Remote irrigation system based on Internet of Things

Abstract

The invention belongs to the technical field of agricultural irrigation, and particularly relates to a remote irrigation system based on the Internet of Things, which breaks through the limitation of traditional single parameter monitoring by simultaneously acquiring performance parameters (original inner diameter, pressure difference and flow velocity) and working parameters (pH value, chloride ion concentration and the like). The method comprises the following steps: generating residual life coefficients of a plurality of irrigation subunits through a preset residual life prediction model by acquiring the blockage degrees and corrosion degrees of the plurality of irrigation subunits, and then determining a speed threshold value of liquid flowing through the plurality of irrigation subunits; the irrigation control module controls the power of water pumps of the irrigation subunits according to the speed threshold value, water pipes in the irrigation subunits are prevented from being overloaded and broken, and the irrigation control module stops irrigation control over the irrigation subunits of which the residual life coefficients are equal to a preset life grade critical value; and the early warning module sends an early warning signal to the irrigation subunit to the terminal.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural irrigation, and particularly relates to a remote irrigation system based on the Internet of Things. Background Art

[0002] With the improvement of agricultural intelligence, in the existing remote irrigation system of a planting greenhouse, based on the collection of operation parameters in the greenhouse, through the logical control function of the system, according to the crop growth stage and soil nutrient status, the control terminal remotely controls the irrigation system in the greenhouse based on the detected operation parameters combined with the set irrigation program, including the control of the working parameters on multiple transport water pipes of the irrigation system, so as to realize the control of the irrigation volume and irrigation speed, and realize the remote control of the irrigation of the greenhouse. The irrigation unit of the irrigation system generally includes an irrigation main unit and a plurality of irrigation sub-units respectively connected to the main unit. The irrigation main unit is the main water pipe for transporting the irrigation system, and the irrigation sub-unit is a unit for irrigating a certain area of crops that is respectively communicated with the irrigation main unit, and it includes a water pipe and an irrigation head.

[0003] However, the water pipe in the irrigation unit of the irrigation system in the greenhouse is the core transport carrier of the irrigation liquid. Since the irrigation liquid may be tap water or contain chemical fertilizers, pesticides and complex water quality, and the water pipe is exposed to a complex liquid environment for a long time, it faces multiple corrosion and wear risks. In addition, acidic or salt components in chemical fertilizers (such as nitrates, chlorides) are likely to cause electrochemical corrosion of metal water pipes or chemical degradation of plastic water pipes; pesticide residues and microbial metabolites (such as hydrogen sulfide) further accelerate the corrosion of the pipe wall, forming local perforations or embrittlement. At the same time, problems such as the scaling of calcium and magnesium ions in hard water and the physical scouring of undissolved particulate matter will cause the inner diameter of the water pipe to shrink, the flow resistance to increase, significantly reduce the water conveyance efficiency, and force the water pump or the water pipe to operate under overload for a long time, resulting in energy waste and equipment loss.

[0004] Moreover, after the water pipe is scaled, it will affect the control of the terminal for the irrigation speed and irrigation volume. If there is scale inside the water pipe, then the equivalent diameter of the water pipe will become smaller, and too high a flow rate may cause the water pipe pressure to exceed the limit (such as the risk of pipe bursting), thereby affecting the control of the flow rate by the terminal.

[0005] The traditional technology relies on regular manual inspections or setting sensors at specific positions of the water pipe to detect a single working parameter of the water pipe, and cannot detect the corrosion rate and scaling degree of the water pipe in real time and comprehensively.

[0006] Therefore, a remote irrigation system based on the Internet of Things is needed to realize the real-time monitoring of the usage status of each irrigation sub-unit in the irrigation system, and adjust the irrigation parameters or replace the scrapped irrigation sub-unit according to the status of the irrigation sub-unit to ensure the operation effect of the irrigation sub-unit. Summary of the Invention

[0007] To solve the above problems existing in the prior art, the present invention provides a remote irrigation system based on the Internet of Things to solve the problem that the corrosion rate and blockage degree of water pipes cannot be detected in real time and comprehensively and corresponding adjustments cannot be made under the prior art.

[0008] The object of the present invention can be achieved by the following technical solutions: A remote irrigation system based on the Internet of Things, including a monitoring platform, and the monitoring platform includes:

[0009] A data acquisition module, used to collect the performance parameters and working parameters of multiple water pipes. The performance parameters include the original inner diameter of the water pipe, the pressure difference between the inlet and outlet of the water pipe, and the real-time flow rate of the liquid in the water pipe. The working parameters include the pH value of the irrigation solution, the chloride ion concentration, the irrigation time, and the irrigation volume;

[0010] A calculation module, used to evaluate the blockage degree of multiple irrigation sub-units respectively according to the performance parameters, calculate the corrosion degree of multiple irrigation sub-units respectively according to the working parameters, generate a remaining life coefficient of the irrigation sub-units based on the blockage degree and corrosion degree using a preset remaining life prediction model, and determine the speed thresholds of multiple irrigation sub-units respectively according to multiple remaining life coefficients using a preset remaining life coefficient - speed threshold piecewise function;

[0011] An irrigation control module, used to adjust and restrict the speed of the liquid flowing through multiple irrigation sub-units to be lower than the speed threshold according to the speed thresholds of multiple irrigation sub-units;

[0012] For the irrigation sub-units with the remaining life coefficient equal to the preset life grade critical value, the irrigation control module generates a stop irrigation control instruction to instruct the irrigation sub-units to stop irrigation.

[0013] Preferably, the calculation module calculates the blockage degree f in multiple sub-irrigation modules respectively based on the Hagen - Poiseuille equation, and the calculation formula is:

[0014]

[0015] Where, Δp is the pressure difference between the inlet and outlet of the water pipe of the irrigation sub-unit, μ is the liquid concentration irrigated by the irrigation sub-unit, L is the pipeline length of the irrigation sub-unit, Q is the liquid flow rate flowing through the irrigation sub-unit, and D is the original inner diameter of the water pipe;

[0016] The calculation formula for the calculation module to calculate the dynamic corrosion degree V(t) according to the working parameters is:

[0017]

[0018] Where, (cl- is the chloride ion concentration in the irrigation liquid, ph(t) is the pH value of the irrigation liquid at time t, T(t) is the temperature of the irrigation liquid at time t, and k is the corrosion coefficient of the water pipe material;

[0019] The calculation module calculates the corrosion degree of a single irrigation based on the single V(t) and the irrigation time, and then calculates the cumulative corrosion degree of the water pipe in combination with historical data.

[0020] Preferably, the data collected by the data acquisition module includes the height difference Δh between the inlet and the outlet of the water pipe of the irrigation subunit, Δh = h1 - h2, where h1 is the altitude of the water pipe outlet and h2 is the altitude of the water pipe inlet;

[0021] The calculation module calculates the blockage degree F of multiple irrigation subunits based on the Hagen - Poiseuille equation correction model, and the calculation formula includes:

[0022]

[0023] where ρ is the density of the irrigation liquid and g is the acceleration due to gravity.

[0024] Preferably, it further includes an early warning module, which is used to give an early warning to the irrigation subunit and transmit the early warning information to the terminal when the remaining life coefficient is lower than the preset life level threshold.

[0025] Preferably, the calculation module determines the speed thresholds of multiple irrigation subunits including:

[0026] The preset remaining life coefficients from low to high include f1, f2, f3, and f4;

[0027] Based on the remaining life coefficient, a piecewise function between the speed thresholds is used to determine the speed threshold of the irrigation subunit, and the piecewise function is:

[0028]

[0029] The early warning module gives an early warning to the irrigation subunit with f = f4.

[0030] Preferably, the confirmation of the remaining life prediction model includes:

[0031] Obtain training data, which includes the historical blockage degree and historical corrosion degree corresponding to multiple irrigation subunits;

[0032] Use the training data as input features and the predicted remaining life coefficient corresponding to the irrigation subunit as the output label to construct an artificial neural network model;

[0033] Using MSE as the model loss function, optimize the weights of the neural network through the backpropagation algorithm, and update the model parameters using the adaptive matrix estimation optimizer.

[0034] Preferably, the number of input layer nodes of the neural network model is 2, namely the blockage degree and the corrosion degree respectively, and the number of output layer nodes is 1, corresponding to the remaining life coefficient.

[0035] Preferably, the confirmation of the remaining life prediction model further includes preprocessing the training data:

[0036] Perform Z-score normalization on multiple sub-data in the training data;

[0037] Generate a two-dimensional feature vector through timestamp alignment.

[0038] Preferably, the data acquisition module includes a piezoresistive sensor, an ultrasonic flowmeter, and an electromagnetic flowmeter.

[0039] The beneficial effects of the present invention are as follows:

[0040] By simultaneously collecting performance parameters (original inner diameter, pressure difference, flow velocity) and working parameters (pH value, chloride ion concentration, etc.), the limitation of traditional single-parameter monitoring is broken through. Moreover, through multi-parameter real-time synchronous collection, compared with the discrete data acquisition method of traditional manual inspection, the monitoring frequency is improved, and problems existing in each irrigation subunit can be found in time. By obtaining the blockage degree and corrosion degree of multiple irrigation subunits, the remaining life coefficients of multiple irrigation subunits are generated through a preset remaining life prediction model, and then the liquid velocity threshold flowing through multiple irrigation subunits is determined. The irrigation control module controls the power of the water pumps of multiple irrigation subunits according to the velocity threshold to prevent the water pipes in the irrigation subunits from being overloaded and ruptured. And the irrigation control module stops the irrigation control for the irrigation subunits whose remaining life coefficients are equal to the preset life level critical value, and the warning module sends a warning signal to the terminal for this irrigation subunit. Regarding the multiple irrigation subunits in the irrigation system as a time-varying system, calculate the remaining life of the irrigation subunits through the calculation module, and realize the transformation from passive maintenance to predictive maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0042] Figure 1 It is the system block diagram of the present invention;

[0043] Figure 2 It is the calculation steps of the calculation module of the system of the present invention for the velocity threshold of the irrigation subunit. DETAILED DESCRIPTION OF THE INVENTION

[0044] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific implementation manners, structures, features and their effects of the present invention as follows.

[0045] Please refer to Figure 1 - Figure 2 , this embodiment provides a remote irrigation system based on the Internet of Things, including a data acquisition module, a calculation module and an irrigation control module that are electrically connected in sequence, where:

[0046] The data acquisition module is used to collect the performance parameters and working parameters of multiple water pipes. Among them, the performance parameters include the original inner diameter of the water pipe, the pressure difference between the inlet and outlet of the water pipe, and the flow rate of the liquid in the water pipe in real time. The working parameters include the pH value of the irrigation solution, the chloride ion concentration, the irrigation time and the irrigation volume. The data acquisition module includes:

[0047] High-precision pressure difference sensors are installed at the inlets and outlets of each section of the water pipe to measure the pressure difference between the inlet and outlet of the water pipe in real time. A piezoresistive sensor can be used as the pressure difference sensor;

[0048] The flow rate sensor is used to measure the flow rate of the liquid flowing through the irrigation subunit in real time. An ultrasonic flowmeter and an electromagnetic flowmeter can be used;

[0049] The pH value and calcium and magnesium ion concentration of the liquid flowing through the water pipe can be obtained from historical irrigation data.

[0050] The data acquisition module transmits the collected data to the calculation module for calculation through LoRa or NB-loT.

[0051] The calculation module is used to evaluate the blockage degree of multiple irrigation subunits respectively according to the performance parameters, calculate the corrosion degree of multiple irrigation subunits respectively according to the working parameters, generate the remaining life coefficient of the irrigation subunit using a preset remaining life prediction model based on the blockage degree and corrosion degree, and determine the speed threshold of multiple irrigation subunits respectively using a preset remaining life coefficient - speed threshold piecewise function. Among them:

[0052] First, the calculation module calculates the blockage degree f in multiple sub-irrigation modules respectively based on the Hagen - Poiseuille equation, including:

[0053] The Hagen - Poiseuille equation is a classical formula in fluid mechanics that describes the relationship between the flow rate and pressure of an incompressible Newtonian fluid in a circular pipe during steady laminar flow. Its original expression is:

[0054]

[0055] Among them, R is the radius of the circular pipe, Δp is the pressure difference between the inlet and outlet of the circular pipe, μ is the liquid concentration in the circular pipe, L is the pipe length, and Q is the liquid volume flow rate flowing through the circular pipe.

[0056] Since the water pipes of the irrigation sub-units in the irrigation system are generally designed as circular pipes, when the water pipes are blocked, the diameter of the water pipes will change, which will cause a significant change in the flow rate through the water pipes. The relationship between the flow rate of the liquid passing through and the pressure difference will be affected by the degree of blockage of the water pipes. Therefore, the Hagen-Poiseuille equation is used to calculate the degree of blockage of the water pipes, and the calculation process is as follows:

[0057] Since in engineering, the pipe parameters of water pipes are often expressed in terms of diameter rather than radius (such as standard pipe diameter specifications), the original diameter D of the water pipe is set, D = 2R. Combining the original Hagen-Poiseuille equation with the degree of blockage f of the water pipe, the flow rate after being affected by the degree of blockage in the water pipe is obtained:

[0058]

[0059] Therefore, the calculation formula for calculating the degree of blockage f of the water pipe of the irrigation sub-unit is:

[0060]

[0061] Among them, D is the original inner diameter of the water pipe, Δp is the pressure difference between the inlet and outlet of the water pipe of the irrigation sub-unit, μ is the liquid concentration of the liquid irrigated by the irrigation sub-unit, L is the pipe length of the irrigation sub-unit, and Q is the liquid volume flow rate flowing through the irrigation sub-unit. The calculation formula is integrated into the operation program of the calculation module to achieve the function.

[0062] Potassium chloride (KCl) is a commonly used potassium fertilizer in agricultural production. Some compound fertilizers and pesticides also contain chloride components. Therefore, in the nutrient solution used for irrigation, its components contain a large amount of chloride ions. The corrosion of metal pipes by chloride ions is a complex electrochemical process, and its corrosion mechanism mainly involves the physical and chemical properties of chloride ions (such as high activity, small radius, strong penetrability) and the interaction with the metal surface. In its irrigation system, the nutrient solution ratio for acid-loving crops generally shows acidity, and acidic solutions will also corrode the pipes for transporting liquids. Chloride ion corrosion and pH corrosion will gradually increase with the accumulation of irrigation times. After the water pipes are corroded, the surface of the pipe body will become brittle. Therefore, it is necessary to limit the flow rate and pressure of the irrigated liquid in the water pipes to prevent the water pipes from bursting due to too high liquid pressure in the water pipes.

[0063] Secondly, the calculation module calculates the dynamic corrosion degree V(t) using the functional relationship between the chloride ion concentration and solution pH of the irrigation solution and the dynamic corrosion degree:

[0064]

[0065] wherein, [cl - is the chloride ion concentration in the irrigation liquid, [cl - 0.8 reflects the destructive effect of chloride ions on the passive film. The exponent 0.8 comes from the regression analysis of seawater corrosion tests. ph(t) is the pH value of the irrigation liquid at time t, and (ph(t) - 7) / 2 is the pH index term, which characterizes the degree of hydrogen evolution corrosion in an acidic environment and is detected and calculated by the test environment. T(t) is the temperature of the irrigation liquid at time t, and k is the corrosion coefficient of the water pipe material, which is calibrated through tests. For example, the corrosion coefficient k of a carbon steel pipe is 0.5.

[0066] The calculation module calculates the corrosion degree of a single irrigation based on the single T(t) and the irrigation time, and then combines historical data, T(t), and the irrigation time to quantitatively calculate the cumulative corrosion degree of the water pipe to obtain the corrosion degree of the water pipe of the current irrigation subunit.

[0067] Again, the calculation module presets a trained remaining life prediction model for the irrigation subunit. By inputting the calculated blockage degree and corrosion degree into the remaining life prediction model, it outputs the remaining life coefficients corresponding to the specific blockage degree and corrosion degree of multiple irrigation subunits. The remaining life prediction model is determined by training an artificial neural network using historical blockage degrees and historical corrosion degrees.

[0068] Finally, the calculation module determines the speed thresholds of multiple irrigation subunits according to multiple remaining life coefficients using a preset remaining life coefficient - speed threshold piecewise function, including:

[0069] The preset remaining life coefficients from low to high include L1, L2, L3, and L4, which are used to characterize the attenuation of the coefficients of the irrigation subunit. Among them, L4 < L3 < L2 < L1 < 1;

[0070] Among them, based on the test environment, when calculating the design conditions of the irrigation subunit at different remaining lives, different water pipes are monitored, and the burst pressure of the water pipe at increasing flow rates is recorded, and the safety margin is recorded. And when it occurs at a specific remaining life and a specific flow rate, cracks appear in the water pipe, and multiple test values are recorded. The test values are fitted, and the corresponding relationship between L - V max is obtained through regression analysis. The relationship between the regression - fitted L - V max is set as a piecewise function. Here, the piecewise function characterizes the maximum flow rate that the water pipe of the irrigation subunit can withstand under specific remaining life conditions.

[0071] Set the L - V max function here as:

[0072] ​

[0073] Among them, when the remaining life coefficient is in the range of L≥L1, it indicates that the safety factor of the water pipe is within the safe area, and it is set as V1 = aV d , V d is the designed flow velocity of the water pipe, which can be obtained by querying the product parameter table.

[0074] When the remaining life coefficient is in the range of L2≤L<L1, it indicates that the remaining life of the water pipe is at an intermediate value, and it is necessary to reduce the maximum flow velocity that it can pass through. V2 = bV d ;

[0075] When the remaining life coefficient is in the range of L3≤L<L2, it indicates that the remaining life of the water pipe is in a severely declining period, and it is necessary to further reduce the maximum flow velocity at this time. V3 = cV d ;

[0076] Among them, the specific values of a, b, and c can be specifically obtained from the relationship function between L - V obtained by regression fitting max relationship function.

[0077] When the remaining life coefficient L = L4, it indicates that the water pipe of a specific irrigation subunit has reached the end of its life, and it needs to be replaced. The warning module warns the corresponding irrigation subunit. At the same time, for the irrigation subunit whose remaining life coefficient is equal to the preset life - level critical value, the irrigation control module generates a stop - irrigation control instruction to instruct the irrigation subunit to stop irrigation. The warning module transmits the number and location information of the specific irrigation subunit to the terminal, the terminal generates a maintenance work order, and sends this maintenance work order to the maintenance personnel.

[0078] The irrigation control module presets the relationship between the flow velocity of the liquid in the water pipe and the pump power in the irrigation subunit, and adjusts the flow velocity of the liquid flowing through multiple irrigation subunits to be lower than the corresponding velocity thresholds according to the velocity thresholds of multiple irrigation subunits, so as to adjust the maximum power value of the corresponding pump, thereby preventing the irrigation subunit from being overloaded.

[0079] This solution breaks through the limitations of traditional single-parameter monitoring by simultaneously collecting performance parameters (original inner diameter, pressure difference, flow velocity) and operating parameters (pH value, chloride ion concentration, etc.). Moreover, through multi-parameter real-time synchronous collection, compared with the discrete data acquisition method of traditional manual inspection tours, the monitoring frequency is increased, and problems existing in each irrigation subunit can be discovered in a timely manner. By obtaining the blockage degree and corrosion degree of multiple irrigation subunits, the remaining life coefficients of multiple irrigation subunits are generated through a preset remaining life prediction model, and then the liquid velocity threshold flowing through multiple irrigation subunits is determined. The irrigation control module controls the power of the water pumps of multiple irrigation subunits according to the velocity threshold to prevent the water pipes in the irrigation subunits from being overloaded and ruptured. Moreover, the irrigation control module stops the irrigation control for the irrigation subunits whose remaining life coefficients are equal to the preset life level critical value, and the warning module sends a warning signal to the terminal for this irrigation subunit.

[0080] In mountainous and hilly areas, the irrigation systems generally have pipelines with undulating heights. Therefore, when the terrain is uneven in a greenhouse or for the installation of irrigation subunits, when the outlet of the water pipe of the irrigation subunit is higher than the inlet, the liquid in the water pipe needs to overcome gravity to do work. The actually measured pressure difference between the outlet and the inlet of the water pipe includes the static pressure difference. If the gravitational potential energy required to overcome the weight of the liquid is ignored, it will cause the calculated blockage degree by the calculation module to be on the high side, which will further affect the subsequent calculation of the life level of the corresponding water pipe, resulting in misjudgment of the system and triggering unnecessary maintenance warnings.

[0081] When the outlet of the water pipe is lower than the inlet, the gravitational potential energy of the liquid in the water pipe will impact the water pipe wall, thereby increasing the risk of the water pipe bursting.

[0082] Therefore, in one implementation, the data collected by the data acquisition module includes the height difference Δh between the inlet and the outlet of the water pipe of the irrigation subunit, Δh = h1 - h2, where h1 is the altitude of the water pipe outlet and h2 is the altitude of the water pipe inlet.

[0083] The calculation module respectively corrects and calculates the blockage degree f of multiple irrigation subunits based on the Hagen-Poiseuille equation correction model. The correction calculation equation is:

[0084]

[0085] Among them, ρ is the density of the irrigation liquid, which is obtained by obtaining the formula of the liquid for irrigation from the terminal, and g is the acceleration due to gravity at the location of the irrigation system.

[0086] The elevation difference between the inlet and outlet of the water pipe collected by the data acquisition module should correspond to the installation requirements of the water pipe or the terrain difference of the irrigation system in the greenhouse, so as to adapt to the irrigation system in complex terrains such as mountains or slopes. It can accurately distinguish the frictional pressure loss and the static pressure difference, avoid misjudgment of the blockage degree of the water pipe by the calculation module, and improve the accuracy of the prediction by the calculation module based on the preset prediction model.

[0087] The core transport carrier of the irrigation liquid in the water pipe of the irrigation unit in the greenhouse. Since the irrigation liquid may be tap water or contain fertilizers, pesticides and complex water quality, and the water pipe is exposed to a complex liquid environment for a long time, it faces multiple corrosion and wear risks. In addition, acidic or salt components in fertilizers (such as nitrates, chlorides) are likely to cause electrochemical corrosion of metal water pipes or chemical degradation of plastic water pipes; pesticide residues and microbial metabolites (such as hydrogen sulfide) further accelerate the corrosion of the pipe wall, forming local perforations or embrittlement.

[0088] In one implementation, the confirmation of the remaining life coefficient prediction model includes:

[0089] S1: Acquisition and preprocessing of training data

[0090] S11: The input features are the historical corrosion degree and historical blockage degree of multiple irrigation subunits;

[0091] The output label is the remaining life coefficient of multiple irrigation subunits;

[0092] S12: Data preprocessing

[0093] Use a filter to filter out abnormal data in the training data and perform Z-score normalization processing. Align through timestamps to generate a two-dimensional feature vector;

[0094] S2: Design of the neural network architecture

[0095] The number of nodes in the input layer of the neural network is 2, which are the blockage degree and the corrosion degree respectively. The number of nodes in the output layer is 1, corresponding to the remaining life coefficient F ∈ [0, 1];

[0096] S3: Optimize the model, including:

[0097] Use the mean square error function as the loss function of the model, and its formula is:

[0098]

[0099] The predicted life level of the i-th sample point by the model, The actual remaining life coefficient of the i-th sample of the historical data, and N is the number of training samples;

[0100] Optimize the neural network weights through the backpropagation algorithm and update the model parameters using an adaptive matrix estimation optimizer.

[0101] A remaining life prediction model for a preset irrigation subunit trains an artificial neural network based on historical clogging degree and corrosion degree to generate a prediction of the remaining life of the irrigation subunit, and improves the prediction of the remaining life of the irrigation subunit through online training of the model.

[0102] Convert the trained neural network model to the TensorFlow Lite format and integrate it inside the computing module.

[0103] The above are only preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A remote irrigation system based on the Internet of Things, characterized in that: Including a monitoring platform, the monitoring platform includes: A data acquisition module for collecting the performance parameters and working parameters of multiple water pipes. The performance parameters include the original inner diameter of the water pipe, the pressure difference between the inlet and outlet of the water pipe, and the flow rate of the liquid in the water pipe in real time. The working parameters include the pH value of the irrigation solution, the chloride ion concentration, the irrigation time, and the irrigation volume; A calculation module for respectively evaluating the blockage degree of multiple irrigation sub-units according to the performance parameters, calculating the corrosion degree of multiple irrigation sub-units according to the working parameters, generating a remaining life coefficient of the irrigation sub-units using a preset remaining life prediction model based on the blockage degree and corrosion degree, and respectively determining the speed thresholds of multiple irrigation sub-units using a preset remaining life coefficient - speed threshold piecewise function according to multiple remaining life coefficients; An irrigation control module for adjusting and restricting the speed of the liquid flowing through multiple irrigation sub-units to be lower than the speed threshold according to the speed thresholds of multiple irrigation sub-units; For the irrigation sub-unit whose remaining life coefficient is equal to the preset life grade critical value, the irrigation control module generates a stop irrigation control instruction to instruct the irrigation sub-unit to stop irrigation.

2. The remote irrigation system based on the Internet of Things according to claim 1, wherein: The calculation module calculates the blockage degree f in multiple sub-irrigation modules respectively based on the Hagen - Poiseuille equation, and the calculation formula is: Where, Δp is the pressure difference between the inlet and outlet of the water pipe of the irrigation sub-unit, μ is the concentration of the liquid irrigated by the irrigation sub-unit, L is the pipe length of the irrigation sub-unit, Q is the liquid flow rate flowing through the irrigation sub-unit, and D is the original inner diameter of the water pipe; The calculation formula for the calculation module to calculate the dynamic corrosion degree V(t) according to the working parameters is: where, [cl - is the chloride ion concentration in the irrigation liquid, ph(t) is the pH value of the irrigation liquid at time t, T(t) is the temperature of the irrigation liquid at time t, and k is the corrosion coefficient of the water pipe material; The calculation module calculates the corrosion degree of a single irrigation according to the single V(t) and the irrigation time, and calculates the cumulative corrosion degree of the water pipe in combination with historical data.

3. The remote irrigation system based on the Internet of Things according to claim 2, characterized in that: The data collected by the data acquisition module includes the height difference Δh between the inlet and outlet of the water pipe of the irrigation sub-unit, Δh = h1 - h2, where h1 is the altitude of the water pipe outlet and h2 is the altitude of the water pipe inlet; The calculation module calculates the blockage degree F of multiple irrigation sub-units based on the Hagen - Poiseuille equation correction model, and the calculation formula includes: Where, ρ is the density of the irrigation liquid and g is the acceleration due to gravity.

4. The remote irrigation system based on the Internet of Things according to claim 1, characterized in that: It further includes an early warning module for warning the irrigation sub-unit and transmitting the warning information to the terminal when the remaining life coefficient is lower than the preset life grade threshold.

5. The remote irrigation system based on the Internet of Things according to claim 4, characterized in that: The calculation module determines the speed thresholds of multiple irrigation sub-units including: The preset remaining life coefficients from low to high include L1, L2, L3, and L4; Based on the remaining life coefficient, a piecewise function between the speed thresholds is used to determine the speed threshold of the irrigation sub-unit, and the piecewise function is: The early warning module warns the irrigation sub-unit with L = L4.

6. The remote irrigation system based on the Internet of Things according to claim 1, characterized in that: The confirmation of the remaining life prediction model includes: Obtaining training data, the training data including the historical blockage degree and historical corrosion degree corresponding to multiple irrigation sub-units; Construct an artificial neural network model with the training data as input features and the predicted remaining life coefficient corresponding to the irrigation subunit as the output label; Use MSE as the model loss function, optimize the neural network weights through the backpropagation algorithm, and update the model parameters using the adaptive matrix estimation optimizer.

7. The remote irrigation system based on the Internet of Things according to claim 6, characterized in that: The number of nodes in the input layer of the neural network model is 2, namely the blockage degree and the corrosion degree respectively, and the number of nodes in the output layer is 1, corresponding to the remaining life coefficient.

8. The remote irrigation system based on the Internet of Things according to claim 7, characterized in that: The confirmation of the remaining life prediction model also includes preprocessing the training data: Perform Z-score normalization on multiple sub-data in the training data; Generate a two-dimensional feature vector through timestamp alignment.

9. The remote irrigation system based on the Internet of Things according to claim 4, wherein: The data acquisition module includes a piezoresistive sensor, an ultrasonic flowmeter, and an electromagnetic flowmeter.

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