Dynamic optimization and intelligent regulation and control system and method for drip irrigation pipes for intelligent agriculture

Abstract

The invention relates to the technical field of intelligent agriculture and water-saving irrigation equipment, and discloses a dynamic optimization and intelligent regulation and control system and method for a drip irrigation pipe for intelligent agriculture, and the system comprises a dynamic pipe optimization unit, a multi-source sensing monitoring module, and an intelligent regulation and control execution module. Through cooperation of a PID algorithm of the central control module and a fuzzy PID strategy, when the pressure deviation is larger than 0.05 MPa, Kp is increased for quick response, when the pressure deviation is smaller than or equal to 0.05 MPa, Ki is increased for stable pressure, dripper flow uniformity compensation is matched, and the dripper flow deviation is smaller than or equal to 5%; in an extreme weather emergency level, the irrigation interval is shortened, the flow speed is reduced and evaporation is reduced at high temperature and strong wind, heat tracing and heat preservation are performed at low temperature, pipes are thickened to prevent icing, irrigation is delayed and pressure is reduced when rainfall is predicted to be larger than 20 mm, and soil is prevented from being too wet; and meanwhile, unmanned aerial vehicle inspection is integrated, regional valves are automatically closed and the flow is adjusted when faults occur, and the overall uniformity is guaranteed. Finally, the water resource utilization rate is increased by 20%-25%, the crop yield is increased by 10%-15%, and the energy consumption per unit area is reduced by 30%.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart agriculture and water-saving irrigation equipment, in particular to a dynamic optimization and intelligent control system and method for drip irrigation pipe materials used in smart agriculture. BACKGROUND

[0002] In the field of smart agriculture water-saving irrigation, drip irrigation technology has become the mainstream due to its "precise water supply and high water resource utilization rate", but the traditional drip irrigation system has four major problems, which cannot meet the needs of large-scale and fine planting: Pipe material parameters are fixed and have poor adaptability: the material, pipe diameter and wall thickness of the traditional drip irrigation pipe material are pre-set at the factory, and cannot be dynamically adjusted according to the crop growth period (such as low water requirement in the seedling stage and high water requirement in the flowering and fruiting stage) and soil characteristics (different flow rates are required for sandy loam and clay), resulting in energy waste (more than 30%) in the seedling stage, insufficient water supply (irrigation uniformity < 85%) in the flowering and fruiting stage, or pipe rupture due to mismatch of pipe wall thickness and soil pressure (failure rate more than 15%).

[0003] Monitoring is extensive and control is lagging: the traditional system relies mainly on a single soil moisture sensor, lacks multi-dimensional monitoring of weather, pipe pressure and dripper flow, data update interval is more than 10 minutes, and there is no abnormal diagnosis function, so pipe blockage and sudden pressure changes are difficult to discover in time; control only uses simple on-off control without PID precise regulation, pipe flow fluctuation is more than ±10%, dripper flow deviation is more than 15%, and irrigation uniformity is low.

[0004] High energy consumption and serious resource waste: the traditional variable frequency pump has no fuzzy control strategy, the start-stop impact current is more than 2 times the rated current, which not only damages the equipment but also increases the energy consumption by more than 20%; and the irrigation plan is not adjusted in combination with weather forecasts (such as rainfall), so rainwater and irrigation water are superimposed to cause soil over-wetting (water content more than 90% of the field water holding capacity), or water evaporation loss is more than 30% when the flow rate is not optimized in high temperature.

[0005] Weak emergency and maintenance capability: the traditional system has no extreme weather emergency mechanism, the pipe is prone to freezing (pipe freeze cracking rate more than 20%) in low temperature and water evaporation is too fast in high temperature; and there is no pipe life prediction, so regular manual inspection is required, and if replacement is not timely, it may cause water leakage (water resource waste more than 10%), and there is no fault positioning means, so it takes more than 2 hours / acre to check the blockage or damage.

[0006] Therefore, there is an urgent need for a "dynamic adaptation, precise monitoring, intelligent control and reliable emergency" drip irrigation system to solve the above technical problems. SUMMARY

[0007] The purpose of the present application is to provide a dynamic optimization and intelligent control system and method for drip irrigation pipe materials used in smart agriculture to solve the problems raised in the background.

[0008] To achieve the above object, the present application provides the following technical solutions: a dynamic optimization and intelligent control system for drip irrigation pipe materials in smart agriculture, comprising a dynamic pipe material optimization unit, a multi-source sensing and monitoring module, an intelligent control execution module, a central control module, and a cloud data interaction platform; the dynamic pipe material optimization unit is used to dynamically match the material quality, pipe diameter, and wall thickness parameters of the drip irrigation pipe material according to the crop type, soil characteristics, and irrigation requirements, wherein the pipe material quality is selected from modified polyethylene or polyvinyl chloride composite nano calcium carbonate material, the anti-aging performance is improved by more than 30%, and the impact strength is greater than or equal to 8 kJ / m 2 ; the pipe diameter optimization range is 16-63 mm, the optimal value is determined by a hydraulic calculation model, the along-path water head loss calculation formula is , the flow rate in the pipe is controlled at 0.3-1.5 m / s, and the water head loss is less than or equal to 5 m / 100 m; the wall thickness is determined according to the soil cover depth and soil pressure, and the value range is 2.0-5.0 mm, which meets the static water pressure test of greater than or equal to 1.0 MPa; The multi-source sensing and monitoring module includes soil moisture sensor, soil temperature sensor, weather sensor, pipe material pressure sensor, and dripper flow sensor, all sensors are connected with the central control module through LoRa wireless communication, and data is aligned through time synchronization protocol; The intelligent control execution module includes a variable frequency constant pressure water supply pump, an electromagnetic regulating valve, and a pressure compensated dripper, and the valve opening and pump set speed are controlled by a pulse width modulation signal; The central control module adopts an industrial grade PLC controller, and a pipe material parameter optimization algorithm and an irrigation control PID algorithm are built-in: the optimization algorithm takes the highest irrigation uniformity and the lowest energy consumption as the target, and the target function is ; The PID algorithm is used for flow and pressure control, and the formula is , and the control accuracy is less than or equal to ±3%; The cloud data interaction platform supports data storage, crop irrigation model updating, and remote monitoring, and the data encryption adopts SM4 algorithm.

[0009] Preferably, the dynamic pipe material optimization unit further comprises a pipe material life prediction sub-module, which collects pipe material use environment parameters and operation parameters to construct a life prediction model , and the prediction accuracy is less than or equal to ±5%; when the predicted remaining life is less than or equal to 1 year, the system automatically pushes a pipe material replacement reminder to the management personnel terminal and matches the recommended pipe material parameters of the same type or upgraded model.

[0010] Preferably, the multi-source sensing and monitoring module further comprises a data preprocessing and abnormal diagnosis sub-module, and the data preprocessing adopts a sliding average filtering algorithm to remove random noise, and the noise suppression ratio is greater than or equal to 25 dB; the abnormal value processing adopts Criteria, a parameter sequence , ,..., , calculate the mean and standard deviation , if , it is determined as an abnormal value, and a linear interpolation method is used for correction; the abnormal value diagnosis submodule diagnoses the abnormal value by analyzing the correlation of the sensor data and combining the pipe pressure mutation threshold = 0.05 MPa, and the diagnosis accuracy is greater than or equal to 92%; the sensor node is provided with a solar power supply unit, supports low power automatic hibernation, and guarantees the continuity of monitoring.

[0011] Preferably, the intelligent control execution module further comprises a dripper flow uniformity compensation submodule, which collects pressure data at different positions through a pipe pressure sensor, calculates the pressure difference , and when > 0.03 MPa, the opening degree compensation of the end electromagnetic regulating valve is started: the adjustment formula is , is the opening degree of the end valve, is the opening degree of the inlet valve, = 0.1 MPa is the maximum allowable pressure difference), to ensure that the dripper flow deviation of the whole pipe section is less than or equal to 5%; The variable frequency constant pressure water supply pump adopts a fuzzy PID control strategy, and introduces a fuzzy rule base on the basis of a traditional PID: when the pressure deviation > 0.05 MPa, increases and reduces to quickly respond; when ≤ 0.05 MPa, reduces and increases to stabilize the pressure, the pressure control precision is improved to ± 1%, the pump set start-stop impact current is less than or equal to 1.5 times the rated current, and the pipe material is prevented from being damaged due to sudden pressure change.

[0012] Another technical problem to be solved by the present application is to provide a dynamic optimization and intelligent control method for drip irrigation pipe materials for smart agriculture, which is applied to the system as described above, and comprises the following steps: Step one, pipe parameter initialization, the central control module calls the cloud database, and according to the crop type, soil type and planting area, the pipe material, pipe diameter and wall thickness are preliminarily matched to generate an initial parameter scheme; Step two, multi-source data acquisition, the multi-source perception monitoring module collects soil moisture , soil temperature , rainfall , pipe pressure and dripper flow , data is transmitted to the central control module after preprocessing, wherein the soil moisture content is set as a threshold ; Step three, pipe material dynamic optimization, the central control module inputs the collected data into the optimization algorithm to calculate the irrigation uniformity of the current pipe material parameters and energy consumption , if or > , adjust the pipe diameter or wall thickness, generate the optimized parameter scheme and push it to the cloud for record; Step four, intelligent irrigation control, the central control module calculates the irrigation amount according to the soil moisture content deviation , combined with the meteorological forecast rainfall , calculates the irrigation amount , outputs the control signal to the frequency conversion pump and electromagnetic valve through the PID algorithm to adjust the water supply pressure and flow, and simultaneously collects the dripper flow feedback in real time, if Step five, feedback and update, after irrigation, the system calculates the irrigation uniformity and energy consumption of this time, compares with the historical data, if the deviation is more than 10%, corrects the optimization algorithm weight coefficient 、 , and updates the cloud crop irrigation model to ensure the accuracy of subsequent control.

[0013] Preferably, the step three pipe material dynamic optimization further comprises crop growth period adaptive adjustment: in the seedling stage, the crop water requirement is low, small diameter pipe material and low flow dripper are selected to reduce energy consumption; in the flowering and fruiting stage, large diameter pipe material and high flow dripper are switched, and the wall thickness is increased to withstand higher water supply pressure; in the post-harvest period, the irrigation frequency is reduced, and the small diameter pipe material parameters are restored; during the adaptive adjustment process, the system predicts the optimal parameters of the pipe material in different growth periods through a machine learning model, the model inputs are growth period days, historical water requirement, and soil moisture content change rate, and the outputs are pipe diameter, wall thickness, and dripper flow recommended value, with a prediction error of ≤8%.

[0014] Preferably, the step four intelligent irrigation control further comprises an extreme weather emergency strategy: when the meteorological sensor detects high temperature and wind speed > 5 m / s, the system shortens the irrigation interval and reduces the pipe flow rate to reduce water evaporation loss; when low temperature is detected, the water supply temperature is increased, and the pipe wall thickness is increased to enhance the thermal insulation property to prevent pipe freezing; when the predicted rainfall > 20 mm, delay irrigation and reduce the pipe pressure setting value to prevent soil from being too wet due to the superposition of rainwater and irrigation water. When the emergency strategy is triggered, the system sends an early warning information to the management personnel, and the normal control parameters are restored within 2 hours after the emergency is over.

[0015] Preferably, the system can also integrate an unmanned aerial vehicle inspection module, the unmanned aerial vehicle shoots the drip irrigation pipe distribution area according to a preset path, detects pipe damage, exposure or blockage through an image recognition algorithm, the damage recognition accuracy is greater than or equal to 95%, the blockage recognition is based on the temperature difference of the outlet area of the dripper, and after recognition, the fault position coordinates are generated and pushed to the regulation and control system, the system automatically closes the valve of the fault area and adjusts the pipe flow of other areas to ensure the overall irrigation uniformity.

[0016] The application provides a dynamic optimization and intelligent regulation and control system and method for drip irrigation pipes in smart agriculture. 1. According to the crop type (such as PE material for vegetables and PVC material for fruit trees), soil characteristics (20-32 mm pipe diameter for sandy loam soil and 16-25 mm pipe diameter for clay soil) and growth period requirements, the system can dynamically match parameters: small diameter (16-20 mm) low flow drippers (0.5-1.0 L / h) are switched in the seedling stage to reduce energy consumption, large diameter (32-40 mm) high flow drippers (2.0-3.0 L / h) are switched in the flowering and fruiting stage, and the pipe material is thickened (from 2.5 mm to 3.0 mm) to withstand high pressure, ensuring that the flow rate in the pipe is stable at 0.3-1.5 m / s, the water head loss is less than or equal to 5 m / 100 m, and the irrigation uniformity is improved to greater than or equal to 95%.

[0017] 2. The pipe life prediction sub-module collects environmental (soil pH, temperature) and operating (pressure fluctuation) parameters, predicts the life based on the model (accuracy less than or equal to ±5%), and automatically pushes a replacement reminder and recommends suitable pipe material when the remaining life is less than or equal to 1 year, avoiding water loss caused by sudden rupture (reducing water resource waste by more than 10%); the pipe material is modified PE / PVC composite nano calcium carbonate material, the aging resistance is improved by 30%, the impact strength is greater than or equal to 8 kJ / m 2 , the service life is extended to 5-8 years, and the maintenance cost is reduced by 40% compared with traditional pipe materials.

[0018] 3. The multi-source sensing module integrates five types of sensors such as soil moisture content (accuracy ±1%vol), weather (wind speed accuracy ±0.3 m / s), pipe pressure (accuracy ±0.02 MPa), etc., through LoRa wireless communication and PTP time synchronization (error less than or equal to 1 ms), data preprocessing uses sliding average filtering (noise suppression ratio greater than or equal to 25 dB) and 3σ abnormal correction to ensure the reliability of the monitoring data. 4, The application cooperates the PID algorithm of the central control module with the fuzzy PID strategy: when the pressure deviation is >0.05MPa, increase Kp to quickly respond, when ≤0.05MPa, increase Ki to stabilize the pressure, cooperate with the uniformity compensation of the flow rate of the dripper, so that the flow rate deviation of the dripper is ≤5%; in the emergency level of extreme weather, when the temperature is high and the wind is strong, shorten the irrigation interval, reduce the flow rate to reduce evaporation, when the temperature is low, heat preservation + thickening pipe material to prevent icing, when the predicted rainfall is >20mm, delay irrigation and reduce the pressure to avoid over-wetting of the soil; at the same time, unmanned aerial vehicle inspection is integrated, the regional valve is automatically closed and the flow rate is adjusted when there is a fault, and the overall uniformity is ensured. Ultimately, the water resource utilization rate is improved by 20%-25%, the crop yield is improved by 10%-15%, and the unit area energy consumption is reduced by 30%. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The system overall architecture and data flow chart of the application is shown in the figure; Figure 2 The dynamic pipe material optimization process and life prediction process chart of the application is shown in the figure; Figure 3 The multi-source sensing monitoring and data preprocessing process chart of the application is shown in the figure; Figure 4 The intelligent control execution and pressure compensation process chart of the application is shown in the figure; Figure 5 The extreme weather emergency strategy execution process chart of the application is shown in the figure. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0021] Examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the application, and cannot be understood as limiting the application.

[0022] Embodiment 1 The preferred embodiment of the intelligent agricultural drip irrigation pipe material dynamic optimization and intelligent control system and method provided by the application is as follows Figures 1-5The dynamic optimization and intelligent control system for drip irrigation pipe material for smart agriculture includes a dynamic pipe material optimization unit, a multi-source sensing and monitoring module, an intelligent control execution module, a central control module, and a cloud data interaction platform. The dynamic pipe material optimization unit is used to dynamically match the material quality, pipe diameter, and wall thickness parameters of the drip irrigation pipe material according to crop type, soil characteristics, and irrigation requirements. The pipe material quality is selected from modified polyethylene (PE) or polyvinyl chloride (PVC) composite nano calcium carbonate material (nano calcium carbonate addition amount 5%-10%), with aging resistance improved by more than 30% (tensile strength retention rate ≥85% after 1000h accelerated aging test), and impact strength ≥8kJ / m 2 ; The pipe diameter optimization range is 16-63mm, and the optimal value is determined by a hydraulic calculation model. The formula for calculating the head loss along the way is ( is the resistance coefficient along the way, is the pipe length, is the pipe diameter, is the water flow velocity in the pipe, is the acceleration of gravity), ensuring that the flow velocity in the pipe is controlled at 0.3-1.5m / s, and the head loss is ≤5m / 100m. The wall thickness is determined according to the soil cover depth (0.3-1.5m) and soil pressure (calculated according to , is the soil bulk density, is the cover depth), with a value range of 2.0-5.0mm, meeting the static water pressure test ≥1.0MPa (pressure retention 1h without leakage); The multi-source sensing and monitoring module includes soil moisture sensor (measurement range 0-100%vol, accuracy ±1%vol, sampling frequency 1 / 5min), soil temperature sensor (-20℃-60℃, accuracy ±0.5℃), weather sensor (collecting rainfall, wind speed, and light intensity, rainfall accuracy ±0.2mm, wind speed 0-30m / s ±0.3m / s), pipe pressure sensor (0-1.6MPa, accuracy ±0.02MPa), and dripper flow sensor (0.1-10L / h, accuracy ±0.05L / h). All sensors are connected to the central control module through LoRa wireless communication (transmission distance ≤3km, communication rate 125kbps), and data is aligned (synchronization error ≤1ms) through time synchronization protocol (PTP); The intelligent control execution module includes variable frequency constant pressure water supply pump (rated power 0.75-5.5kW, lift 10-50m, flow regulation range 0.5-50m 3 / h), electromagnetic regulating valve (passage diameter 15-50mm, response time ≤0.5s, control accuracy ±2%), and pressure compensated dripper (flow deviation ≤5%, working pressure range 0.1-0.3MPa), controlled by pulse width modulation (PWM) signal to control valve opening and pump set speed. The central control module adopts an industrial-grade PLC controller (CPU main frequency 1GHz, memory 1GB), with built-in pipe material parameter optimization algorithm and irrigation control PID algorithm: the optimization algorithm takes the highest irrigation uniformity and the lowest energy consumption as the goal, and the objective function is ( for the irrigation uniformity, for the unit area energy consumption, =0.6, =0.4 are weight coefficients); the PID algorithm is used for flow and pressure control, and the formula is ( for the actual value and the set value deviation, =2.5, =0.8, =0.3), and the control accuracy is ≤±3%; The cloud data interaction platform supports data storage (≥10 years of historical data), crop irrigation model updating (once a month), and remote monitoring (mobile APP / web page, update frequency ≤1s), and data encryption uses the SM4 algorithm (128-bit key).

[0023] The dynamic pipe material optimization unit also includes a pipe material life prediction sub-module, which collects pipe material use environment parameters (soil value 4.5-8.5, temperature -10℃-40℃, microbial content) and operating parameters (pressure fluctuation frequency, water flow rate change), and constructs a life prediction model ( for the standard environment life, for the pressure fluctuation value, for the temperature deviation, for the neutral value deviation, =0.02, =0.01, =0.03 are attenuation coefficients), and the prediction accuracy is ≤±5%; when the predicted remaining life is ≤1 year, the system automatically pushes the pipe replacement reminder to the administrator terminal, and matches the recommended same type or upgraded pipe material parameters (such as the original PE pipe material anti-aging is insufficient, the modified PE pipe material with 10% nanometer titanium dioxide is recommended, and the aging life is extended by 50%). In addition, the unit has a built-in pipe material database (containing more than 50 common drip irrigation pipe material parameters, including material composition, hydraulic property curve, weather resistance index), which supports updating the database parameters according to new crop varieties or soil improvement, and completes the pipe optimization algorithm adaptation within 24 hours after updating.

[0024] The multi-source perception monitoring module also includes a data preprocessing and anomaly diagnosis sub-module, which uses a sliding average filtering algorithm ( =10 is a filter window, is the first acquired data) removes random noise, noise suppression ratio ≥ 25dB; the abnormal value processing adopts criteria, a parameter sequence , ,..., , the mean value and the standard deviation are calculated, if , it is determined as an abnormal value, and a linear interpolation method is used for correction; the abnormal diagnosis sub-module diagnoses the sensor data correlation (such as soil moisture sudden drop but no irrigation record, it is determined that the pipe material may leak), in combination with the pipe pressure sudden change threshold =0.05MPa (if the pressure change exceeds the threshold within 10s, the leakage alarm is triggered), the diagnosis accuracy is ≥92%; the sensor node is equipped with a solar power supply unit (10W photovoltaic panel + 50Ah lithium battery, endurance ≥72h without light), supports low power automatic sleep (when the remaining power is ≤20%, the sampling frequency is reduced to 1 / 30min), and guarantees the monitoring continuity.

[0025] The intelligent control execution module also includes a dripper flow uniformity compensation sub-module, which acquires pressure data at different positions (inlet, middle, and end) through a pipe pressure sensor, calculates the pressure difference , and when >0.03MPa, the end electromagnetic regulating valve opening compensation is started: the adjustment formula is , is the end valve opening, is the inlet valve opening, =0.1MPa is the maximum allowable pressure difference), to ensure that the whole pipe section dripper flow deviation is ≤5%. The variable frequency constant pressure water supply pump adopts a fuzzy PID control strategy, which introduces a fuzzy rule base on the basis of the traditional PID: when the pressure deviation >0.05MPa, increases and reduces to quickly respond; when ≤0.05MPa, reduces and increases to stabilize the pressure, the pressure control precision is improved to ±1%, the pump set start-stop impact current is ≤1.5 times the rated current, and the pipe material is prevented from being damaged due to sudden pressure change.

[0026] Example 2 Please refer to Figures 1-5 , and on the basis of example 1, it is further obtained that another technical problem to be solved by the present application is to provide a dynamic optimization and intelligent control method for drip irrigation pipe material for smart agriculture, applied to the above system, comprising the following steps: Step 1: Pipe parameter initialization. The central control module calls the cloud database and, based on crop type (e.g., vegetables, fruit trees), soil type (sandy loam, clay), and planting area (1-100 acres), initially matches the pipe material (e.g., PE for vegetables, PVC for fruit trees), pipe diameter (20-32mm for sandy loam, 16-25mm for clay), and wall thickness (2.5mm for 0.5m coverage depth, 3.5mm for 1.0m coverage depth), generating an initial parameter scheme. Step 2: Multi-source data acquisition. The multi-source sensing and monitoring module collects soil moisture data at a set frequency. Soil temperature Rainfall Pipe pressure and dripper flow rate After preprocessing, the data is transmitted to the central control module, where soil moisture thresholds are set. (Vegetables require 80%-90% field water holding capacity, and fruit trees require 60%-70%). Step 3: Dynamic optimization of pipe materials. The central control module inputs the collected data into the optimization algorithm to calculate the irrigation uniformity of the current pipe material parameters. (According to Christensen's uniformity formula) , For single dropper flow rate, For average flow rate, (Number of drippers) and energy consumption If or > ( (For rated unit energy consumption), then adjust the pipe diameter (according to) , =95) or wall thickness (according to , This represents the actual soil pressure. To allow for pressure, an optimized parameter scheme is generated and pushed to the cloud for filing; Step four: Intelligent irrigation regulation. The central control module adjusts irrigation based on soil moisture deviations. Combined with meteorological forecasts of rainfall Calculate the required irrigation amount ( For soil bulk density, For root depth, To improve irrigation efficiency, a PID algorithm outputs control signals to the variable frequency pump and solenoid valve, adjusting the water supply pressure (0.15-0.25MPa) and flow rate (according to...). , (for irrigation duration), and simultaneously collect real-time dripper flow feedback; if necessary, pressure compensation will be activated. Step five, feedback and update, after irrigation, the system calculates the uniformity and energy consumption of this irrigation, compares with historical data, if the deviation is more than 10%, the optimization algorithm weight coefficient is corrected 、 , and the cloud crop irrigation model is updated to ensure the accuracy of subsequent regulation.

[0027] Step three, dynamic optimization of pipe material also includes adaptive adjustment of crop growth period: during seedling stage, the crop water requirement is low, small diameter pipe material (16-20mm) and low flow rate dripper (0.5-1.0L / h) are selected to reduce energy consumption; during flowering and fruiting stage, the water requirement is high, large diameter pipe material (32-40mm) and high flow rate dripper (2.0-3.0L / h) are switched, and the wall thickness is increased (from 2.5mm to 3.0mm) to withstand higher water supply pressure; during post-harvest period, irrigation frequency is reduced and small diameter pipe material parameters are restored. During the adaptive adjustment process, the system predicts the optimal parameters of pipe material at different growth stages through machine learning model (random forest algorithm), the model input is growth period, historical water requirement, and soil moisture change rate, and the output is pipe diameter, wall thickness, and dripper flow rate recommendation value, with a prediction error of ≤8%. In addition, for rotation scenarios (such as growing tomatoes in spring and Chinese cabbage in autumn), the system automatically completes pipe material parameter pre-optimization 72 hours before crop switching to avoid irrigation interruption.

[0028] Step four, intelligent irrigation regulation also includes extreme weather emergency strategy: when the weather sensor detects high temperature (T>35℃) and wind speed >5m / s, the system shortens the irrigation interval (from original 8h / time to 4h / time), and reduces the pipe flow rate (from 1.2m / s to 0.8m / s) to reduce water evaporation loss; when low temperature (T<5℃) is detected, the water supply temperature is increased (through pipe heating device to maintain water temperature at 10-15℃), and the pipe wall thickness is increased (from 3.0mm to 3.5mm) to enhance the insulation property to avoid pipe freezing; when the predicted rainfall >20mm, delay irrigation and reduce pipe pressure setting value (from 0.2MPa to 0.1MPa) to prevent rainwater and irrigation water from overlapping to cause soil over-wetting. When the emergency strategy is triggered, the system sends warning information (including emergency regulation parameters and reasons) to the management personnel, and restores the normal regulation parameters within 2 hours after the emergency ends.

[0029] The system can also integrate a drone inspection module. The drone (equipped with a 12-megapixel high-definition camera and a 640×512 resolution infrared thermal imager) will photograph the drip irrigation pipe distribution area along a preset path (inspecting once every 24 hours). Using an image recognition algorithm (improved YOLOv8 model), it will detect pipe damage, exposure, or blockage. Damage identification accuracy is ≥95%. Blockage identification is based on temperature differences in the dripper outlet area (the temperature around a blocked dripper is 5-8℃ lower). After identification, the system generates fault location coordinates (error ≤1m) and pushes them to the control system. The system automatically closes the valves in the fault area and adjusts the flow rate of pipes in other areas (according to...). , Traffic in the faulty area This represents the area of ​​the normal region. (Total area), ensuring overall irrigation uniformity; methodologically, soil moisture and crop growth images (collected by drones) can be combined, using crop growth models. ( Plant height, =0.5、 =0.3、 =0.2 is the coefficient, and I is the irrigation amount) to correct the irrigation amount and pipe material parameters, realize the whole closed loop of "sensing-optimization-control-feedback", and increase crop yield by 10%-15% and water resource utilization rate by 20%-25%.

[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0031] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic optimization and intelligent control system for drip irrigation pipes in smart agriculture, characterized in that, The system includes a dynamic pipe optimization unit, a multi-source sensing and monitoring module, an intelligent control and execution module, a central control module, and a cloud-based data interaction platform. The dynamic pipe optimization unit dynamically matches the material, diameter, and wall thickness parameters of the drip irrigation pipes based on crop type, soil characteristics, and irrigation requirements. The pipe material is selected from modified polyethylene or polyvinyl chloride composite nano-calcium carbonate material, which improves aging resistance by more than 30% and has an impact strength ≥8kJ / m². 2 The optimal pipe diameter range is 16-63mm. The optimal value is determined through a hydraulic calculation model. The formula for calculating the head loss along the pipe is as follows: Ensure that the flow velocity inside the pipe is controlled at 0.3-1.5m / s and the head loss is ≤5m / 100m; the wall thickness is determined according to the soil cover depth and soil pressure, with a range of 2.0-5.0mm, and meets the requirement of hydrostatic pressure test ≥1.0MPa; The multi-source sensing and monitoring module includes a soil moisture sensor, a soil temperature sensor, a meteorological sensor, a pipe pressure sensor, and a dripper flow sensor. All sensors are connected to the central control module via LoRa wireless communication, and the data is aligned using a time synchronization protocol. The intelligent control and execution module includes a variable frequency constant pressure water supply pump, an electromagnetic regulating valve, and a pressure-compensated dripper, which controls the valve opening and pump speed through pulse width modulation signals. The central control module uses an industrial-grade PLC controller, with built-in pipe parameter optimization algorithms and irrigation regulation PID algorithms: the optimization algorithms aim to maximize irrigation uniformity and minimize energy consumption, with the objective function being... ; PID algorithm is used for flow and pressure control, the formula is: Control accuracy ≤ ±3%; The cloud-based data interaction platform supports data storage, crop irrigation model updates, and remote monitoring. Data encryption uses the SM4 algorithm.

2. The smart agriculture drip irrigation pipe dynamic optimization and intelligent control system according to claim 1, characterized in that, The dynamic pipe optimization unit also includes a pipe life prediction submodule, which constructs a life prediction model by collecting environmental and operational parameters of the pipe. The prediction accuracy is ≤ ±5%; when the predicted remaining lifespan is ≤ 1 year, the system automatically pushes a pipe replacement reminder to the management personnel terminal and matches and recommends the same model or upgraded pipe parameters.

3. The smart agriculture drip irrigation pipe dynamic optimization and intelligent control system according to claim 1, characterized in that, The multi-source sensing and monitoring module also includes a data preprocessing and anomaly diagnosis submodule. The data preprocessing uses a moving average filtering algorithm. Remove random noise with a noise suppression ratio ≥25dB; outlier handling employs... Criterion, for a certain parameter sequence , ,..., Calculate the mean with standard deviation ,like If it is determined to be an outlier, linear interpolation will be used. Correction; the anomaly diagnosis submodule analyzes the correlation of sensor data and combines it with the pipe pressure change threshold. =0.05MPa, diagnostic accuracy ≥92%; the sensor node is equipped with a solar power unit, supports automatic sleep mode when the battery is low, and ensures continuous monitoring.

4. The smart agriculture drip irrigation pipe dynamic optimization and intelligent control system according to claim 1, characterized in that, The intelligent control and execution module also includes a dripper flow uniformity compensation submodule, which collects pressure data at different locations using a pipe pressure sensor and calculates the pressure difference. ,when When the pressure is >0.03MPa, the opening compensation of the terminal solenoid regulating valve is initiated: the adjustment formula is as follows. ( This refers to the opening degree of the end valve. For the opening degree of the imported valve, =0.1MPa is the maximum allowable pressure difference), ensuring that the dripper flow rate deviation is ≤5% for the entire pipe section; The variable frequency constant pressure water supply pump adopts a fuzzy PID control strategy, which introduces a fuzzy rule base on the basis of traditional PID: when the pressure deviation... When the pressure is >0.05 MPa, increase , reduce With rapid response; when When ≤0.05MPa, reduce Increase To stabilize pressure, the pressure control accuracy is improved to ±1%, and the pump start-up and shutdown impact current is ≤1.5 times the rated current, thus avoiding damage to the pipes due to sudden pressure changes.

5. A method for dynamic optimization and intelligent control of drip irrigation pipes for smart agriculture, applied to the system described in any one of claims 1-4, characterized in that, Includes the following steps: Step 1: Pipe parameter initialization. The central control module calls the cloud database to initially match the pipe material, pipe diameter and wall thickness according to the crop type, soil type and planting area, and generate an initial parameter scheme. Step 2: Multi-source data acquisition. The multi-source sensing and monitoring module collects soil moisture data at a set frequency. Soil temperature Rainfall Pipe pressure and dripper flow rate After preprocessing, the data is transmitted to the central control module, where soil moisture thresholds are set. ; Step 3: Dynamic optimization of pipe materials. The central control module inputs the collected data into the optimization algorithm to calculate the irrigation uniformity of the current pipe material parameters. Energy consumption If or > If so, adjust the pipe diameter or wall thickness, generate an optimized parameter scheme, and push it to the cloud for filing. Step four: Intelligent irrigation regulation. The central control module adjusts irrigation based on soil moisture deviations. Combined with meteorological forecasts of rainfall Calculate the required irrigation amount The system outputs control signals to the variable frequency pump and solenoid valve through the PID algorithm to adjust the water supply pressure and flow rate, while collecting the dripper flow feedback in real time. If the feedback is insufficient, pressure compensation is activated. Step 5: Feedback and Updates. After irrigation, the system calculates the irrigation uniformity and energy consumption, compares it with historical data, and adjusts the weight coefficients of the optimization algorithm if the deviation exceeds 10%. , And update the cloud-based crop irrigation model to ensure the accuracy of subsequent regulation.

6. The method for dynamic optimization and intelligent control of drip irrigation pipes for smart agriculture according to claim 5, characterized in that, Step three, dynamic optimization of pipe materials, also includes adaptation adjustments for crop growth stages: during the seedling stage, when crop water requirements are low, small-diameter pipes and low-flow emitters are selected to reduce energy consumption; during the flowering and fruiting stage, when water requirements are high, large-diameter pipes and high-flow emitters are switched, while the wall thickness is increased to withstand higher water supply pressure; in the later stages of harvest, irrigation frequency is reduced, and small-diameter pipe parameters are restored; during the adaptation adjustment process, the system uses a machine learning model to predict the optimal parameters of pipe materials for different growth stages. The model inputs are the number of days in the growth stage, historical water requirements, and soil moisture change rate, and the outputs are recommended values ​​for pipe diameter, wall thickness, and emitter flow rate, with a prediction error ≤8%.

7. The method for dynamic optimization and intelligent control of drip irrigation pipes for smart agriculture according to claim 5, characterized in that, Step four, intelligent irrigation control, also includes extreme weather emergency strategies: when the meteorological sensor detects high temperatures and wind speeds >5m / s, the system shortens the irrigation interval and reduces the water flow velocity in the pipes to minimize water evaporation loss; when low temperatures are detected, the system increases the water supply temperature and increases the pipe wall thickness to enhance insulation and prevent icing inside the pipes; when rainfall is predicted... When the water level exceeds 20mm, irrigation is delayed and the pipe pressure setting is reduced to prevent the soil from becoming overly wet due to the accumulation of rainwater and irrigation water. When the emergency strategy is triggered, the system sends a warning to administrators, and normal control parameters are restored within 2 hours after the emergency ends.

8. The dynamic optimization and intelligent control system and method for drip irrigation pipes in smart agriculture according to any one of claims 1-7, characterized in that, The system can also integrate a drone inspection module. The drone takes pictures of the drip irrigation pipe distribution area according to a preset path and uses image recognition algorithms to detect pipe damage, exposure or blockage. The damage recognition accuracy is ≥95%. Blockage recognition is based on the temperature difference of the dripper outlet area. After recognition, the fault location coordinates are generated and pushed to the control system. The system automatically closes the valve in the fault area and adjusts the flow rate of pipes in other areas to ensure the overall irrigation uniformity.

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