Multi-channel-pool cooperative regulation and control method, system, equipment and medium

Abstract

The invention relates to a multi-channel-pool cooperative regulation and control method, system and device and a medium, and belongs to the technical field of hydraulic engineering automation control. The method comprises the steps that the overshoot flow of each channel pool under the preset water diversion working condition is calculated; calculating the maximum variable amplitude of the water level in front of a sluice of each channel pool under the preset water diversion working condition by combining an objective function of a feed-forward control optimization model with the overshoot flow; under the constraint of at least one constraint condition, through the feed-forward control optimization model, with the minimization of the maximum variable amplitude of the water level in front of the gate as a target, dynamic storage capacity compensation iterative calculation is carried out, and a target overshoot flow proportionality coefficient of each channel pool is obtained; and according to the target overshoot flow proportionality coefficient of each channel pool, a regulation and control instruction of each check gate is generated, and the multi-stage check gates are regulated and controlled according to the regulation and control instructions. Through the cooperative regulation and control instruction set of the multi-stage check gates, cooperative control of the multi-stage check gates is realized, the hydraulic coupling effect between channels and pools is quantified, and the real-time control efficiency under complex working conditions is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of water conservancy projects, and particularly to a multi-channel pool collaborative regulation method, system, device and medium. Background Technique

[0002] Long-distance water conveyance projects are the core carriers of cross-basin water resource allocation, and their operation stability is directly related to water supply safety and project benefits.

[0003] In the regulation of water conveyance channels, the traditional feedforward control method designs a single gate action strategy through the secondary compensation of storage by static hydraulic relations, which cannot respond in time, resulting in lag or overshoot of the gate action, causing water level overlimit. Moreover, for complex channel systems with multiple regulating gates, the coupling effect between gates is not quantified by the single gate compensation strategy, exacerbating the downstream water level oscillation. Summary of the Invention

[0004] To solve the above problems, the present application provides a multi-channel pool collaborative regulation method, system, device and medium.

[0005] The first aspect of the embodiment of the present application provides a multi-channel pool collaborative regulation method, including:

[0006] Calculating the overshoot flow of each channel pool under the preset water diversion condition at the flow regulation moment;

[0007] Calculating the maximum variation range of the water level in front of the gate of each channel pool under the preset water diversion condition by combining the objective function of the feedforward control optimization model with the overshoot flow; wherein, the objective function is constructed by at least one constraint condition;

[0008] Under the constraint of at least one constraint condition, through the feedforward control optimization model, with the minimization of the maximum variation range of the water level in front of the gate as the goal, dynamic storage compensation iterative calculation is performed to obtain the target overshoot flow proportional coefficient of each channel pool;

[0009] Generating a regulation instruction for each regulating gate according to the target overshoot flow proportional coefficient of each channel pool, and generating a collaborative regulation instruction set for multi-level regulating gates according to the regulation instructions of all regulating gates; wherein, each channel pool corresponds to one regulating gate;

[0010] Collaboratively regulating multi-level regulating gates according to the instruction set.

[0011] Optionally, it further includes:

[0012] Generating a first constraint condition, a second constraint condition and a third constraint condition respectively according to the hourly water level variation range, the 24-hour cumulative variation range and the instantaneous water level deviation;

[0013] Construct an objective function for calculating the maximum amplitude change of the water level in front of the sluice for each canal pool based on multiple constraints, the water level value in front of the sluice for each canal pool under the preset water diversion condition, and the target regulation water level for each canal pool.

[0014] Optionally, it further includes:

[0015] Obtain the water level value in front of the sluice for each canal pool under the preset water diversion condition;

[0016] Based on the overshoot flow rate of each canal pool and the water level value in front of the sluice, obtain the target regulation water level for each canal pool.

[0017] Optionally, the generating of the regulation instruction for each check gate according to the target overshoot flow rate proportional coefficient of each canal pool specifically includes:

[0018] According to the target overshoot flow rate proportional coefficient of each canal pool, calculate the regulation instruction for each check gate in combination with the gate flow equation.

[0019] Optionally, the calculating of the regulation instruction for each check gate according to the target overshoot flow rate proportional coefficient of each canal pool and in combination with the gate flow equation specifically includes:

[0020] Based on the target overshoot flow rate proportional coefficient of each canal pool and the overshoot flow rate of each canal pool, obtain the flow rate through the check gate of each canal pool;

[0021] Based on the flow rate through the gate and in combination with the gate flow equation, calculate the opening degree of the gate adjusted for each canal pool from the initial stable state to the final stable state;

[0022] Generate the regulation instruction for each check gate according to the opening degree of the gate of each canal pool.

[0023] Optionally, the calculating of the overshoot flow rate of each canal pool under the preset water diversion condition at the flow regulation moment specifically includes:

[0024] At the flow regulation moment, calculate the storage difference according to the initial condition and the stable condition of each canal pool after the first compensation of the storage volume under the preset water diversion condition;

[0025] Based on the initial condition of each canal pool and in combination with the dynamic wave equation, calculate the feedforward time;

[0026] Conduct the second compensation calculation of the storage volume according to the storage difference and the feedforward time to obtain the overshoot flow rate of each canal pool.

[0027] Optionally, the performing of the dynamic storage volume compensation iterative calculation with the goal of minimizing the maximum amplitude change of the water level in front of the sluice through the feedforward control optimization model to obtain the target overshoot flow rate proportional coefficient for each canal pool specifically includes:

[0028] Through the feedforward control optimization model, with the goal of minimizing the maximum variation range of the water level in front of the sluice, the particle swarm optimization algorithm is used to screen and calculate the feasible solutions that meet the constraint conditions, and iterative calculations are performed through a preset number of times until the maximum number of iterations is reached, to obtain the target overshoot flow rate proportionality coefficient of each canal pond.

[0029] The second aspect of the embodiments of the present application provides a multi-canal pond collaborative regulation system, including:

[0030] An overshoot flow rate calculation module, configured to calculate the overshoot flow rate of each canal pond under a preset water diversion condition at the flow regulation moment;

[0031] A water level calculation module, configured to calculate the maximum variation range of the water level in front of the sluice of each canal pond under the preset water diversion condition by combining the objective function of the feedforward control optimization model with the overshoot flow rate; wherein, the objective function is constructed through at least one constraint condition;

[0032] Dynamic compensation calculation, configured to perform dynamic storage capacity compensation iterative calculation through the feedforward control optimization model with the goal of minimizing the maximum variation range of the water level in front of the sluice under the constraint of at least one constraint condition, to obtain the target overshoot flow rate proportionality coefficient of each canal pond;

[0033] A regulation instruction generation module, configured to generate a regulation instruction for each check gate according to the target overshoot flow rate proportionality coefficient of each canal pond, and generate a collaborative regulation instruction set for multi-level check gates according to the regulation instructions of all check gates; wherein, each canal pond corresponds to one check gate;

[0034] A regulation module, configured to collaboratively regulate multi-level check gates according to the instruction set.

[0035] The third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, wherein,

[0036] The memory is used to store a program;

[0037] The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the steps in a multi-canal pond collaborative regulation method according to any of the above solutions.

[0038] The fourth aspect of the embodiments of the present application is a computer-readable storage medium, used to store computer-readable programs or instructions, and when the programs or instructions are executed by a processor, they can implement the steps in a multi-canal pond collaborative regulation method according to any of the above solutions.

[0039] Applying the technical solution provided by the embodiments of the present application, through the dynamic storage compensation iterative calculation by the feedforward control optimization model, the traditional compensation coefficient is optimized, effectively suppressing the water level fluctuation in front of the sluice under the diversion disturbance. Through the collaborative control instruction set of multiple check gates, the collaborative control of multiple check gates is realized, the hydraulic coupling effect between canal pools is quantified, and the real-time control efficiency under complex working conditions is improved. Description of the Drawings

[0040] In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings required to be used in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0041] Figure 1 It is a flowchart of the steps of a multi-canal pool collaborative control method provided in the embodiments of the present invention;

[0042] Figure 2 It is a flowchart of the steps for generating control instructions provided in the embodiments of the present invention;

[0043] Figure 3 It is a flowchart of the steps for calculating overshoot flow provided in the embodiments of the present invention;

[0044] Figure 4 It is a schematic diagram of the dynamic storage compensation calculation principle provided in the embodiments of the present invention;

[0045] Figure 5 It is the calculation process of the feedforward control optimization model provided in the embodiments of the present invention;

[0046] Figure 6 It is a schematic diagram of the example canal system simulation provided in the embodiments of the present invention;

[0047] Figure 7 It is a schematic diagram of the water diversion situation at each water diversion port under different working conditions provided in the embodiments of the present invention;

[0048] Figure 8 It is a schematic diagram of the change process of the water level in front of the downstream sluice of each canal pool under each working condition provided in the embodiments of the present invention;

[0049] Figure 9 It is a structural block diagram of a multi-canal pool collaborative control system provided in the embodiments of the present invention;

[0050] Figure 10 It is a hardware structural block diagram of an electronic device provided in the embodiments of the present invention. Detailed Embodiments

[0051] To make the above objects, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0052] Referring to Figure 1 as shown, a step flow chart of a multi-channel pool collaborative regulation method is shown. This method can be applied in the field of water conservancy project automation control technology, such as Figure 1 as shown, and specifically may include the following steps:

[0053] Step S101: Calculate the overshoot flow of each channel pool under the preset water diversion condition at the flow regulation moment;

[0054] In one embodiment, at the flow regulation moment, calculate the storage difference according to the initial condition and the stable condition of each channel pool after the first compensation of the storage volume under the preset water diversion condition; according to the initial condition of each channel pool and the dynamic wave equation, calculate the feedforward time; perform the second compensation calculation of the storage volume according to the storage difference and the feedforward time to obtain the overshoot flow of each channel pool. In one embodiment, the water conveyance canal system may include: multiple water diversion ports; wherein, the preset water diversion condition may be that each water diversion port is assigned a corresponding water diversion flow rate change amount, and the preset water diversion condition may assign corresponding flow values to each water diversion port according to actual needs. A water diversion condition where each water diversion port has a water diversion flow rate change amount. For example, as shown in Figure 4 Condition 1, Condition 2, Condition 3, and Condition 4 shown.

[0055] Step S102: Calculate the maximum change range of the water level in front of the gate of each channel pool under the preset water diversion condition by combining the objective function of the feedforward control optimization model with the overshoot flow; wherein, the objective function is constructed by at least one constraint condition;

[0056] In one embodiment, an objective function for calculating the maximum change range of the water level in front of the gate of each channel pool is constructed according to multiple constraint conditions, the water level value in front of the gate of each channel pool under the preset water diversion condition, and the target regulation water level of each channel pool.

[0057] Step S103: Under the constraint of at least one constraint condition, through the feedforward control optimization model, with the goal of minimizing the maximum change range of the water level in front of the gate, perform dynamic storage volume compensation iterative calculation to obtain the target overshoot flow ratio coefficient of each channel pool;

[0058] In one embodiment, under the constraints of multi-dimensional constraints such as hourly water level amplitude change, cumulative amplitude change, and instantaneous deviation, through the feedforward control optimization model, with the goal of minimizing the maximum amplitude change of the water level in front of the sluice, the particle swarm optimization algorithm is used to screen and calculate the feasible solutions that meet the constraints, and through a preset number of iterations until the maximum number of iterations is reached, the target overshoot flow rate proportionality coefficient of each canal pond is obtained.

[0059] Step S104: Generate a regulation instruction for each check gate according to the target overshoot flow rate proportionality coefficient of each canal pond, and generate a collaborative regulation instruction set for the multi-level check gates according to the regulation instructions of all the check gates; where each canal pond corresponds to one check gate.

[0060] In one embodiment, the particle swarm optimization (PSO) algorithm is used to solve the optimal dynamic compensation coefficient combination θ1, θ2,..., θ N , and based on the GPU parallel computing architecture, the performance of multiple groups of parameters is evaluated synchronously, and finally a gate opening instruction set that meets the constraints is generated to achieve the collaborative regulation of the multi-level check gates.

[0061] Step S105: Collaboratively regulate the multi-level check gates according to the instruction set.

[0062] In one embodiment, the instruction set is assigned to the regulation devices corresponding to each check gate, and the regulation devices control the check gates according to the gate opening regulation instructions.

[0063] In one embodiment, the regulation device can have functions such as gate opening and closing, automatic information collection, local / remote automatic control, and can communicate with the software of the irrigation area data center through a wireless network, etc.; and can be configured with a gate position sensor, a back weir trough water level gauge, an intelligent controller, and a limit protection device. This regulation device can control the check gate according to the gate opening regulation instruction to achieve the real-time collaborative regulation of the multi-level check gates. Among them, the instruction set can include: gate opening regulation instruction, regulation time instruction, regulation mode instruction, safety inspection instruction, emergency stop instruction, and data recording instruction;

[0064] The gate opening degree represents the target opening degree of each gate and is used to control the flow rate through the gate.

[0065] The regulation time instruction represents the time arrangement of each regulation step, including the start time and the end time.

[0066] The regulation mode instruction represents the regulation mode of the gate, such as manual, automatic, or remote control.

[0067] The safety inspection instruction represents the safety inspection requirements before and after the gate operation to ensure that the operation will not cause safety problems.

[0068] The emergency stop instruction indicates an instruction to immediately stop the gate operation in case of an emergency, such as when the water level exceeds the safety limit;

[0069] The data recording instruction indicates recording key data during the gate operation process, such as the opening degree, flow rate, water level, etc., for subsequent analysis and model calibration;

[0070] The communication protocol indicates the communication protocol between the regulation instruction set and the gate control terminal to ensure the correct transmission and execution of the instructions.

[0071] It should be noted that the gate opening degree regulation instruction and the regulation time instruction can be obtained according to the multi-channel pool collaborative regulation method in the above embodiments. Specifically, the flow rate through the check gate of each channel pool is obtained according to the target overshoot flow rate proportional coefficient of each channel pool, and the gate opening degree adjusted by each channel pool from the initial stable state to the final stable state, as well as the start time and end time, are obtained based on the flow rate through the gate. Other instructions can be directly given by the control center according to actual needs and sent to the regulation device through the communication protocol.

[0072] In one embodiment, the steps of constructing the objective function may specifically include the following:

[0073] Generate the first constraint condition, the second constraint condition, and the third constraint condition respectively according to the hourly water level variation amplitude, the 24-hour cumulative variation amplitude, and the instantaneous water level deviation;

[0074] Construct an objective function for calculating the maximum variation amplitude of the water level in front of the gate of each channel pool according to multiple constraint conditions, the water level value in front of the gate of each channel pool under the preset water diversion condition, and the target regulation water level of each channel pool.

[0075] In one embodiment, determine the channel pool u with the largest water level fluctuation through the storage volume secondary compensation simulation. With the goal of minimizing the maximum variation amplitude of the water level in front of the gate of channel pool u, construct a feedforward control optimization model with a penalty function, and set multi-level constraint conditions with reference to the engineering operation specifications to optimize the objective function of the model: P(θ1, θ2,..., θ N , φ) is expressed as follows:

[0076]

[0077] In the formula: Z u，t (θ1, θ2,..., θ N ) is the water level value in front of the gate of channel pool u at time t under the condition of the proportional combination of θ1, θ2,..., θ N ; Z u,0 is the target regulation water level of channel pool u, φ is the penalty factor; N is the total number of constraint conditions; g l,i (q i ) is the nth constraint condition of channel pool i, g l,i (qi ) ≤ 0。

[0078] Among them, the constraint condition expressions are as follows:

[0079] The water level amplitude per hour ≤ 0.15 m: |Z i，(t,1) (θ1, θ2,..., θ N ) - Z i，t | ≤ Z h , Zh = 0.15;

[0080] The cumulative amplitude in 24 hours ≤ 0.3 m: |Z i，(t,24) (θ1, θ2,..., θ N ) - Z i，t | ≤ Z d , Zd = 0.3;

[0081] The instantaneous water level deviation ≤ 0.1 m: |Z i，t - Z i，0 | ≤ Z b , Zb = 0.1;

[0082] In the formula: Z i，t (θ1, θ2,..., θ N ) is the water level amplitude of the i-th channel pool at time t under the condition of the proportional combination of θ1, θ2,..., θ N ; Z i，(t,1) (θ1, θ2,..., θ N ) is the water level amplitude at the moment 1 hour after time t under the condition of the proportional combination of θ1, θ2,..., θ N ; Z i，(t,24) (θ1, θ2,..., θ N ) and the water level amplitude at the moment 24 hours after time t under the condition of the proportional combination of θ1, θ2,..., θ N .

[0083] In an embodiment, the hydrodynamic coupling effect modeling and simulation specifically include:

[0084] Construct a one-dimensional hydrodynamic model based on the Saint-Venant equations and discretize it using the Preissmann implicit difference scheme. By simultaneously solving the continuity equation and the momentum equation and performing iterative calculations, simulate the hydraulic coupling effect between multiple channel pools, predict the dynamic impact of the gate action on the upstream and downstream water levels, and provide simulation support for coordinated regulation:

[0085] Among them, the formulas of the Saint-Venant equations are as follows:

[0086]

[0087]

[0088] In the formula: B is the surface width of the cross-section of flowing water; Z is the water level; t is the time; Q is the flow rate; x is the longitudinal distance of the channel along the main flow direction; q is the lateral inflow; α is the momentum correction coefficient; A is the cross-sectional area of flowing water; g is the acceleration due to gravity; S f is the friction slope; n c is the Manning roughness coefficient of the canal or pond; R is the hydraulic radius.

[0089] The discrete formulas of the system of equations are as follows:

[0090]

[0091] In the formula, the coefficients C i , D i , E i , G i , F i , Φ i can all be calculated from the hydraulic parameters and the hydraulic elements at the nth moment.

[0092] In one embodiment, C i and F i : These coefficients are usually related to the continuity equation, involving the cross-sectional area A of flowing water, the wetted perimeter P, the acceleration due to gravity g, and the time step Δt and the spatial step Δx;

[0093] D i and Φ i : These coefficients are related to the boundary conditions and source terms, and can include lateral inflow, pumping, or natural evaporation, etc.;

[0094] E i and G i : These coefficients are related to the momentum equation, involving the flow rate Q, the cross-sectional area A of flowing water, the wetted perimeter P, the acceleration due to gravity g, and the friction slope S f ;

[0095] Through the discrete formulas of the system of equations, the simulation data of the water level Z and the flow rate Q can be quickly obtained.

[0096] Based on the Saint-Venant system of equations, a one-dimensional hydrodynamic model is constructed to simulate the hydraulic coupling effect between multiple canals or ponds, predict the dynamic influence of the gate operation on the upstream and downstream water levels, simulate the process of secondary compensation of the storage volume, and provide data support for the coordinated regulation of multiple canals or ponds.

[0097] In the multi-canal or pond system, each canal or pond is coupled with each other through the flow of water. Through the above model, the transmission and exchange process of water flow between different canals or ponds can be simulated, and the changes in the water levels and flow rates of each canal or pond can be analyzed, so as to reveal the hydraulic coupling effect between multiple canals or ponds.

[0098] For this system of equations, through the linearized control equations of various types of buildings and boundary conditions, for example, the building can be a sluice gate, and the linearized control equation of the sluice gate can be the linearized processing equation of the sluice gate's flow rate, the opening height of the sluice gate, and the water level difference between the upstream and downstream. The boundary conditions can be the specified flow rate through the sluice gate, water level, or the opening height of the sluice gate. Set the corresponding recursive formula and directly solve it through the chasing method to obtain the water level and flow rate simulation data of each cross-section.

[0099] In one embodiment, the storage secondary compensation simulation can be achieved through hydrodynamic coupling effect modeling and simulation.

[0100] Taking the minimization of the maximum water level variation before the gate of the key canal pools as the core goal, combining multi-dimensional constraint conditions such as the hourly water level variation, cumulative variation, and instantaneous deviation, construct a feedforward control optimization model with a penalty function to quantify the influence of the hydraulic coupling effect between multiple canal pools on the regulation strategy.

[0101] In one embodiment, the steps of obtaining the target regulation water level can specifically include the following:

[0102] Obtain the water level value before the gate of each canal pool under the preset water diversion condition;

[0103] According to the overshoot flow rate of each canal pool and the water level value before the gate, obtain the target regulation water level of each canal pool.

[0104] In one embodiment, the steps of generating the regulation command for each sluice according to the target overshoot flow rate proportional coefficient of each canal pool can specifically include the following steps:

[0105] According to the target overshoot flow rate proportional coefficient of each canal pool, calculate the regulation command for each sluice in combination with the gate flow equation.

[0106] In one embodiment, as shown in Figure 2 shown, the flow chart of the steps of generating the regulation command is shown. As shown in Figure 2 shown, it can specifically include the following steps:

[0107] Step S201: According to the target overshoot flow rate proportional coefficient of each canal pool and the overshoot flow rate of each canal pool, obtain the flow rate through the sluice of the sluice of each canal pool;

[0108] Step S202: According to the flow rate through the sluice and in combination with the gate flow equation, calculate the opening of the gate adjusted by each canal pool from the initial stable state to the final stable state;

[0109] Step S203: Generate the regulation command for each sluice according to the opening of the gate of each canal pool.

[0110] In one embodiment, as shown in Figure 3As shown, a flowchart of steps for calculating overshoot flow rate is presented, as follows Figure 3 shown, and specifically may include the following steps:

[0111] Step S301: Calculate the storage difference at the flow regulation moment based on the initial condition and the stable condition of each canal pond after the first compensation of the storage volume under the preset water distribution condition;

[0112] Step S302: Calculate the feedforward time according to the initial condition of each canal pond in combination with the dynamic wave equation;

[0113] Step S303: Perform a second compensation calculation of the storage volume based on the storage difference and the feedforward time to obtain the overshoot flow rate of each canal pond.

[0114] In one embodiment, the calculation using the dynamic wave equation is as follows:

[0115]

[0116] where, ΔT i,m is the feedforward control time calculated for canal pond i according to the water demand m situation, L is the length of the canal pond, v0 is the initial average flow velocity of the canal pond, and c0 is the initial average wave velocity of the water wave.

[0117] In one embodiment, the calculation formula for performing a second compensation calculation of the storage volume based on the storage difference and the feedforward time to obtain the overshoot flow rate of each canal pond includes:

[0118] Δq d，i，m = Δq i，m - ΔQ i，m ',

[0119] where,

[0120]

[0121] t d，i，m = t dm ,

[0122] N is the total number of canal pond numbers; m is the water demand number; i is the canal pond number, i ∈ [1, N]; ΔQ i,m ’ is the first regulation flow rate of canal pond i in response to water demand m; ΔV i,m is the change amount of the storage volume adjusted from the initial stable state to the final stable state in canal pond i caused by water demand m; ΔT i,m is the feedforward control time calculated for canal pond i according to the water demand m situation;

[0123] Δq d,i,m is the second regulation flow rate of canal pond i based on the initial stable state and the final stable state; Δq i,mis the change in water demand of canal pond i; t dm is the water diversion plan time; t 0,i,m and t d,i,m are respectively the first flow regulation time and the second flow regulation time for canal pond i to respond to water demand m.

[0124] In one embodiment, as Figure 4 shown, according to the change in the flow rate Δq at the water diversion outlet downstream of each canal pond i,m , the change in the storage volume ΔV of each canal pond i,m and the feed-forward control time ΔT i,m , by introducing a dynamic compensation coefficient θ i , construct the calculation formula for the adjusted value ΔQ' of the gate control flow rate i,m ' and balance the inlet and outlet flow rates of the canal pond based on the callback flow rate Δq d,i,m .

[0125] Based on the traditional storage volume secondary compensation method, introduce a dynamic compensation coefficient θ i , and suppress the water level fluctuation by dynamically adjusting the value of θ i to solve the problem of water level overshoot under the static compensation logic.

[0126] In the regulation of the water conveyance canal system, feed-forward control anticipates the impact of flow rate disturbances on the water level and adjusts the gate opening in advance to suppress the water level fluctuation. However, the traditional feed-forward control method: the storage volume secondary compensation only designs the gate action strategy based on static hydraulic relationships, and there are significant limitations: when the water conveyance channel is affected by boundary conditions (such as sudden changes in the flow rate at the water diversion outlet), the feed-forward operation strategy under the storage volume secondary compensation method often cannot respond in time to the dynamic processes of water flow inertia and resistance changes, resulting in lag or over-adjustment of the gate action and causing the water level to exceed the limit; for a complex canal system with multiple regulating gates, the coupling effect between the gates is not quantified, and the single-gate compensation strategy may exacerbate the downstream water level oscillation.

[0127] The present invention aims at the problems such as the water level overshoot in front of the gate and the insufficient multi-canal pond collaborative regulation ability caused by the static compensation logic in the canal pond system formed by multiple regulating gates (each gate is provided with a water diversion outlet on the upstream side, and each gate is equipped with an independent regulation device). By optimizing the dynamic storage volume compensation coefficient, it suppresses the water level fluctuation in front of the gate under the water diversion disturbance; constructs a multi-canal pond collaborative regulation mechanism to quantify the hydraulic coupling effect between the canal ponds; generates a collaborative regulation instruction set for multiple regulating gates based on the GPU parallel architecture to improve the real-time control efficiency under complex working conditions.

[0128] In one embodiment, the steps of obtaining the target overshoot flow rate proportionality coefficient of each canal pond may specifically include the following:

[0129] Through the feedforward control optimization model, with the goal of minimizing the maximum variation range of the water level before the sluice, the particle swarm optimization algorithm is used to screen and calculate the feasible solutions that meet the constraint conditions, and through a preset number of iterations until the maximum number of iterations is reached, the target overshoot flow rate proportionality coefficient of each canal pond is obtained.

[0130] In one embodiment, the particle swarm optimization (PSO) algorithm is used to solve the optimal dynamic compensation coefficient combination θ1, θ2, …, θ N The solution process specifically includes:

[0131] Define the particle position vector θ vec =[θ1, θ2, …, θ N (θ i is the dynamic compensation coefficient of canal pond i), randomly initialize the positions and velocities v j (0) corresponding to the particle swarm size M, and set the initial states of the individual optimal position and the global optimal position G best ;

[0132] Based on the GPU parallel architecture, synchronously call the one-dimensional hydrodynamic model in the above embodiment, that is, the simulation model constructed by the hydrodynamic coupling effect, to perform hydrodynamic simulation on each particle and calculate the comprehensive fitness value F of the objective function and the constraint violation penalty term j , screen the feasible solutions that meet the constraint conditions in the above embodiment, and update the individual optimal and the global optimal G best ;

[0133] Use the linearly decreasing inertia weight w(k) to update the particle velocity v j (k + 1), combine the learning factors c1, c2 and random numbers r1, r2 to drive the position update, and perform reflection boundary processing on the out-of-bounds θ i ;

[0134] Loop to execute fitness evaluation, optimal solution update and particle state iteration until the maximum number of iterations K max or the improvement amplitude of the global optimal solution is lower than the threshold ε for multiple consecutive generations; finally output the optimal dynamic compensation coefficient combination

[0135] Generate a multi-level regulating sluice collaborative regulation instruction set by back-calculating through the calculation formula of the one-dimensional hydrodynamic model and the sluice flow formula, and synchronously send it to the sluice control terminal to realize the real-time collaborative regulation of the multi-level regulating sluice.

[0136] In one embodiment, the formula for updating the particle velocity is as follows:

[0137]

[0138] Among them, v j (k + 1) represents the velocity of the j-th particle at the (k + 1)-th iteration; w(k) represents the inertia weight, which is used to control the influence of the particle's previous velocity and may change during the iteration process to balance global search and local search; v j (k) represents the velocity of the j-th particle at the k-th iteration; c1 and c2 represent learning factors, usually constants, which are used to adjust the tendency of the particle to move towards the individual optimal position and the global optimal position; r1 and r2 represent random numbers between 0 and 1, which are used to increase the randomness of the algorithm and help jump out of the local optimum; represents the optimal position found by the j-th particle so far; represents the position of the j-th particle at the k-th iteration; G best represents the global optimal position found by all particles so far; represents the position of the j-th particle at the k-th iteration.

[0139] The formula for driving the position update is as follows:

[0140]

[0141] Among them, θ vecj (k + 1): represents the position (or decision variable value) of the j-th particle at the (k + 1)-th iteration; θ vecj (k): represents the position (or decision variable value) of the j-th particle at the k-th iteration; v j (k + 1): represents the velocity of the j-th particle at the (k + 1)-th iteration.

[0142] The formula for the gate flow is as follows:

[0143]

[0144] Among them, Q is the flow rate through the check gate; M is the comprehensive flow coefficient; Δe is the adjusted gate opening from the initial stable state to the final stable state; B g is the width of the gate opening; Z1 is the water level in front of the gate; Z2 is the water level behind the gate.

[0145] By dynamically adjusting the compensation ratio of the storage volume of each channel pool, the problem of overshoot in compensation caused by the traditional secondary compensation method of storage volume is solved, so that the amplitude of water level change strictly meets the requirements of the engineering safety code; the parallel optimization algorithm architecture is adopted to achieve the rapid global optimization of the regulation parameters and meet the real-time regulation requirements of the complex water conveyance system.

[0146] In the application of the particle swarm optimization algorithm, the selection of the population size and the number of iterations has a significant impact on the algorithm performance. Generally, increasing the population size and extending the iteration cycle can improve the accuracy of the solution, but it will also increase the consumption of computing resources synchronously. During actual implementation, dynamic parameter optimization can be carried out according to the hardware performance, time constraints and accuracy requirements.

[0147] In one embodiment, the calculation process of the feedforward control optimization model is as Figure 5 shown. The process of secondary storage compensation includes: collecting the basic information of the channel or the channel pond; calculating the initial storage volume V0 according to the initial working condition combined with the steady flow equation, and calculating the final water surface line and the storage volume V ij under the steady working condition. Based on the initial working condition, the feedforward time τ i is calculated. According to the initial storage volume V0 and the storage volume V ij under the steady working condition, the storage volume difference ΔV i =V ij -V0 is calculated. According to the feedforward time τ i and the storage volume difference, through the secondary storage compensation calculation, the overshoot flow rate Δq i of each channel pond is obtained.

[0148] The processing process of the feedforward control optimization model includes:

[0149] 1. Initial parameter setting: Set relevant parameters.

[0150] 2. The number of iterations T = 1: Start the first iteration.

[0151] 3. Calculate the feedforward overshoot: According to the target water level, calculate the opening of the gate corresponding to the initial working condition before the start of water transfer.

[0152] 4. Flow balance: At the water diversion moment, the gate is adjusted secondly to ensure the balance of the inflow and outflow.

[0153] 5. Construct the objective function: Carry out constraint condition limitation and construct the objective function:

[0154]

[0155] Among them,

[0156]

[0157] Among them: Z u，t (θ1, θ2,..., θ N ) is the water level value in front of the gate of channel pond u at time t under the working condition of the proportional combination of θ1, θ2,..., θ N ; Z u,0 is the target regulation water level of channel pond u. Φ is the penalty factor; N is the total number of constraint conditions; g l,i (θi ) is the nth constraint condition for the canal pond i, g l,i (θ i ) ≤ 0;

[0158] According to the engineering specifications, the constraint condition expressions are as follows:

[0159] Water level amplitude per hour ≤ 0.15m: |Z i，(t,1) (θ1, θ2,..., θ N ) - Z i，t | ≤ Z h , Zh = 0.15;

[0160] Cumulative amplitude in 24 hours ≤ 0.3m: |Z i，(t,24) (θ1, θ2,..., θ N ) - Z i，t | ≤ Z d , Zd = 0.3;

[0161] Instantaneous water level deviation ≤ 0.1m: |Z i，t - Z i，0 | ≤ Z b , Zb = 0.1;

[0162] Where, Z i，t (θ1, θ2,..., θ N ) is the water level amplitude of the ith canal pond at time t under the working condition of θ1, θ2,..., θ N proportional combination; Z i，(t,1) (θ1, θ2,..., θ N ) is the water level amplitude at the moment 1 hour after time t under the working condition of θ1, θ2,..., θ N proportional combination; Z i，(t,24) (θ1, θ2,..., θ N ) and the water level amplitude at the moment 24 hours after time t under the working condition of θ1, θ2,..., θ N proportional combination.

[0163] 6. Conduct real-time simulation to obtain the numerical value of the objective function (maximum amplitude in front of the sluice).

[0164] 7. Screen the feasible solutions that meet the constraint conditions.

[0165] 8. Update the particle state.

[0166] 9. Iteratively determine whether the maximum number of iterations is reached: if not, then T = T + 1 and continue the iteration; if so, end the iteration.

[0167] 10. Output the optimal overshoot flow rate proportionality coefficient of each canal pond and generate a collaborative regulation instruction set for the multi-level check gates.

[0168] In one embodiment, taking the five canal pools between Taocha Canal Head of the Middle Route Project of South-to-North Water Diversion and the Shierlihe Check Gate as the research object, the present invention will be further described. The regional schematic diagram is shown in Figure 6 , and the specific parameters of the simulation channel are shown in Table 1. The real-time simulation roughness coefficient is taken as 0.015, the calculation time step is taken as 1 min, and the boundary conditions of each canal pool take the flow rate through the upstream gate of each canal pool as the upstream boundary, and the water level in front of the downstream gate of each canal pool as the downstream boundary. Under the initial state, the flow rate at the canal head is 230 m 3 ·s -1 , and the initial water intake flow rate at each water diversion outlet is 10 m 3 ·s -1 , and the initial water level in front of each check gate is the design water level of each check gate. During the simulation calculation, the water level in front of the Shierlihe Check Gate remains unchanged.

[0169] Table 1

[0170]

[0171]

[0172] According to different water demand plans, this embodiment simulates four water diversion conditions for real-time simulation calculation. The variation of the water diversion flow rate at each water diversion outlet under different conditions is as Figure 7 shown. The water diversion flow rate of the water diversion outlets not participating in the water diversion condition remains unchanged at the initial value. The simulation period is set to 48 h. The starting change time of water intake at each water diversion outlet is 9 h, and the ending change time of water intake is 11 h. The change process is a linear change.

[0173] To determine the target regulation canal pool under different conditions, it is necessary to conduct real-time simulation calculation of the storage secondary compensation feedforward control for the research canal system. The variation curve of the real-time water level in front of the gate with time is as Figure 8 shown. By statistics, the maximum water level deviation value ΔZ i (i is the canal pool number) in front of the downstream of each canal pool can be obtained, as shown in Table 2, which shows the maximum water level deviation in front of the downstream of each canal pool based on the storage secondary compensation calculation. The canal pool where the maximum value of the maximum water level deviation under each condition is located is taken as the target regulation canal pool under the corresponding condition, and is used to construct the objective function of the optimization model. The solution of the optimization model adopts the parallel PSO algorithm, and at the same time, the standard PSO algorithm is used for performance comparison research. The initial setting parameters of the two algorithms are the same: the population size is set to 50, the maximum number of iterations is 50 times, the minimum inertia weight w min takes the value of 0.4, the maximum inertia weight wmax takes the value of 0.9, the learning factors c1 and c2 are both set to 1.494, and the random numbers r1 and r2 are uniformly distributed random numbers obeying [0,1].

[0174] Under the same working conditions, the secondary compensation operation mode of storage volume and the operation mode of the optimization model under different solution algorithms are applied respectively. The variation curves of the water levels in front of the gates corresponding to each canal pond with time under different working conditions are as Figure 8 shown, and the operation time tables of different operation modes are shown in Table 3. Considering that the parallel PSO algorithm only adopts a parallelization strategy to solve multiple particles simultaneously without improving the theoretical basis of the algorithm, the control performance gaps of the two optimization models in terms of the fluctuation range of water levels are not significant. Therefore, the key point of the comparative study on the control performance of the two optimization models only considers the computational time factor.

[0175] Table 2

[0176]

[0177]

[0178] Table 3

[0179]

[0180] As Figure 8 shown, under the feedforward control of secondary compensation of storage volume, in working conditions 1 to 4, the water level fluctuations of some canal ponds exceed the water level boundary limit: in working condition 1, the water level fluctuations of 3 canal ponds significantly exceed the water level boundary limit, and the regulation effect is the worst; in working condition 2, the number of canal ponds exceeding the boundary limit is reduced to 2, and the regulation effect is improved; in working condition 3, only one canal pond significantly exceeds the boundary limit, but the maximum water level variation amplitudes in front of the gates downstream of the other three canal ponds are all close to the boundary value; in working condition 4, only one canal pond exceeds the boundary limit, and the maximum water level variation amplitudes of other canal ponds are all close to 0.05 m, and the regulation effect is the best. The above analysis shows that the regulation ability of the secondary compensation of storage volume to diverse working conditions has limitations. The feedforward control strategy based on the optimization model can control the water level fluctuations of each canal pond within the limit range of the water level boundary under all test working conditions. Compared with the secondary compensation method of storage volume, the regulation effect of this method is more significant, especially its adaptability is more prominent when facing complex water diversion working conditions in a multi-canal pond system. The operation time data in Table 3 show that the optimization model combined with the parallel PSO algorithm reduces the operation time by 99.03% (working condition 1), 99.03% (working condition 2), 99.06% (working condition 3), and 99.09% (working condition 4) compared with the standard PSO algorithm. It is proved that the optimization model combined with the parallel PSO algorithm is more suitable for demand scenarios with high real-time requirements.

[0181] In this case, by constructing a feed-forward control optimization model based on dynamic storage compensation, it is proved that the traditional method of secondary storage compensation has the technical defect of excessive overshoot of the water level in front of the sluice under complex working conditions. Through the calculation of the optimization model, while maintaining the real-time regulation efficiency, the maximum amplitude of the water level in front of the sluice under each working condition is strictly controlled within the specification limit of 0.1 m. On the basis of ensuring the response speed of the regulation system, this model significantly improves the stability of the water conveyance of the Middle Route Project of the South-to-North Water Diversion, realizes the goal of fine water level control, and fully reflects the technical advantages and application value of this method in long-distance water diversion projects. The research shows that this optimization model has good engineering applicability, and its application scenarios can be further expanded through the extension of multi-objective control strategies and cross-project verification in the future.

[0182] Referring to Figure 9 As shown, a structural block diagram of a multi-channel pool collaborative regulation system 900 according to an embodiment of the present invention is shown. As Figure 9 shown, the device may specifically include the following modules:

[0183] An overshoot flow calculation module 901, configured to calculate the overshoot flow of each channel pool under a preset water diversion working condition at the flow regulation moment;

[0184] A water level calculation module 902, configured to combine the overshoot flow through the objective function of the feed-forward control optimization model to calculate the maximum amplitude of the water level in front of the sluice of each channel pool under the preset water diversion working condition; wherein, the objective function is constructed through at least one constraint condition;

[0185] Dynamic compensation calculation 903, configured to perform dynamic storage compensation iterative calculation through the feed-forward control optimization model under the constraint of at least one constraint condition, with the goal of minimizing the maximum amplitude of the water level in front of the sluice, to obtain the target overshoot flow proportion coefficient of each channel pool;

[0186] A regulation instruction generation module 904, configured to generate a regulation instruction for each check gate according to the target overshoot flow proportion coefficient of each channel pool, and generate a collaborative regulation instruction set for multi-level check gates according to the regulation instructions of all check gates; wherein, each channel pool corresponds to a check gate;

[0187] A regulation module 905, configured to collaboratively regulate multi-level check gates according to the instruction set.

[0188] In an embodiment, an objective function construction module is configured to generate a first constraint condition, a second constraint condition, and a third constraint condition respectively according to the hourly water level amplitude, the 24-hour cumulative amplitude, and the instantaneous water level deviation;

[0189] An objective function for calculating the maximum amplitude of the water level in front of the sluice of each channel pool is constructed according to multiple constraint conditions, the water level value in front of the sluice of each channel pool under the preset water diversion working condition, and the target regulation water level of each channel pool.

[0190] In one embodiment, the objective function construction module is specifically configured to obtain the water level value in front of the gate of each canal pond under a preset water diversion condition;

[0191] Based on the overshoot flow of each canal pond and the water level value in front of the gate, the target regulation water level of each canal pond is obtained.

[0192] In one embodiment, the regulation instruction generation module 904 is specifically configured to calculate the regulation instruction of each check gate according to the target overshoot flow ratio coefficient of each canal pond in combination with the gate flow equation.

[0193] In one embodiment, the regulation instruction generation module 904 is specifically configured to obtain the flow rate through the gate of the check gate of each canal pond according to the target overshoot flow ratio coefficient of each canal pond and the overshoot flow of each canal pond;

[0194] Based on the flow rate through the gate and the gate flow equation, calculate the opening degree of the gate adjusted by each canal pond from the initial stable state to the final stable state;

[0195] Generate the regulation instruction of each check gate according to the opening degree of the gate of each canal pond.

[0196] In one embodiment, the overshoot flow calculation module 901 is configured to calculate the storage difference according to the initial condition and the stable condition of each canal pond after the first compensation of the storage volume under a preset water diversion condition;

[0197] Based on the initial condition of each canal pond and the dynamic wave equation, calculate the feed-forward time;

[0198] Perform the second compensation calculation of the storage volume according to the storage difference and the feed-forward time to obtain the overshoot flow of each canal pond.

[0199] In one embodiment, the dynamic compensation calculation 903 is specifically configured to, through the feed-forward control optimization model, with the minimization of the maximum amplitude change of the water level in front of the gate as the target, use the particle swarm optimization algorithm to screen and calculate the feasible solutions that meet the constraint conditions, and perform iteration through a preset number of times until the maximum number of iterations is reached, to obtain the target overshoot flow ratio coefficient of each canal pond.

[0200] The multi-channel pond collaborative regulation system provided in the above embodiment can implement the technical solution described in the above embodiment of the multi-channel pond collaborative regulation method. The specific implementation principles of the above modules or units can be referred to the corresponding content in the above embodiment of the multi-channel pond collaborative regulation method, which will not be elaborated here.

[0201] Based on the same inventive concept, the present invention also correspondingly provides an electronic device 1000, as Figure 10 shown. The electronic device 1000 includes a processor 1001, a memory 1002, and a display 1003. Figure 10Only some components of the electronic device 1000 are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively.

[0202] In some embodiments, the memory 1002 may be an internal storage unit of the electronic device 1000, such as a hard disk or memory of the electronic device 1000. In other embodiments, the memory 1002 may also be an external storage device of the electronic device 1000, such as a plug-in hard disk equipped on the electronic device 1000, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0203] Furthermore, the memory 1002 may also include both an internal storage unit of the electronic device 1000 and an external storage device. The memory 1002 is used to store the application software installed in the electronic device 1000 and various types of data.

[0204] In some embodiments, the processor 1001 may be a central processing unit (CPU), a microprocessor or other data processing chips, and is used to run the program code stored in the memory 1002 or process data, such as the multi-channel pool collaborative regulation method in the present invention.

[0205] In some embodiments, the display 1003 may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. The display 1003 is used to display the information in the electronic device 1000 and to display a visual user interface. The components 1001 - 1003 of the electronic device 1000 communicate with each other through a system bus.

[0206] In some embodiments of the present invention, when the processor 1001 executes the multi-channel pool collaborative regulation program in the memory 1002, the following steps can be implemented: calculating the overshoot flow rate of each channel pool under the preset water diversion condition at the flow regulation moment; calculating the maximum variation range of the water level in front of the gate of each channel pool under the preset water diversion condition by combining the overshoot flow rate with the objective function of the feedforward control optimization model; wherein, the objective function is constructed by at least one constraint condition; under the constraint of at least one constraint condition, through the feedforward control optimization model, with the minimization of the maximum variation range of the water level in front of the gate as the objective, performing dynamic storage compensation iterative calculation to obtain the target overshoot flow rate proportional coefficient of each channel pool; generating the regulation instruction of each check gate according to the target overshoot flow rate proportional coefficient of each channel pool, and generating the collaborative regulation instruction set of the multi-level check gates according to the regulation instructions of all the check gates; wherein, each channel pool corresponds to a check gate; and collaboratively regulating the multi-level check gates according to the instruction set.

[0207] It should be understood that when the processor 1001 executes the multi-channel pool collaborative regulation program in the memory 1002, in addition to the above functions, other functions can also be implemented. For specific details, reference can be made to the description of the corresponding method embodiments above.

[0208] Further, the type of the electronic device 1000 mentioned in the embodiments of the present invention is not specifically limited. The electronic device 1000 can be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of the portable electronic device include but are not limited to portable electronic devices equipped with IOS, android, microsoft or other operating systems. The above portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (such as a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1000 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (such as a touch panel).

[0209] Based on the same inventive concept, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is used to implement the multi-channel pool collaborative regulation method provided by the above various methods.

[0210] Those skilled in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a disk, an optical disc, a read-only memory or a random access memory, etc.

[0211] The above has introduced in detail a multi-channel pond collaborative regulation method, system, device and medium provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A multi-channel pond collaborative regulation method, characterized in that Including: Calculating the overshoot flow of each canal pool under the preset water diversion condition; Calculating the maximum variation range of the water level in front of the gate of each canal pool under the preset water diversion condition by combining the overshoot flow with the objective function of the feedforward control optimization model; wherein, the objective function is constructed by at least one constraint condition; Under the constraint of at least one constraint condition, through the feedforward control optimization model, with the minimization of the maximum variation range of the water level in front of the gate as the objective, performing dynamic storage compensation iterative calculation to obtain the target overshoot flow proportional coefficient of each canal pool; Generating a regulation instruction for each check gate according to the target overshoot flow proportional coefficient of each canal pool, and generating a collaborative regulation instruction set for the multi-level check gates according to the regulation instructions of all the check gates; wherein, each canal pool corresponds to one check gate; Collaboratively regulating the multi-level check gates according to the instruction set.

2. The multi-channel pond collaborative regulation method according to claim 1, wherein Also including: Generating a first constraint condition, a second constraint condition and a third constraint condition respectively according to the hourly water level variation range, the 24-hour cumulative variation range and the instantaneous water level deviation; Constructing an objective function for calculating the maximum variation range of the water level in front of the gate of each canal pool according to multiple constraint conditions, the water level value in front of the gate of each canal pool under the preset water diversion condition and the target regulation water level of each canal pool.

3. The multi-channel pond collaborative regulation method according to claim 2, wherein, Also including: Obtaining the water level value in front of the gate of each canal pool under the preset water diversion condition; Obtaining the target regulation water level of each canal pool according to the overshoot flow of each canal pool and the water level value in front of the gate.

4. A multi-channel pond collaborative regulation method according to any one of claims 1-3, characterized in that, The generating a regulation instruction for each check gate according to the target overshoot flow proportional coefficient of each canal pool specifically includes: Calculating the regulation instruction of each check gate according to the target overshoot flow proportional coefficient of each canal pool and combining with the gate flow equation.

5. A multi-channel pond collaborative regulation method according to claim 4, characterized in that The calculating the regulation instruction of each check gate according to the target overshoot flow proportional coefficient of each canal pool and combining with the gate flow equation specifically includes: Obtaining the flow through the check gate of each canal pool according to the target overshoot flow proportional coefficient of each canal pool and the overshoot flow of each canal pool; Calculating the gate opening adjusted by each canal pool from the initial stable state to the final stable state according to the flow through the gate and combining with the gate flow equation; Generating a regulation instruction for each check gate according to the gate opening of each canal pool.

6. A multi-channel pond collaborative regulation method according to claim 1, characterized in that The calculating the overshoot flow of each canal pool under the preset water diversion condition specifically includes: Calculating the storage difference according to the initial condition and the stable condition of each canal pool after the first storage compensation under the preset water diversion condition; Calculating the feedforward time according to the initial condition of each canal pool and combining with the dynamic wave equation; Performing the second storage compensation calculation according to the storage difference and the feedforward time to obtain the overshoot flow of each canal pool.

7. A multi-channel pool collaborative regulation method according to claim 1 or 6, characterized in that The performing dynamic storage compensation iterative calculation through the feedforward control optimization model with the minimization of the maximum variation range of the water level in front of the gate as the objective to obtain the target overshoot flow proportional coefficient of each canal pool specifically includes: Through the feedforward control optimization model, with the minimization of the maximum variation range of the water level in front of the gate as the objective, using the particle swarm optimization algorithm to screen and calculate the feasible solutions that meet the constraint conditions, and through a preset number of iterations until the maximum number of iterations is reached, obtaining the target overshoot flow proportional coefficient of each canal pool.

8. A multi-channel pond collaborative regulation system, characterized in that, Including: An overshoot flow calculation module for calculating the overshoot flow of each canal pool under the preset water diversion condition; The water level calculation module is used to calculate the maximum variation range of the water level before the sluice of each canal pool under the preset water diversion condition by combining the overshoot flow rate with the objective function of the feedforward control optimization model; wherein, the objective function is constructed by at least one constraint condition; The dynamic compensation calculation is used to perform dynamic storage compensation iterative calculation with the minimization of the maximum variation range of the water level before the sluice as the objective through the feedforward control optimization model under the constraint of at least one constraint condition, so as to obtain the target overshoot flow rate proportionality coefficient of each canal pool; The regulation instruction generation module is used to generate the regulation instructions of each check gate according to the target overshoot flow rate proportionality coefficient of each canal pool, and generate the cooperative regulation instruction set of the multi-level check gates according to the regulation instructions of all the check gates; wherein, each canal pool corresponds to one check gate; The regulation module is used to cooperatively regulate the multi-level check gates according to the instruction set; 9. An electronic device, characterized in that, It includes a memory and a processor, wherein, The memory is used to store programs; The processor is coupled with the memory and is used to execute the program stored in the memory to implement the steps in a multi-canal pool cooperative regulation method described in any one of claims 1 to 7 above; 10. A computer-readable storage medium, characterized in that, It is used to store computer-readable programs or instructions, and when the programs or instructions are executed by the processor, the steps in a multi-canal pool cooperative regulation method described in any one of claims 1 to 7 above can be implemented.

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