles to form a closed-loop transmission feedback data.

[0108] S106. Based on the dynamic adaptation delivery results, the current day deviation data is corrected using a dynamic calibration mechanism for growth days. The growth day threshold grading table and the dynamic range of soil moisture threshold are updated synchronously. At the same time, the latest historical data of the ratio parameters are stored to determine the reference parameters for the next adjustment.

[0109] Further, step S106 specifically includes: The corrected day deviation information is obtained through the growth day calibration mechanism. The deviation information is compared with the preset growth stage standard. If the deviation exceeds the preset threshold, the grading table update process is triggered to obtain the adjusted growth day grading data. Based on the adjusted growth days grading data, the corresponding dynamic range of soil moisture is obtained. Combined with the humidity information collected by the current environmental sensors, it is determined whether the humidity requirements of the current growth stage are met, and the matching status of the humidity range is determined. Based on the matching status of the humidity range, historical backtracking records of the ratio parameters are obtained, and the support vector machine algorithm is used to perform trend analysis on the historical records to obtain the predicted adjustment direction of the ratio parameters; By predicting and adjusting the direction of the proportioning parameters, and combining the real-time feedback data of the current delivery results, it is determined whether the delivery configuration needs to be corrected. If there is a deviation between the predicted direction and the real-time feedback, a new adjustment benchmark value is generated. Based on the new adjustment benchmark value, update the storage record of the proportioning parameters, and synchronously adjust the control logic of the conveying system to obtain the updated proportioning configuration scheme. Obtain the updated ratio and configuration scheme, continuously monitor soil moisture changes in conjunction with environmental sensors, and determine whether further optimization is needed based on the phased requirements of the growth day grading data, and determine the final delivery adjustment parameters. By transmitting the final adjustment parameters, updating the historical backtracking records within the system, and synchronously refreshing the reference data of the adjustment benchmark, the initialization conditions for the next adjustment are obtained.

[0110] In one embodiment, the crop development progress is quantitatively assessed through a growth days calibration mechanism.

[0111] Specifically, the leaf spread or plant height data identified by the sensor is converted into actual growth days and compared with a standard growth curve.

[0112] For example, when the preset standard number of days for the jointing period is 45 days, and the real-time monitoring and calculation of the growth progress is 49 days, the deviation of 4 days exceeds the preset threshold of 2 days, triggering the grading table update and shifting the subsequent irrigation stage to ensure that water and fertilizer supply is highly synchronized with the physiological needs of crops.

[0113] In one embodiment, the dynamic range of soil moisture for the current stage is determined based on updated growth day grading data.

[0114] Specifically, for crops in the flowering stage, the suitable humidity range is adjusted from 50% to 60% during the seedling stage to 65% to 75%.

[0115] For example, if the soil volumetric moisture content collected in real time by the environmental sensor is 62%, and it is compared with the new range, it is determined that the current state is low humidity, and the water replenishment and pressurization process needs to be started to accurately meet the physiological consumption of crops during the critical water demand period.

[0116] In one embodiment, a support vector machine algorithm is used to perform in-depth mining of the historical backtracking records of the matching parameters.

[0117] Specifically, the nitrogen, phosphorus and potassium ratios, irrigation durations and corresponding yield prediction factors of the past 15 irrigation cycles were used as input feature vectors, and nonlinear regression analysis was performed using radial basis kernel functions.

[0118] For example, the algorithm predicts that under the current trend of rising temperatures, the adjustment of the ratio parameters should be to increase the proportion of potassium fertilizer by 2.5% and reduce the amount of irrigation per irrigation by 8% to prevent nutrient loss and enhance the crop's resistance to stress.

[0119] In one embodiment, the predicted adjustment direction is cross-validated with real-time feedback data of the delivery results.

[0120] Specifically, if the support vector machine predicts that the pressure needs to be increased to 0.32 MPa, but the end pressure sensor reports that the current pipeline pressure has reached 0.31 MPa and is accompanied by slight vibration, it is determined that the predicted direction deviates from the actual operating conditions.

[0121] For example, using the SVM prediction increment of 0.01 MPa and the measured safety increment of 0 as the weighting objects, and setting the prediction weight and feedback weight to 0.5 each, the correction increment is calculated to be 0.005 MPa by weighted average method, which is used as the new adjustment benchmark value.

[0122] In one embodiment, the control logic of the drip irrigation head is dynamically refreshed by using the final determined delivery adjustment parameters.

[0123] Specifically, the corrected pulse width modulation signal is sent to the fertilizer pump to adjust the opening ratio of the fertilizer suction channel.

[0124] For example, the nitrogen fertilizer injection frequency was adjusted from 45 times per minute to 48 times per minute, and the soil electrical conductivity was continuously monitored in 5-minute cycles. The closed-loop data generated in this process was stored in the historical backtracking record to provide an initial reference benchmark for the next irrigation cycle, thus realizing the continuous evolution and precise iteration of the irrigation scheme.

[0125] This invention also proposes an integrated water, fertilizer, and pesticide control system based on the drip irrigation head, the system comprising: Acquisition Module: Through the built-in mixing device in the drip irrigation head, it acquires real-time data on the segmented intervals of the growing days and the accuracy level data of the soil moisture sensor. Combined with the triggering conditions for the switching of the growing days stage and the frequency of moisture data acquisition, it initially compares the growing days threshold grading table with the dynamic range of the soil moisture threshold to determine whether the current crop is in a specific growth stage and whether the soil moisture is insufficient, and obtains the initial adjustment demand signal. Adjustment module: Based on the initial adjustment demand signal, the module uses a dual-threshold cross-validation logic to determine the current growth period ratio setting based on the growth days threshold grading table. At the same time, it calculates the water volume increment parameter by combining the dynamic range of soil moisture threshold and the moisture abnormal fluctuation filtering mechanism. Simultaneously, it applies the ratio adjustment step size parameter and the ratio adjustment time delay mechanism to determine the specific ratio adjustment scheme and water volume adjustment value. Pressure regulation module: The pressure regulation device acquires real-time monitoring data of the current delivery pressure. If the detected delivery pressure deviates from the preset constant pressure range, the output power of the pressure control module is adjusted. The influence of the growth days and humidity correlation weight on the pressure demand is combined to stabilize the delivery pressure and obtain a stable pressure output state. Delivery module: Based on the proportional adjustment scheme and water volume adjustment value, combined with the stable pressure output state, the water-fertilizer-pesticide ratio parameters in the mixing device are adjusted. The proportion of each component is distributed in real time through the built-in flow control unit. At the same time, the final mixing and delivery formula is obtained by referring to the historical data of the ratio parameters and the spatial distribution analysis results of humidity data. Fine-tuning module: The final mixed delivery formula is obtained through the delivery pipe of the drip irrigation head and delivered to the target area under stable pressure. At the same time, the environmental response sensor continuously monitors soil moisture and crop growth status. Combined with threshold over-limit alarm triggering conditions and dual threshold adjustment feedback closed-loop mechanism, it determines whether to trigger the ratio fine-tuning to obtain dynamically adapted delivery results. Update module: Based on the dynamically adapted delivery results, the current day deviation data is corrected using a dynamic calibration mechanism for growth days. The growth day threshold grading table and the dynamic range of soil moisture threshold are updated synchronously. At the same time, the latest historical data of the ratio parameters are stored to determine the benchmark parameters for the next adjustment.

[0126] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the integrated water, fertilizer and pesticide control method based on the drip irrigation head.

[0127] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the integrated water, fertilizer, and pesticide control method based on a drip irrigation head.

[0128] The memory in this application embodiment can be volatile memory or non-volatile memory, or it can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the methods described in this invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0129] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0130] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0131] It should be noted that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied as execution by a hardware decoding processor, or as a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0132] The above provides a detailed description of the integrated water, fertilizer, and pesticide control method and system based on the drip irrigation head proposed in this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for integrated water, fertilizer, and pesticide control based on the drip irrigation head, characterized in that, The method includes: S101, through the mixing device built into the drip irrigation head, acquires real-time growth day segment interval data and soil moisture sensor accuracy level data. Combined with the growth day stage switching trigger conditions and humidity data acquisition frequency, it initially compares the growth day threshold grading table with the dynamic range of soil moisture threshold to determine whether the current crop is in a specific growth stage and whether the soil moisture is insufficient, and obtains the initial adjustment demand signal. S102. Based on the initial adjustment demand signal, the dual threshold cross-validation logic is used to determine the current growth period ratio setting based on the growth days threshold classification table. At the same time, the water volume increment parameter is calculated by combining the dynamic range of soil moisture threshold and the moisture abnormal fluctuation filtering mechanism. The ratio adjustment step size parameter and ratio adjustment time delay mechanism are applied simultaneously to determine the specific ratio adjustment scheme and water volume adjustment value. S103: The pressure regulating device obtains real-time monitoring data of the current conveying pressure. If the detected conveying pressure deviates from the preset constant pressure range, the output power of the pressure control module is adjusted, and the influence of the growth days and humidity correlation weight on the pressure demand is combined to stabilize the conveying pressure and obtain a stable pressure output state. S104, based on the proportional adjustment scheme and water volume adjustment value, combined with the stable pressure output state, adjusts the water-fertilizer-pesticide ratio parameters in the mixing device, distributes the proportion of each component in real time through the built-in flow control unit, and at the same time refers to the historical data of the ratio parameters and the spatial distribution analysis results of humidity data to obtain the final mixing and conveying formula. S105 obtains the final mixed delivery formula through the delivery pipe of the drip irrigation head and transmits it to the target area under stable pressure. At the same time, it uses environmental response sensors to continuously monitor soil moisture and crop growth status. Combined with threshold over-limit alarm triggering conditions and dual threshold adjustment feedback closed-loop mechanism, it determines whether to trigger the ratio fine adjustment to obtain dynamically adapted delivery results. S106. Based on the dynamic adaptation delivery results, the current day deviation data is corrected using a dynamic calibration mechanism for growth days. The growth day threshold grading table and the dynamic range of soil moisture threshold are updated synchronously. At the same time, the latest historical data of the ratio parameters are stored to determine the reference parameters for the next adjustment.

2. The method according to claim 1, characterized in that, Step S101 specifically includes: By using the built-in mixing device in the drip irrigation head, the crop growth days and soil moisture data are acquired in real time. Combined with the preset growth stage switching conditions and collection frequency, the current growth stage and water status are preliminarily determined. By comparing the acquired growth days data with the preset segmentation intervals and threshold grading table, it is determined whether the crop has entered a new growth stage, and the stage division results are obtained. Based on the stage division results, combined with soil moisture data and sensor accuracy information, if the detected moisture value is lower than the lower limit of the dynamic range, it is determined that the water is insufficient and a preliminary adjustment signal is generated. Based on the initial adjustment signal and acquisition frequency, adjust the data update cycle. If the humidity remains below the preset threshold during the update cycle, confirm the priority of the adjustment signal. The support vector machine algorithm is used to classify the confirmed regulatory signals and growth stage data to determine the specific category and intensity of the regulatory signals. By combining the categorized adjustment signals with water status and dynamic range data, targeted irrigation scheduling instructions are generated to determine the timing of irrigation execution. According to the irrigation scheduling instructions, the drip irrigation system is linked to perform water replenishment operations, and the changes in soil moisture during the execution process are recorded in real time to determine the feedback data of the regulation effect.

3. The method according to claim 1, characterized in that, Step S102 specifically includes: By adjusting the demand signal and combining it with dual threshold verification logic, the current growth period ratio data is obtained from the preset growth days table to determine the phased ratio setting result. Based on the phased ratio setting results, soil moisture values ​​and dynamic interval limit data are obtained. A humidity anomaly filtering mechanism is applied to determine whether there are abnormal fluctuations. If abnormal fluctuations are detected, the humidity data is smoothed to obtain a corrected humidity baseline value. Based on the corrected humidity baseline value and combined with the dynamic interval limit, the water volume increment is calculated, and a preset increment mapping table is used to determine the initial water volume adjustment direction. From the initial water volume adjustment direction, combined with the ratio adjustment step parameters, the adjustment step length constraint is obtained. If the step length exceeds the preset range, the step length is trimmed to obtain the optimized adjustment step length value. Based on the optimized adjustment step size value and combined with the adjustment time delay mechanism, time delay constraint data is obtained, and it is determined whether the delay period meets the execution conditions. If the conditions are met, the final proportional adjustment plan is generated. By combining the final proportional adjustment plan with the water volume increment and water volume adjustment value, a specific irrigation ratio instruction is generated, and the combination of execution parameters is determined. For each combination of execution parameters, the linkage system devices send out parameters, record real-time feedback data during the execution process, and obtain the final execution status confirmation.

4. The method according to claim 1, characterized in that, Step S103 specifically includes: The pressure regulating device, combined with the real-time monitoring system, continuously collects and transmits pressure data. The collected data stream is then processed into a time series to obtain an orderly pressure monitoring record. Based on the orderly pressure monitoring records and the preset constant pressure range, if the pressure value is detected to deviate from the range, the response mechanism of the pressure control module is triggered to determine the direction of the pressure deviation that needs to be adjusted. Given the determined direction of pressure deviation, the constraints for output power adjustment are obtained. Combined with the preset rules of the pressure stabilization mechanism, the specific magnitude of power adjustment is calculated, and the power adjustment command is obtained. The power adjustment command triggers the pressure control module to adjust the output power, while simultaneously collecting real-time feedback at the data acquisition frequency to determine whether the power adjustment has achieved the expected pressure stability. Based on the feedback results of the pressure steady state, combined with the weight of growth days and humidity correlation, the potential interference of environmental factors on pressure is analyzed, and comprehensive environmental correction parameters are obtained. Based on the comprehensive environmental correction parameters, the dynamic threshold of the pressure stabilization mechanism is adjusted, the fine-tuning range of the constant pressure range is updated synchronously, and the final pressure output state is determined.

5. The method according to claim 1, characterized in that, Step S104 specifically includes: By using a proportional adjustment scheme and combining water volume adjustment data, the initial proportion parameters of each component in the mixing device are obtained. Based on the deviation between the initial proportion parameters and the preset threshold, the direction and magnitude of the proportion adjustment are determined to obtain a preliminary proportion scheme. Based on the preliminary mixing scheme, the linkage flow control unit allocates the component ratio of water, fertilizer and pesticide in real time, and at the same time obtains the stability data of the current delivery environment from the pressure output status to determine whether the conditions for mixing execution are met. If the real-time allocation data of the flow control unit matches the stability data, the mixing device performs dynamic adjustment of the component ratio, while collecting humidity distribution information to obtain an environmentally adapted mixing ratio. Based on the environmental adaptation of the formulation results, combined with spatial data analysis, the adaptability differences of the delivery formula in different regions are obtained, and regionalized formulation optimization schemes are determined. Based on the regionalized proportion optimization scheme and referring to the proportion adjustment data in the historical records, the support vector machine algorithm is used to predictively adjust the proportion parameters to obtain the optimized proportion parameter set; By optimizing the set of proportioning parameters, the final configuration of the conveying formula is updated. Combined with real-time distribution data, it is determined whether the output of the mixing device has reached the expected proportioning balance state, and the final mixing and conveying scheme is obtained.

6. The method according to claim 1, characterized in that, Step S105 specifically includes: The mixed formula is delivered to the target area through the drip irrigation head and delivery pipeline. The pressure status data fluctuation is monitored in real time. Combined with the preset pressure threshold range, the stability of the delivery process is judged, and the preliminary results of pressure monitoring are obtained. Based on the preliminary results of pressure monitoring, the environmental sensors are linked to collect soil moisture information in the target area. The deviation between the collected moisture data and the preset moisture range is analyzed to determine the environmental adaptability and the adaptability of the moisture distribution. Based on the adaptation status of humidity distribution, data indicators related to crop growth are obtained. Real-time changes are recorded by environmental sensors and combined with preset growth requirement thresholds to determine whether crop growth conditions are met, thus obtaining growth adaptation assessment data. Based on the growth adaptation assessment data and the triggering conditions of the threshold alarm, if the soil moisture or growth index exceeds the preset range, the adjustment feedback mechanism is used to calculate the range of ratio fine-tuning and determine the adjustment scheme of the fine-tuning parameters. For the adjustment scheme of fine-tuning parameters, the support vector machine algorithm is used to predictively optimize the magnitude of the ratio fine-tuning. Combined with historical environmental data and crop growth records, the optimized ratio adjustment value is obtained. By optimizing the mixing ratio adjustment value, updating the delivery configuration of the mixed formula, and linking the control unit of the drip irrigation head, the mixing ratio in the delivery pipeline is adjusted in real time to obtain a dynamically adapted final delivery result. Based on the final delivery results, the soil moisture and crop growth status of the target area are continuously monitored through environmental sensors. Combined with the closed-loop control of the adjustment feedback mechanism, it is determined whether further fine-tuning is needed to obtain continuously adaptive delivery feedback data.

7. The method according to claim 1, characterized in that, Step S106 specifically includes: The corrected day deviation information is obtained through the growth day calibration mechanism. The deviation information is compared with the preset growth stage standard. If the deviation exceeds the preset threshold, the grading table update process is triggered to obtain the adjusted growth day grading data. Based on the adjusted growth days grading data, the corresponding dynamic range of soil moisture is obtained. Combined with the humidity information collected by the current environmental sensors, it is determined whether the humidity requirements of the current growth stage are met, and the matching status of the humidity range is determined. Based on the matching status of the humidity range, historical backtracking records of the ratio parameters are obtained, and the support vector machine algorithm is used to perform trend analysis on the historical records to obtain the predicted adjustment direction of the ratio parameters; By predicting and adjusting the direction of the proportioning parameters, and combining the real-time feedback data of the current delivery results, it is determined whether the delivery configuration needs to be corrected. If there is a deviation between the predicted direction and the real-time feedback, a new adjustment benchmark value is generated. Based on the new adjustment benchmark value, update the storage record of the proportioning parameters, and synchronously adjust the control logic of the conveying system to obtain the updated proportioning configuration scheme. Obtain the updated ratio and configuration scheme, continuously monitor soil moisture changes in conjunction with environmental sensors, and determine whether further optimization is needed based on the phased requirements of the growth day grading data, and determine the final delivery adjustment parameters. By transmitting the final adjustment parameters, updating the historical backtracking records within the system, and synchronously refreshing the reference data of the adjustment benchmark, the initialization conditions for the next adjustment are obtained.

8. An integrated water, fertilizer, and pesticide control system based on a drip irrigation head, characterized in that, The system includes: Acquisition Module: Through the built-in mixing device in the drip irrigation head, it acquires real-time data on the segmented intervals of the growing days and the accuracy level data of the soil moisture sensor. Combined with the triggering conditions for the switching of the growing days stage and the frequency of moisture data acquisition, it initially compares the growing days threshold grading table with the dynamic range of the soil moisture threshold to determine whether the current crop is in a specific growth stage and whether the soil moisture is insufficient, and obtains the initial adjustment demand signal. Adjustment module: Based on the initial adjustment demand signal, the module uses a dual-threshold cross-validation logic to determine the current growth period ratio setting based on the growth days threshold grading table. At the same time, it calculates the water volume increment parameter by combining the dynamic range of soil moisture threshold and the moisture abnormal fluctuation filtering mechanism. Simultaneously, it applies the ratio adjustment step size parameter and the ratio adjustment time delay mechanism to determine the specific ratio adjustment scheme and water volume adjustment value. Pressure regulation module: The pressure regulation device acquires real-time monitoring data of the current delivery pressure. If the detected delivery pressure deviates from the preset constant pressure range, the output power of the pressure control module is adjusted. The influence of the growth days and humidity correlation weight on the pressure demand is combined to stabilize the delivery pressure and obtain a stable pressure output state. Delivery module: Based on the proportional adjustment scheme and water volume adjustment value, combined with the stable pressure output state, the water-fertilizer-pesticide ratio parameters in the mixing device are adjusted. The proportion of each component is distributed in real time through the built-in flow control unit. At the same time, the final mixing and delivery formula is obtained by referring to the historical data of the ratio parameters and the spatial distribution analysis results of humidity data. Fine-tuning module: The final mixed delivery formula is obtained through the delivery pipe of the drip irrigation head and delivered to the target area under stable pressure. At the same time, the environmental response sensor continuously monitors soil moisture and crop growth status. Combined with threshold over-limit alarm triggering conditions and dual threshold adjustment feedback closed-loop mechanism, it determines whether to trigger the ratio fine-tuning to obtain dynamically adapted delivery results. Update module: Based on the dynamically adapted delivery results, the current day deviation data is corrected using a dynamic calibration mechanism for growth days. The growth day threshold grading table and the dynamic range of soil moisture threshold are updated synchronously. At the same time, the latest historical data of the ratio parameters are stored to determine the benchmark parameters for the next adjustment.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-7.

Images