Multi-point hydraulic cooperative control method, device, medium and product for irrigating open channel

Abstract

The invention discloses a multi-point hydraulic cooperative control method, device, medium and product for irrigating an open channel, and relates to the field of water conservancy automation and control engineering.The method comprises the steps that a state space model is built based on a Saint-Venant equation set; calculating a current actually-measured water level tracking error, determining a predicted water level tracking error based on the state space model, and constructing a predicted sliding mode surface function based on the actually-measured water level tracking error, the predicted water level tracking error and historical error information; designing a disturbance observer based on the state space model to determine a disturbance estimation signal; based on the state space model, the prediction sliding mode surface function and the disturbance estimation signal, deducing a control quantity combining a boundary layer method and a feed-forward compensation mechanism, so as to cooperatively adjust the opening degree of each gate according to the control quantity; according to the method, stable, high-precision and high-robustness multi-point hydraulic cooperative control is carried out on the irrigation open channel multi-channel pool series system under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of water conservancy automation and control engineering, and in particular to a method, device, equipment and medium for multi-point hydraulic coordinated control of irrigation open channels. Background Technology

[0002] Open channels are the main infrastructure for water conveyance and distribution in my country's irrigation areas, and their operation and control level directly affects water resource utilization efficiency and agricultural irrigation security. At present, many large irrigation areas in my country still rely on manual scheduling or simple local automatic control, which has disadvantages such as fixed control mode, slow response and weak anti-interference ability, which can easily lead to problems such as uncontrolled upstream water levels, fluctuations in downstream water intake, and waste of water resources.

[0003] To achieve efficient and precise allocation of water resources, multi-point hydraulic coordinated control of open channel systems is of significant practical importance. This strategy aims to achieve differentiated and precise control of water levels at key cross-sections within the channel by coordinating the opening of multiple gates in a series-connected canal-pool system, while ensuring stable downstream outflow. However, existing control methods still have shortcomings: traditional multi-point hydraulic coordinated control methods struggle to achieve high-precision and robust coordinated control of multiple hydraulic points in open channels under complex operating conditions. Specifically, the feedback mechanism of traditional control methods exhibits slow dynamic response to sudden disturbances, long recovery times, and often fails to adequately consider the online estimation and compensation of unknown downstream disturbances, resulting in insufficient system robustness. Summary of the Invention

[0004] The purpose of this application is to provide a method, equipment, medium, and product for multi-point hydraulic coordinated control of irrigation open channels, which can solve the problem that "traditional multi-point hydraulic coordinated control methods are difficult to achieve high-precision and high-robust coordinated control of multi-point hydraulics in open channels under complex working conditions".

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a multi-point hydraulic coordinated control method for irrigation open channels, including: A state-space model is constructed based on the Saint-Venant equations; Calculate the current measured water level tracking error, determine the predicted water level tracking error based on the state space model, and construct a predicted sliding surface function based on the measured water level tracking error, the predicted water level tracking error, and historical error information. Based on the state-space model, a disturbance observer is designed to determine the disturbance estimation signal; Based on the state-space model, the predicted sliding surface function, and the disturbance estimation signal, a control quantity combining the boundary layer method and the feedforward compensation mechanism is derived to coordinately adjust the opening of each gate according to the control quantity.

[0006] In one embodiment, the step of constructing a state-space model based on the Saint-Venant equations specifically includes: The Saint-Venant equations are linearized by performing a first-order Taylor expansion at the steady-state point. By discretizing the linearized Saint-Venant equations in both space and time, a linear time-invariant state-space model is obtained.

[0007] In one embodiment, the step of designing a disturbance observer based on the state-space model to determine the disturbance estimation signal is calculated using the following formula: In the formula, for k State observations at time; for k The state vector at any given time; The state observation value at the previous moment; This is the state vector from the previous time step; This is the control input vector from the previous time step; This is the estimated disturbance value from the previous moment; for k The estimated value of the disturbance at time; This is the gain matrix of the state observer; Here is the gain matrix of the perturbation estimator; For the system matrix; To control the input matrix; The perturbation input matrix; This is the output matrix.

[0008] In one embodiment, the step of deriving the control quantity combining the boundary layer method and the feedforward compensation mechanism based on the state-space model, the predicted sliding mode surface function, and the disturbance estimation signal specifically includes: The ideal control quantity is solved by combining the state-space model, the predicted sliding surface function, and the disturbance estimation signal. The sliding mode control quantity is obtained based on the ideal control quantity using the boundary layer method. A feedforward compensation term based on flow error is introduced, and the control quantity is determined based on the feedforward compensation term and the sliding mode control quantity.

[0009] In one embodiment, the step of solving for the ideal control quantity by combining the state-space model, the predicted sliding mode surface function, and the disturbance estimation signal is calculated using the following formula: In the formula, For ideal control quantity; For the system matrix; To control the input matrix; The perturbation input matrix; This is the output matrix; for k The estimated value of the disturbance at time; for k The state vector at any given time; for k Real-time water level tracking error, among which, for k The actual water level at that moment. for k The target water level value at that moment; for k Target water level at time +1; Let be the sliding surface parameter matrix, where As the baseline parameter, This is the adjustment coefficient; This is the integral gain matrix; for k The integral term of the water level error at any given time.

[0010] In one embodiment, the step of obtaining the sliding mode control quantity based on the ideal control quantity using the boundary layer method is calculated using the following formula: In the formula, For sliding mode control; For ideal control quantity; To switch the gain matrix; This is the boundary layer thickness parameter matrix; It is the hyperbolic tangent function; for k The value of the sliding surface function at time t.

[0011] In one embodiment, the step of introducing a feedforward compensation term based on flow error and determining the control quantity based on the feedforward compensation term and the sliding mode control quantity is calculated using the following formula: In the formula, ,in, This is the feedforward gain matrix; for k Downstream flow rate measured at all times; for k The target traffic volume set at any time; To control the quantity.

[0012] Secondly, this application also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0013] Thirdly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0014] Fourthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a multi-point hydraulic collaborative control method, equipment, medium, and product for irrigation open channels. It proposes a multi-point hydraulic collaborative control method based on Improved Sliding Mode Predictive Control (ISMPC). A linear discrete state-space model of the coupled hydraulic relationship between multiple channels and pools is established based on the Saint-Venant equations, providing a foundation for subsequent controller design. A disturbance observer based on the error rate of change is designed for online real-time estimation and compensation of unknown disturbances caused by downstream random water distribution. An integrated integral term and parameter adaptive predictive sliding surface are designed, and the control quantity is further derived. This control quantity combines the boundary layer method and feedforward compensation, effectively suppressing the chattering phenomenon of traditional sliding mode control while fully leveraging the strong robustness and fast response advantages of sliding mode control, thus improving the tracking performance of target flow changes. Therefore, stable, high-precision, and robust multi-point hydraulic collaborative control of a multi-channel and pool series system in an irrigation open channel is achieved under complex and variable operating conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a multi-point hydraulic coordinated control method for an irrigation open channel according to an embodiment of this application; Figure 2 This is a schematic diagram of a multi-channel pool series system, which is an embodiment of the multi-point hydraulic collaborative control method for irrigation open channels in this application. Figure 3This is a schematic block diagram of a control system for a multi-point hydraulic coordinated control method for an irrigation canal according to an embodiment of this application; Figure 4 This is a water level curve diagram of each monitoring point in a multi-point hydraulic coordinated control method for an irrigation open channel according to an embodiment of this application; Figure 5 This is a diagram showing the downstream flow curves of each canal and pool in a multi-point hydraulic coordinated control method for an irrigation open canal according to an embodiment of this application. Figure 6 This is a diagram showing the downstream flow curves of each canal and pool under the target flow step scenario of a multi-point hydraulic collaborative control method for an irrigation open canal according to an embodiment of this application. Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] See Figure 1 This application provides a multi-point hydraulic coordinated control method for irrigation open channels, comprising the following steps: S100: Constructing a state-space model based on the Saint-Venant equations; S200: Calculate the current measured water level tracking error, determine the predicted water level tracking error based on the state space model, and construct the predicted sliding surface function based on the measured water level tracking error, the predicted water level tracking error, and historical error information. S300: Design of a disturbance observer based on a state-space model to determine the disturbance estimation signal; S400: Based on the state-space model, predicted sliding surface function and disturbance estimation signal, a control quantity combining boundary layer method and feedforward compensation mechanism is derived to coordinately adjust the opening of each gate according to the control quantity.

[0021] In S100, the steps for constructing a state-space model based on the Saint-Venant equations specifically include the following steps: S110: Perform a first-order Taylor expansion of the Saint-Venant equations at the steady-state point to obtain the linearized Saint-Venant equations. S120: Spatially and temporally discretize the linearized Saint-Venant equations to obtain a linear time-invariant state-space model.

[0022] See S110. Figure 2 The multi-channel pool series system includes 3 channels and pools and 4 control gates, and a total of 6 monitoring points are set up at key upstream and downstream sections of each channel to obtain water level and flow information at different locations in the channel.

[0023] For a prismatic open channel, without lateral inflow, the Saint-Venant equation is as follows: (1) In the formula, This refers to the cross-sectional area of ​​the water passage. The flow rate through the cross section; The cross-sectional water depth; The process coordinates are taken from the head of the canal. For time; It is the acceleration due to gravity; The slope of the canal bottom; This refers to the friction slope.

[0024] The nonlinear nature of the Saint-Venant equations makes them difficult to use directly in controller design. Therefore, this application employs a method of linearization near the steady-state operating point, transforming them into a linear model suitable for sliding mode predictive control. Assuming the channel is initially in a steady state, with flow rate and water depth as follows: and Equation (1) at the steady state point Performing a first-order Taylor expansion at the given point yields the linearized Saint-Venant equations, as shown below: (2) In the formula, The steady-state water flow area; This represents the steady-state water surface width. The steady-state flow velocity; This represents the steady-state friction slope. ; .

[0025] In S120, to facilitate numerical calculation and controller design, the linearized model is discretized spatially and temporally. The channel is divided into multiple segments of length... A tiny segment, with a time step of 1. The partial differential terms are discretized using an implicit Preissmann four-point difference scheme, transforming the entire channel system into a high-dimensional linear time-invariant state-space model, as shown in the following equation: (3) In the formula, fork The time-state vector is composed of the flow deviation at each cross section. and water depth deviation Together they constitute, which can be represented as ; To control the input vector; It is a bounded perturbation vector; This is the system output vector; , , and These are the system matrix, control input matrix, disturbance input matrix, and output matrix, respectively. Further, the system matrix... Represents state variables and The coupling relationship between them; control input matrix This represents the effect of the control input on the flow rate and water depth at adjacent cross sections; the disturbance input matrix. Used to describe the impact of disturbances on the flow and water level status of the monitoring section; output matrix It is a selection matrix, where each monitoring point has a corresponding position of 1 and the rest are 0.

[0026] In an open channel system with multiple channels and pools connected in series, the upstream and downstream channels and pools are dynamically coupled through hydraulic connections at their joints. The input flow of the downstream channel and pool is directly constrained by the output flow of the upstream channel and pool and the water distribution disturbance. The coupling relationship is shown in the following equation: (4) In the formula, For the first The outflow rate at the upstream end of the canal pool; For the first The inflow rate at the downstream head of the canal pool; For water diversion disturbance; Indicates the first The total number of discrete cross-sections of each canal pool; and Representing the first Water level deviation and flow rate deviation at the final cross-section of each canal / pond; For the first The control input variation of each canal pool; coupling matrix and Determined by the system's geometric and hydraulic parameters. Among them, The elements constitute the global system matrix. The non-zero, non-diagonal blocks connecting upstream and downstream state variables, and correspond The corresponding elements define the cross-channel pooling action of the control input.

[0027] It should be noted that the state-space model in formula (3) is a global and centralized model expression. The first row of equations in formula (4) defines the flow continuity, which directly determines how the flow state variables at the end of the upstream channel pool are related to the flow state variables at the beginning of the downstream channel pool when constructing the global system matrix A. The specific elements of the coupling matrix and input matrix in the second row of equations are the global system matrix. and control input matrix The source of non-zero, off-diagonal block elements used to connect the state variables of upstream and downstream channels.

[0028] This application, based on an established multi-channel-pool coupled centralized state-space model, designs an improved sliding mode predictive control to achieve multi-point coordinated hydraulic control of a series open channel system. The core objective is to implement differentiated precision management at different monitoring point sections. Since the upstream gate is the starting point of the entire control chain, instability there will propagate downstream, causing the entire system to become uncontrollable. Therefore, it is necessary to maintain a stable upstream gate. Figure 2 The water levels at monitoring points C1-1, C2-1, and C3-1 upstream of the canal are close to the set values. The downstream gate, as the entrance to the downstream canal, is affected by the combined effects of the water flow dynamics within the canal, unknown disturbances, and fluctuations in the upstream flow, making it difficult to control precisely. Therefore, the water levels at monitoring points C1-2, C2-2, and C3-2 downstream of the canal are allowed to fluctuate within the set range.

[0029] In S200, at time k Calculate the tracking error of the measured water level: (5) In the formula, for k The actual water level at any given moment can be directly measured using sensors and other equipment. for k The target water level at any given time.

[0030] To anticipate system dynamics, the next time step is predicted based on a state-space model. k +1 water level value, predicted output The calculation is as follows: Calculate the predicted water level tracking error: In the formula, for k Time Prediction k Water level error at time +1; for k Prediction of water level at time +1; for k The water level setting value at time +1 is the target water level value.

[0031] Design predictive sliding surface function , Indicates in k Time prediction k The sliding surface value at time +1 is shown in the following formula: (6) In the formula, for k Time Prediction k Water level error at time +1; for k The tracking error of the measured water level at any given time; for k The integral term of the water level error at any given time; Let be the sliding surface parameter matrix, where As the baseline parameter, This is the adjustment coefficient; Let be the integral gain matrix. The control objective is therefore equivalent to solving for the control quantity. , making .

[0032] This predictive sliding surface function integrates information from actual measurement errors, prediction errors, and historical errors, aiming to attract the system state trajectory onto the sliding surface. This enables rapid convergence and strong robustness.

[0033] In S300, to enhance the open channel control system's ability to suppress unknown disturbances, a disturbance observer based on the error rate of change is designed for online estimation and compensation of disturbances. Define the disturbance estimate. Its physical meaning lies in the fact that, to counteract the expected impact of a disturbance on the system output, the controller proactively and in advance applies a control action of equal magnitude but opposite direction. The disturbance observer equation is: (7) In the formula, for k State observations at time; for k The state vector at any given time; The state observation value at the previous moment; This is the state vector from the previous time step; This is the control input vector from the previous time step; This is the estimated disturbance value from the previous moment; for k The estimated value of the disturbance at time; This is the gain matrix of the state observer; Here is the gain matrix of the perturbation estimator; For the system matrix; To control the input matrix; The perturbation input matrix; This is the output matrix.

[0034] In S400, based on the state-space model, predicted sliding mode surface function, and disturbance estimation signal, the steps for deriving the control quantity combining the boundary layer method and feedforward compensation mechanism include the following: S410: Solve for the ideal control quantity by combining the state-space model, the predicted sliding surface function and the disturbance estimation signal; S420: The boundary layer method is used to obtain the sliding mode control quantity based on the ideal control quantity; S430: Introduce a feedforward compensation term based on flow error, and determine the control quantity based on the feedforward compensation term and sliding mode control quantity.

[0035] In S410, the predicted sliding surface function is: The basic principle of sliding mode control is to design a control law that forces the system's state trajectory to be attracted and maintained on this sliding surface. Once the system state is on the sliding surface, it satisfies... The system will exhibit the desired dynamic performance, such as fast convergence and strong robustness. This is discussed in the derivation of the ideal control variable. When the sliding surface is equal to zero, the design objective is to require that: .

[0036] Therefore, it can be concluded that: From equation (5), we can see that: Therefore: According to equation (3), k Always k The output prediction at time +1 is as follows: (8) In summary, Substituting into the above equation, we get: (9) Furthermore, it can be derived from equation (9) that: (10) In the formula, For ideal control quantity; For the system matrix; To control the input matrix; The perturbation input matrix; This is the output matrix; for k The estimated value of the disturbance at time; for k The state vector at any given time; for k Real-time water level tracking error, among which, for k The actual water level at that moment. for k The target water level value at that moment; for k Target water level at time +1; Let be the sliding surface parameter matrix, where As the baseline parameter, This is the adjustment coefficient; This is the integral gain matrix; for k The integral term of the water level error at any given time.

[0037] In the S420, to enhance robustness and suppress chattering, a boundary layer method is used. The ideal switching control is replaced by a hyperbolic tangent function, and a continuous robust term constructed using the boundary layer method is introduced. The resulting control variables are shown below: (11) In the formula, For sliding mode control; For ideal control quantity; To switch the gain matrix; This is the boundary layer thickness parameter matrix; It is the hyperbolic tangent function; for k The value of the sliding surface function at time t.

[0038] In S430, to further improve the tracking performance of target flow changes, a feedforward compensation term based on flow error is introduced, resulting in the control quantity as shown in the following formula: (12) In the formula, ,in, This is the feedforward gain matrix; for k Downstream flow rate measured at all times; for k The target traffic volume set at any time; To control the quantity.

[0039] In summary, the control quantity calculated by equation (12) is converted into the gate opening change by the actuator, and the precise control of the water flow in the channel is achieved by changing the flow area.

[0040] See Figure 3 In a specific example, the control system of this application includes a target water level and target flow module, an ISMPC controller, a disturbance observer, and a flow feedforward module.

[0041] Specifically, the target water level and target flow rate module outputs as follows: and The inputs to the ISMPC controller include , as well as Compared with the actual water level reported back The difference after subtraction, and... The output is The input to the interference observer is the actual water level value. and the final control quantity obtained by solving The output is a real-time estimate of the unknown disturbance. The input to the flow feedforward module is the target flow rate. and downstream actual flow The output is the feedforward compensation control quantity. .

[0042] To perform the stability proof, a Lyapunov function is designed as shown in the following equation: (13) The changes between adjacent time points are shown in the following formula: (14) Combining equations (3), (5), (6), and (12), and considering the disturbance estimation error... We can obtain: (15) Substituting the above equation into equation (14), we get: (16) Since the feedforward compensation term is a bounded input, the disturbance estimation error... Bounded, and the hyperbolic tangent function is bounded. Therefore, there exists a positive number. ,when Sometimes, According to Lyapunov stability theory, It is ultimately uniformly bounded, because Includes tracking error ,therefore It is also ultimately uniformly bounded. (Combined with the system matrix) From the Shure stability and the bounded input bounded output property, we can know that and All signals in the closed-loop system are eventually uniformly bounded. Therefore, the entire closed-loop system is eventually uniformly bounded and stable.

[0043] In a specific example, the geometric and hydraulic parameters of the three-channel-pond-connected open irrigation canal system studied in this invention, as well as the test condition parameters during the simulation process, are shown in Tables 1 and 2, respectively: Table 1 Basic Parameters of Multi-Channel Pool

[0044] Table 2 Test parameters for multi-channel pool

[0045] To fully verify the performance of the ISMPC algorithm designed in this application, performance tests were conducted in the following two test scenarios. For the upstream monitoring point, the water level should be as close as possible to the target water level; for the downstream monitoring point, the water level is allowed to fluctuate within a safe range, with a fluctuation range of ±0.2m. (1) Scenario 1: The target water level in front of each monitoring section and the downstream flow rate of the canal and pool are set to constant values. See Figure 4 By employing the ISMPC control strategy to coordinate the control of multiple upstream and downstream sluice gates in a series canal-pool system, the water level and flow rate at multiple monitoring points can be effectively adjusted according to preset targets. At six different monitoring point sections, the average absolute errors of the water levels at the upstream monitoring points C1-1, C2-1, and C3-1 were 0.0317m, 0.0181m, and 0.0107m, respectively, all close to the target values, indicating good control accuracy. The average absolute errors of the water levels at the downstream monitoring point sections C1-2, C2-2, and C3-2 were 0.1011m, 0.1002m, and 0.0934m, respectively. Although the fluctuation range was larger than that at the upstream monitoring point sections, they all remained within the set fluctuation range.

[0046] See Figure 5 As shown in the figure, under ISMPC control, the downstream flow rates of each channel and pool are close to the set target values. Among them, the average absolute error of the downstream flow rate of channel and pool 1 is 0.0054 m³ / s. 3 / s, the average absolute error of the downstream flow rate of channel pool 2 is 0.0359m³ / s. 3 / s, the average absolute error of the downstream flow rate of channel pool 3 is 0.0911m³ / s. 3 / s, further demonstrating that the designed control strategy can effectively ensure the stability of downstream flow in each canal and pool.

[0047] (2) Scenario 2: Six hours after the simulation started, the downstream target flow rate increased by 10%. See Figure 6 As shown in the figure, after a step change in the target flow rate, the outflow from the channel under ISMPC control undergoes a rapid adjustment and then stabilizes again near the set value. The specific flow rate error statistics for each channel are as follows: the average absolute error of the downstream flow rate of channel 1 is 0.2417 m³ / s. 3 / s, the average absolute error of the downstream flow rate of channel pool 2 is 0.2454m³ / s. 3 / s, the average absolute error of the downstream flow rate of channel pool 3 is 0.3261m³ / s. 3 / s. In summary, this demonstrates that the designed ISMPC strategy not only exhibits high accuracy and strong anti-interference capabilities under steady-state conditions, but also ensures rapid system recovery and stable operation when faced with sudden adjustments to the target flow.

[0048] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection.

[0049] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0050] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0051] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0052] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0053] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0054] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0055] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0056] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for multi-point hydraulic coordinated control of an irrigation open canal, characterized in that, include: A state-space model is constructed based on the Saint-Venant equations; Calculate the current measured water level tracking error, determine the predicted water level tracking error based on the state space model, and construct a predicted sliding surface function based on the measured water level tracking error, the predicted water level tracking error, and historical error information. Based on the state-space model, a disturbance observer is designed to determine the disturbance estimation signal; Based on the state-space model, the predicted sliding surface function, and the disturbance estimation signal, a control quantity combining the boundary layer method and the feedforward compensation mechanism is derived to coordinately adjust the opening of each gate according to the control quantity.

2. The multi-point hydraulic coordinated control method for irrigation open channels according to claim 1, characterized in that, The steps for constructing a state-space model based on the Saint-Venant equations specifically include: The Saint-Venant equations are linearized by performing a first-order Taylor expansion at the steady-state point. By discretizing the linearized Saint-Venant equations in both space and time, a linear time-invariant state-space model is obtained.

3. The multi-point hydraulic coordinated control method for irrigation open channels according to claim 1, characterized in that, The step of designing a disturbance observer based on the state-space model to determine the disturbance estimation signal is calculated using the following formula: In the formula, for k State observations at time; for k The state vector at any given time; The state observation value at the previous moment; This is the state vector from the previous time step; This is the control input vector from the previous time step; This is the estimated disturbance value from the previous moment; for k The estimated disturbance value at time; Here is the gain matrix of the state observer; Here is the gain matrix of the perturbation estimator; For the system matrix; To control the input matrix; The perturbation input matrix; This is the output matrix.

4. The multi-point hydraulic coordinated control method for irrigation open channels according to claim 1, characterized in that, The step of deriving the control quantity combining the boundary layer method and feedforward compensation mechanism based on the state-space model, the predicted sliding mode surface function, and the disturbance estimation signal specifically includes: The ideal control quantity is solved by combining the state-space model, the predicted sliding surface function, and the disturbance estimation signal. The sliding mode control quantity is obtained based on the ideal control quantity using the boundary layer method. A feedforward compensation term based on flow error is introduced, and the control quantity is determined based on the feedforward compensation term and the sliding mode control quantity.

5. The multi-point hydraulic coordinated control method for irrigation open channels according to claim 4, characterized in that, The step of combining the state-space model, the predicted sliding surface function, and the disturbance estimation signal to solve for the ideal control quantity is calculated using the following formula: In the formula, For ideal control quantity; For the system matrix; To control the input matrix; The perturbation input matrix; This is the output matrix; for k The estimated disturbance value at time; for k The state vector at any given time; for k Real-time water level tracking error, among which, for k The actual water level at that moment. for k The target water level value at that moment; for k Target water level at time +1; Let be the sliding surface parameter matrix, where As the baseline parameter, This is the adjustment coefficient; This is the integral gain matrix; for k The integral term of the water level error at any given time.

6. The multi-point hydraulic coordinated control method for irrigation open channels according to claim 4, characterized in that, The step of obtaining the sliding mode control quantity based on the ideal control quantity using the boundary layer method is calculated using the following formula: In the formula, For sliding mode control; For ideal control quantity; To switch the gain matrix; This is the boundary layer thickness parameter matrix; It is the hyperbolic tangent function; for k The value of the sliding surface function at time t.

7. The multi-point hydraulic coordinated control method for irrigation open channels according to claim 4, characterized in that, The step of introducing a feedforward compensation term based on flow error, and determining the control quantity based on the feedforward compensation term and the sliding mode control quantity, is calculated using the following formula: In the formula, ,in, This is the feedforward gain matrix; for k Downstream flow rate measured at all times; for k The target traffic volume set at any time; To control the quantity.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the multi-point hydraulic coordinated control method for an irrigation canal according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the multi-point hydraulic coordinated control method for irrigation open channels as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the multi-point hydraulic coordinated control method for irrigation open channels as described in any one of claims 1-7.

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