Intelligent remote control method and system for irrigation electric valve

Abstract

The application belongs to the technical field of control, and particularly relates to an intelligent remote control method and system for an irrigation electric valve, comprising the following steps: S1, obtaining a target soil humidity setting value and a current soil humidity actual value of an irrigation area; based on network communication quality parameters between a remote control terminal and the electric valve, pipe network pressure sensor data and historical instruction execution data, a predicted execution lag time of a current control period is calculated; S2, using a preset water-soil model, a theoretical predicted value of the soil humidity is calculated according to valve opening degree and environmental parameters of the last period, and a model deviation between the theoretical predicted value and the current soil humidity actual value is obtained. Through comprehensive analysis of network quality, pipe network pressure and historical data, the application can pre-identify and compensate for execution lag in the control process, avoid irrigation overabundance or deficiency caused by delay, and improve the stability of control.

Description

Technical Field

[0001] This invention belongs to the field of control technology, specifically relating to an intelligent remote control method and system for an irrigation electric valve. Background Technology

[0002] Remote intelligent irrigation systems can not only improve water resource utilization but also increase crop yields. Existing intelligent irrigation systems typically acquire real-time data through soil moisture sensors deployed in the field. A remote control center then sends on / off commands to electric valves in the irrigation area based on preset humidity thresholds, achieving automatic irrigation. However, the entire process—from the control center issuing the command to its transmission via wireless or wired network to the field controller, and finally to the electric valve completing the mechanical action—involves a lag. This lag often leads to control overshoot, causing unnecessary over-irrigation or under-irrigation, resulting in water waste and adverse effects on crop roots.

[0003] PID control, applied to irrigation systems, considers the control process, but fluctuations in network latency in remote irrigation systems can affect the arrival time of control commands. Traditional PID controllers struggle to handle these randomly changing pure time delays. The response of an irrigation system is influenced by a combination of factors, including pipe network water pressure, soil texture, ambient temperature and humidity, and wind speed. When actual conditions deviate from preset parameters, such as a valve failing to fully execute its opening command, deviations occur, leading to decreased control accuracy. Current technical solutions generally lack a mechanism for online evaluation and compensation of these model deviations, and fail to integrate information such as network quality and historical performance into control decisions to adjust control strategies, thus limiting the stability of irrigation control. Summary of the Invention

[0004] This invention provides an intelligent remote control method and system for irrigation electric valves to solve the technical problems of insufficient accuracy and stability caused by lag fluctuations, lack of compensation for model deviations, and lack of fusion of multi-source information in the prior art.

[0005] In a first aspect, the present invention provides an intelligent remote control method for an irrigation electric valve, comprising the following steps:

[0006] S1, obtain the target soil moisture setpoint and the current actual soil moisture value of the irrigation area; based on the network communication quality parameters between the remote control terminal and the electric valve, the pipeline pressure sensor data, and the historical command execution data, calculate the predicted execution lag time of the current control cycle;

[0007] S2, using a preset soil and water model, calculates the theoretical prediction value of soil moisture based on the valve opening and environmental parameters of the previous cycle, and obtains the model deviation between the theoretical prediction value and the current actual soil moisture value; when the model deviation exceeds the first preset threshold, queries the electric valve for the actual operating status, and corrects the soil and water model based on the query result.

[0008] S3, based on the current target difference between the current actual soil moisture value and the target soil moisture setpoint, the target difference of the previous cycle, the predicted execution lag time, and the model deviation, calculate the valve opening adjustment amount for the next control cycle; the predicted execution lag time is used to determine the control gain attenuation coefficient, and the model deviation is used to determine the adjustment magnitude weight;

[0009] S4: Generate a control command with the sum of the current valve opening and the valve opening adjustment amount as the target opening, and set the expected execution time of the control command in combination with the predicted execution lag time, and then send it to the electric valve; determine whether the absolute value of the current target difference is less than the second preset threshold, and whether the moving average of the target difference change rate is less than the change rate threshold within a continuous preset number of control cycles. When both conditions are met, end the current irrigation control.

[0010] Furthermore, based on the network communication quality parameters between the remote control terminal and the electric valve, pipeline pressure sensor data, and historical command execution data, the predicted execution lag time for the current control cycle is calculated, including:

[0011] Based on the historical command issuance timestamps, command reception timestamps from electric valve feedback, command completion timestamps, and pipeline pressure data during execution, a relationship model between command execution delay and pipeline pressure is established.

[0012] Obtain the round-trip time and packet loss rate of network data packets between the terminal and the electric valve at the current moment, and calculate the predicted transmission delay under the current network conditions;

[0013] Acquire current pipeline pressure sensor data and use a relational model to predict the current command execution delay;

[0014] The predicted transmission delay is added to the predicted current instruction execution delay to obtain the predicted execution lag time for the current control cycle.

[0015] Furthermore, when the model deviation exceeds a first preset threshold, the actual operating status is queried from the electric valve, and the water and soil model is corrected based on the query results, including:

[0016] The first preset threshold is set to 5% of the target soil moisture setting value;

[0017] Query the electric valve for the actual average opening degree and cumulative opening duration in the previous control cycle;

[0018] Based on the actual average opening degree and cumulative opening time obtained from the query, and combined with the pipeline pressure, the actual irrigation water volume of the previous cycle is recalculated;

[0019] Using the actual irrigation water volume as input, the soil infiltration rate parameter in the soil and water model is adjusted through an optimization algorithm to minimize the error between the moisture prediction value of the soil and water model under the input and the actual current soil moisture value.

[0020] Furthermore, the predicted execution lag time is used to determine the control gain decay coefficient, including:

[0021] Calculate the control gain attenuation coefficient using the following formula. : ,in, To predict execution lag time; is the preset decay constant; exp is the natural exponential function.

[0022] Furthermore, the model bias is used to determine the adjustment magnitude weights, including:

[0023] The adjustment magnitude weight W is calculated using the following formula: ,in, This represents the absolute value of the model deviation. The preset weighting factor.

[0024] Furthermore, based on the current target difference between the actual soil moisture value and the target soil moisture setpoint, the target difference of the previous cycle, the prediction execution lag time, and the model bias, the valve opening adjustment for the next control cycle is calculated, including:

[0025] The valve opening adjustment is calculated using the following improved incremental proportional-integral control algorithm. : Where W is the adjustment magnitude weight; For proportional gain; This is the integral gain; This is the current target difference; This is the target difference from the previous period; To control the gain attenuation coefficient.

[0026] Furthermore, after setting the expected execution time of the control command based on the predicted execution lag time, it is sent to the electric valve, including:

[0027] Get the current system timestamp of the control terminal ;

[0028] Predict execution lag time Compared with the current system timestamp Add them together to get the expected execution time. ;

[0029] This will include the target opening size and the expected execution time. The control command data packet is encapsulated and sent to the electric valve via a wireless communication module.

[0030] Furthermore, in S4, the rate of change threshold is set based on the prediction execution lag time, including:

[0031] The rate of change threshold is calculated using the following linear function. : ,in, The basic rate of change threshold; The lag effect coefficient; To predict execution lag time.

[0032] Furthermore, the control command data packet is sent to the electric valve via the NB-IoT module.

[0033] Secondly, the present invention provides an intelligent remote control system for an irrigation electric valve, comprising a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent remote control method for the irrigation electric valve is implemented.

[0034] The beneficial effects are as follows: By comprehensively analyzing network quality, pipeline pressure, and historical data, this invention can pre-identify and compensate for execution lags in the control process, avoiding over- or under-irrigation caused by delays and improving control stability. By comparing the theoretical predictions of the soil and water model with the actual sensor values, model deviations can be identified and used to correct subsequent control decisions, even when the actual valve operating state does not match the command or when environmental factors change abruptly. When calculating the valve opening adjustment and setting the irrigation termination conditions, this method uses the predicted lag time and model deviation as key adjustment factors, enabling the control output to respond quickly to target deviations while suppressing system oscillations. This ensures that the system can smoothly stop after reaching the target humidity, achieving more precise soil moisture management, saving water resources, and guaranteeing irrigation quality. Attached Figure Description

[0035] Figure 1 A flowchart for an intelligent remote control method for irrigation electric valves. Detailed Implementation

[0036] 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, not all, of the embodiments of the present invention. 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.

[0037] An embodiment of the intelligent remote control method for irrigation electric valves provided by the present invention:

[0038] like Figure 1 As shown, the intelligent remote control method for irrigation electric valves includes the following steps:

[0039] S1: Obtain the target soil moisture setpoint and the current actual soil moisture value collected by the sensor in the irrigation area; based on the network communication quality parameters between the remote control terminal and the electric valve, the pipeline pressure sensor data, and the historical command execution data, calculate the predicted execution lag time of the current control cycle.

[0040] Remote control terminals, such as mobile apps or web pages, pre-set and store target soil moisture settings, such as 65% relative humidity. Soil moisture sensors deployed in the irrigation area periodically collect the actual current soil moisture value via wireless modules such as LoRa, such as 50% relative humidity, and upload the data to the central control server. Network communication latency is obtained by pinging the electric valve controller or recording the round-trip time of data packets, for example, a one-way latency of 300ms. Based on the readings of the pipeline pressure sensor, the hydraulic latency is found from a preset pressure-water flow delivery time relationship table, for example, 0.2MPa pressure corresponds to a hydraulic latency of 180s. The actual time records from the time the most recent irrigation commands were sent to the humidity sensor to start responding are retrieved, and a weighted average is calculated. Combined with the above latency, the predicted execution lag time for the current control cycle is obtained, for example, a total of 210s.

[0041] In an optional embodiment, based on network communication quality parameters between the remote control terminal and the electric valve, pipeline pressure sensor data, and historical command execution data, the predicted execution lag time for the current control cycle is calculated, including:

[0042] Based on the historical command issuance timestamps, command reception timestamps from electric valve feedback, command completion timestamps, and pipeline pressure data during execution, a relationship model between command execution delay and pipeline pressure is established.

[0043] Obtain the round-trip time and packet loss rate of network data packets between the terminal and the electric valve at the current moment, and calculate the predicted transmission delay under the current network conditions;

[0044] Acquire current pipeline pressure sensor data and use a relational model to predict the current command execution delay;

[0045] The predicted transmission delay is added to the predicted current instruction execution delay to obtain the predicted execution lag time for the current control cycle.

[0046] Specifically, historical data is analyzed, such as a database containing 1000 instruction execution records. Each record includes the instruction issuance time, valve reception time, execution completion time, and average pipeline pressure during the period. Regression analysis reveals that when the pipeline pressure increases from 1.5 MPa to 3.0 MPa, the valve's execution delay from fully closed to partially open increases from 25 seconds to 35 seconds. Based on this, a relational model is established; for example, the instruction execution delay equals 15 plus 6.67 multiplied by the pipeline pressure value. At the start of the current control cycle, a test data packet is sent to the valve and a response is received. The measured network data packet round-trip time (RTT) is 200 ms, with a packet loss rate of 1%. Based on the data, the predicted transmission delay under the current network condition is calculated to be 202 ms, or 0.202 seconds. The pipeline pressure sensor is read, and the current pressure value is 2.5 MPa. Substituting this pressure value into the previously established relational model, the predicted current instruction execution delay is 31.68 seconds. Adding the predicted transmission delay to the predicted current instruction execution delay, we get the predicted execution lag time for the current control cycle as 31.882s.

[0047] S2, using a preset irrigation area water and soil model, calculates the theoretical prediction value of soil moisture based on the valve opening and environmental parameters of the previous cycle, and obtains the model deviation between the theoretical prediction value and the current actual soil moisture value; when the model deviation exceeds the first preset threshold, queries the electric valve for the actual operating status, and corrects the water and soil model based on the query result.

[0048] The soil-water model is a simplified water balance equation. It calculates the theoretical inflow based on the valve opening (e.g., 60%) from the previous cycle command and the current pipeline pressure. It then estimates evaporation by combining environmental parameters such as temperature and air humidity, thus predicting the theoretical soil moisture value, for example, 58%. This theoretical value is compared with the actual value (55%) collected by the sensor, resulting in a model deviation of -3%. If the absolute value of this deviation (3%) exceeds a first preset threshold (e.g., 2%), a status query command is immediately sent to the electric valve. The electric valve, through its built-in angle sensor, may report an actual opening of only 52%, not the commanded 60%. The theoretical value is recalculated using the actual opening of 52%. If the recalculated theoretical value is close to the actual value, the deviation is attributed to a valve malfunction. If there is still a significant deviation, parameters such as the soil infiltration coefficient in the soil-water model are adjusted accordingly to better reflect the actual situation.

[0049] In an optional embodiment, when the model deviation exceeds a first preset threshold, the actual operating status is queried from the electric valve, and the soil and water model is corrected based on the query result, including:

[0050] The first preset threshold is set to 5% of the target soil moisture setting value;

[0051] Query the electric valve for the actual average opening degree and cumulative opening duration in the previous control cycle;

[0052] Based on the actual average opening degree and cumulative opening time obtained from the query, and combined with the pipeline pressure, the actual irrigation water volume of the previous cycle is recalculated;

[0053] Using the actual irrigation water volume as input, the soil infiltration rate parameter in the soil and water model is adjusted through an optimization algorithm to minimize the error between the moisture prediction value of the soil and water model under the input and the actual current soil moisture value.

[0054] Assuming the target soil moisture setting is 40%, the first preset threshold is 2%. At the end of a certain control cycle, the soil moisture predicted by the water and soil model is 37.5%, while the actual value measured by the sensor is 40.2%, a deviation of -2.7%. Since the absolute value of this deviation exceeds the preset threshold of 2%, a correction process will be initiated. A query command is sent to the electric valve, which reports an actual average opening degree of 45% and a cumulative opening time of 600 seconds in the previous control cycle. Combining this with the recorded average pipeline pressure of 2.2 MPa and the valve flow characteristic curve, the actual irrigation water volume for the previous cycle is recalculated to be 5.5. Instead of the 5.0 originally estimated by the model. At 5.5 Using verified actual irrigation water volume as input, optimization algorithms such as gradient descent are applied to adjust the soil and water model. The algorithm iteratively modifies the soil infiltration rate parameter in the model, for example, adjusting it from 0.012 to 0.011, until the model uses the new parameter and 5.5. The humidity prediction calculated from the irrigation amount is closest to the actual measured value of 40.2%, thus completing the calibration of the soil and water model.

[0055] S3, based on the current target difference between the current actual soil moisture value and the target soil moisture setpoint, the target difference of the previous cycle, the predicted execution lag time, and the model deviation, calculate the valve opening adjustment amount for the next control cycle; the predicted execution lag time is used to determine the control gain attenuation coefficient, the model deviation amount is used to determine the adjustment magnitude weight, and the control gain attenuation coefficient and the adjustment magnitude weight together determine the valve opening adjustment amount.

[0056] Specifically, the current target difference is 10%, and the target difference in the previous cycle was 12%. The predicted execution lag time is 210 seconds. A small control gain attenuation coefficient, such as 0.7, is calculated based on a preset function to avoid excessive adjustment due to long lag. A model deviation of -3% indicates that the actual water volume is less than expected. Therefore, an adjustment magnitude weight greater than 1, such as 1.2, is calculated to strengthen the adjustment. The valve opening adjustment is calculated using a PD-like algorithm. The base value is determined by the current target difference and the change in the difference. Then, it is multiplied by the control gain attenuation coefficient of 0.7 and the adjustment magnitude weight of 1.2 to calculate a specific opening adjustment value, such as increasing the opening by 6%.

[0057] In an optional embodiment, predicting the execution lag time to determine the control gain attenuation coefficient includes:

[0058] Calculate the control gain attenuation coefficient using the following formula. : ,in, The predicted execution lag time is expressed in seconds (s). is the preset decay constant; exp is the natural exponential function.

[0059] Specifically, within a control cycle, the predicted execution lag time is calculated. The duration is 31.882 s. A preset decay constant is used. =0.15s.

[0060] According to the given formula, control the gain attenuation coefficient. The calculated result is approximately 0.0084. A control gain attenuation coefficient much smaller than 1 will be used in subsequent control algorithms. It weakens the effect of the integral term, thus preventing overly aggressive control actions that could lead to irrigation water overshoot when there is a significant delay in the system response.

[0061] In an optional embodiment, the model bias is used to determine the adjustment magnitude weights, including:

[0062] The adjustment magnitude weight W is calculated using the following formula: ,in, This represents the absolute value of the model deviation, expressed as a percentage. The preset weighting factor.

[0063] Assuming the soil moisture model predicts a soil moisture content of 38%, while the actual sensor measurement is 40.7%, then the model bias is... It is -2.7%. (Absolute value) That is, 2.7. The preset weighting influence factor. The value is 0.5. Substituting the value into the formula for calculation, the adjustment magnitude weight W is approximately equal to 0.4255. This indicates that because the current model prediction is not accurate enough, the subsequently calculated valve opening adjustment amount will be multiplied by this weight to reduce it, making the adjustment action of the control system more gradual and conservative, and reducing the risk of incorrect adjustment due to model inaccuracy.

[0064] In an optional embodiment, based on the current target difference between the current actual soil moisture value and the target soil moisture setpoint, the target difference of the previous cycle, the prediction execution lag time, and the model deviation, the valve opening adjustment amount for the next control cycle is calculated, including:

[0065] The valve opening adjustment is calculated using the following improved incremental proportional-integral control algorithm. : Where W is the adjustment magnitude weight; For proportional gain; This is the integral gain; This is the current target difference; This is the target difference from the previous period; To control the gain attenuation coefficient.

[0066] In one control instance, the target soil moisture is 45%, and the current actual moisture is 40.7%, therefore the current target difference is... The value is 4.3. The actual humidity in the previous cycle was 40%, therefore the target difference for the previous cycle is... The value is 5.0. Based on the above calculations, the adjustment magnitude weight W is 0.4255, and the control gain attenuation coefficient is... The set proportional gain is 0.0084. The integral gain is 1.2. The value is 0.3. Substituting all the above values ​​into the improved incremental PI control algorithm formula, the proportional part is calculated, yielding -0.84. The integral part is calculated, yielding approximately 0.0108. Adding the two parts together yields -0.8292. Multiplying the result by the adjustment magnitude weight of 0.4255, the valve opening adjustment amount is calculated. Approximately -0.35. This indicates that in the next control cycle, the valve opening needs to be reduced by 0.35% compared to the current opening.

[0067] S4 generates a control command with the sum of the current valve opening and the valve opening adjustment amount as the target opening, and sends the expected execution time of the control command to the electric valve after setting the expected execution time in combination with the predicted execution lag time; it determines whether the absolute value of the current target difference is less than the second preset threshold, and whether the moving average of the target difference change rate is less than the change rate threshold within a consecutive preset number of control cycles. The change rate threshold is set according to the predicted execution lag time. When both conditions are met, the irrigation control ends.

[0068] Assuming the valve is currently open at 60%, and the calculated adjustment is an increase of 6%, the new target opening is 66%. A control command containing the target opening of 66% is generated. Based on the predicted execution lag time of 210 seconds, the expected execution time of the command is set to the current time plus 210 seconds. This complete command, containing the target opening and the future execution timestamp, is packaged and sent to the field controller of the electric valve via the network. After receiving the command, the controller will wait for the local time to reach the timestamp before performing the opening adjustment action. Check the current target difference, for example, the difference between the target of 65% and the actual 64.5% is 0.5%, and the absolute value is less than the second preset threshold, such as 1%; record the rate of change of the target difference in the last 5 control cycles and calculate the moving average; the rate of change threshold will be adjusted according to the prediction execution lag time. The longer the lag time, the smaller the rate of change threshold is set, and the higher the standard of stability required; for example, a long lag of 210s corresponds to a very small rate of change threshold, such as 0.02% / min; when the calculated moving average is less than this threshold, it indicates that the soil moisture has entered a very stable convergence state; at this time, both conditions are met at the same time, the irrigation is determined to be completed, and a command is sent to completely close the electric valve.

[0069] In an optional embodiment, after setting the expected execution time of the control command in conjunction with the predicted execution lag time, it is sent to the electric valve, including:

[0070] Get the current system timestamp of the control terminal ;

[0071] Predict execution lag time Compared with the current system timestamp Add them together to get the expected execution time. ;

[0072] This will include the target opening size and the expected execution time. The control command data packet is encapsulated and sent to the electric valve via a wireless communication module.

[0073] Specifically, the control terminal obtains the current system timestamp. For example, the time is 14:30:05. The predicted execution lag time for this control command has been calculated. The value is 31.882s. Adding the two times together, we can calculate the expected execution time of the instruction. That is, 14:30:36.882. The calculated target valve opening, for example 49.65%, is compared with the expected execution time. All of these are encapsulated in a control command data packet. This control command data packet is sent to the remote electric valve via a wireless communication module, such as an NB-IoT module. After receiving the command, the electric valve does not execute it immediately, but waits for its internal clock to reach 14:30:36.882 before it begins to precisely perform the opening adjustment action.

[0074] In an optional embodiment, the rate of change threshold is set based on the prediction execution lag time, including:

[0075] The rate of change threshold is calculated using the following linear function. : ,in, The unit is humidity % / min; The basic rate of change threshold; The lag effect coefficient; The unit for predicting execution lag time is seconds (s).

[0076] Specifically, assuming a set baseline rate of change threshold The lag effect coefficient is 0.05% / min. Each minute and each percentage point corresponds to 0.00033 seconds. The calculated predictive execution lag time is given within the current control cycle. It is 31.882s.

[0077] According to the formula, the basic rate of change threshold is added to the product of the lag effect coefficient and the predicted execution lag time. The result is approximately 0.06052. This is the rate of change threshold for the current control period. The setting is 0.06052% / min. This indicates that when the system latency is large, the tolerance for sudden changes in soil moisture data will increase. Only when the rate of change in moisture exceeds a higher threshold will it be judged as abnormal and trigger corresponding processing, thereby avoiding overreaction to normal but slightly drastic moisture changes due to information delay.

[0078] An embodiment of the intelligent remote control system for irrigation electric valves provided by this invention:

[0079] The intelligent remote control system for irrigation electric valves includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned intelligent remote control method for irrigation electric valves is implemented.

[0080] The intelligent remote control system for irrigation electric valves also includes other components well known to those skilled in the art, such as communication interfaces. Their setup and functions are known in the art and will not be described in detail here.

[0081] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0082] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method of intelligent remote control of an irrigation electric valve, characterized in that, The method comprises the following steps: S1, obtaining a target soil moisture setting value and a current soil moisture actual value of an irrigation area; Based on the network communication quality parameters between the remote control terminal and the electric valve, the pipe network pressure sensor data and the historical instruction execution data, the predicted execution lag time of the current control period is calculated; S2, using a preset water-soil model, the theoretical prediction value of the soil moisture is calculated according to the valve opening degree and the environmental parameters of the last period, and the model deviation between the theoretical prediction value and the current soil moisture actual value is obtained; when the model deviation exceeds the first preset threshold, the actual running state of the electric valve is queried, and the water-soil model is corrected based on the query result; S3, based on the current target difference between the current soil moisture actual value and the target soil moisture setting value, the target difference of the last period, the predicted execution lag time and the model deviation, the valve opening degree adjustment amount of the next control period is calculated; The predicted execution lag time is used to determine the control gain attenuation coefficient, and the model deviation is used to determine the adjustment amplitude weight; S4, a control instruction with the target opening degree being the sum of the current valve opening degree and the valve opening degree adjustment amount is generated, and after the expected execution time of the control instruction is set in combination with the predicted execution lag time, the control instruction is sent to the electric valve; whether the absolute value of the current target difference is less than the second preset threshold and whether the moving average value of the target difference change rate in the continuous preset number of control periods is less than the change rate threshold are judged, and when the two conditions are met at the same time, the current irrigation control is ended.

2. The method of claim 1, wherein, Based on the network communication quality parameters between the remote control terminal and the electric valve, the pipe network pressure sensor data and the historical instruction execution data, the predicted execution lag time of the current control period is calculated, which comprises: Based on the historical instruction issue time stamp, the instruction receiving time stamp and the instruction execution completion time stamp fed back by the electric valve, and the pipe network pressure data during execution, a relationship model between the instruction execution time delay and the pipe network pressure is established; The network data packet round trip time and the packet loss rate between the terminal and the electric valve at the current time are obtained, and the predicted transmission time delay under the current network state is calculated; The current pipe network pressure sensor data is obtained, and the current instruction execution time delay is predicted by using the relationship model; The predicted transmission time delay and the predicted current instruction execution time delay are added to obtain the predicted execution lag time of the current control period.

3. The method of claim 1, wherein, When the model deviation exceeds the first preset threshold, the actual running state of the electric valve is queried, and the water-soil model is corrected based on the query result, which comprises: The first preset threshold is set to 5% of the target soil moisture setting value; The actual average opening degree and the cumulative opening time of the electric valve in the last control period are queried; According to the queried actual average opening degree and the cumulative opening time, the actual irrigation water quantity of the last period is recalculated in combination with the pipe network pressure; The actual irrigation water quantity is taken as input, the soil infiltration rate parameter in the water-soil model is adjusted by using an optimization algorithm, so that the error between the humidity prediction value of the water-soil model under the input and the current soil moisture actual value is minimized.

4. The method of claim 1, wherein, The predicted execution lag time is used to determine the control gain attenuation coefficient, which comprises: The control gain attenuation coefficient is calculated by the following formula : , wherein, is a predicted execution lag time; is a preset attenuation constant; and exp is a natural exponential function.

5. The method of intelligent remote control of irrigation motorized valves according to claim 1, characterized in that, The model deviation is used to determine the adjustment amplitude weight, which comprises: The adjustment amplitude weight W is calculated by using the following formula: wherein, is an absolute value of the model bias amount; is a preset weight influence factor.

6. The method of intelligent remote control of irrigation motorized valves according to claim 1, characterized in that, Based on the current target difference between the current soil moisture actual value and the target soil moisture set value, the last period target difference, the predicted execution lag time and the model deviation, the valve opening adjustment amount of the next control period is calculated, including: The valve opening adjustment amount is calculated by the following improved incremental proportional-integral control algorithm : wherein W is an adjustment amplitude weight; is a proportional gain; is an integral gain; is a current target difference value; is a target difference value of the last period; is a control gain attenuation coefficient.

7. The method of intelligent remote control of irrigation motorized valves according to claim 1, characterized in that, After the expected execution time of the control instruction is set according to the predicted execution lag time, the control instruction data packet is sent to the electric valve, including: Acquiring a current system timestamp of the control terminal ; Predict execution lag time Compared with the current system timestamp Add them together to get the expected execution time. ; This will include the target opening size and the expected execution time. The control command data packet is encapsulated and sent to the electric valve through the wireless communication module.

8. The method of intelligent remote control of irrigation motorized valves according to claim 1, characterized in that, In S4, the change rate threshold is set according to the predicted execution lag time, including: The rate of change threshold is calculated using the following linear function : wherein is the base rate of change threshold; is a hysteresis influence coefficient; is a prediction execution hysteresis time.

9. The method of claim 7, wherein the method further comprises: The control instruction data packet is sent to the electric valve through the NB-IoT module.

10. An intelligent remote control system for irrigation electrically operated valves, characterized in that, The irrigation electric valve intelligent remote control method comprises a memory and a processor, and computer program instructions are stored in the memory. When the computer program instructions are executed by the processor, the irrigation electric valve intelligent remote control method of any one of claims 1-9 is realized.

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