Fishery water body environment dissolved oxygen control optimization method

Abstract

The invention discloses a fishery water body environment dissolved oxygen control optimization method, and belongs to the technical field of control optimization, and the method comprises the steps: S1, constructing a fishery water body management dissolved oxygen PID control system; s2, introducing an improved barber optimization algorithm; s3, setting a dissolved oxygen PID control parameter in the fishery water body management dissolved oxygen PID control system by using an improved barber optimization algorithm, and obtaining an optimal PID control parameter through optimization; and S4, setting the three optimal control parameters obtained through optimization by using the improved barber optimization algorithm as parameters of a dissolved oxygen PID controller in the fishery water body management dissolved oxygen PID control system, and optimizing a dissolved oxygen regulation control effect. By introducing the improved barber optimization algorithm, higher optimization precision is realized, and a PID controller parameter solution with better performance is obtained, so that the dissolved oxygen concentration of the water body can be more accurately and stably maintained, and a reliable technical guarantee is provided for fishery water body dissolved oxygen management.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of control optimization, and particularly relates to a fishery water body environment dissolved oxygen control optimization method. BACKGROUND

[0002] In modern high-density aquaculture, the dissolved oxygen (DO) concentration of water body is a key environmental factor determining the success or failure of aquaculture. Too low dissolved oxygen will directly lead to the death of aquatic products due to hypoxia, and too high dissolved oxygen will increase energy consumption and may cause bubble disease. The fishery water body dissolved oxygen system is a complex dynamic process, and its characteristics include: long response time of large water bodies to oxygenation, time required for oxygen to diffuse throughout the water body after the oxygenation device is turned on, susceptibility to disturbance, and nonlinearity. PID controllers are widely used in industrial process control due to their simple structure, good robustness, and high reliability. The performance of a PID controller largely depends on the tuning of the proportional gain Kp, the integral time Ki, and the derivative time Kd. However, traditional PID parameter tuning methods, such as the Ziegler-Nichols empirical formula method or the trial-and-error method, have significant limitations. These methods often rely on the experience of engineers and are time-consuming and labor-intensive to tune for complex industrial objects. Moreover, they may not guarantee optimal control performance, especially for controlled objects with complex dynamic characteristics such as nonlinearity, large time delay, and non-minimum phase. In such cases, traditional tuning methods may only obtain a set of parameters that are barely usable or even perform poorly, leading to slow system response, large overshoot, and sustained oscillation, which cannot meet the modern industrial requirements for high precision and high stability control. SUMMARY

[0003] To overcome the technical problems described in the background, the present application provides a fishery water body environment dissolved oxygen control optimization method. By introducing an improved barber optimization algorithm, the method effectively solves the problems of traditional optimization algorithms when facing controlled objects with complex characteristics such as large time delay and non-minimum phase, such as falling into local optimum, insufficient optimization precision, and poor control effect. The method achieves higher optimization precision and obtains a more optimal PID controller parameter solution.

[0004] The technical solution of the present application is as follows: a fishery water body environment dissolved oxygen control optimization method, comprising the following steps: S1, constructing a fishery water body management dissolved oxygen PID control system, including a dissolved oxygen error calculation module, a dissolved oxygen PID controller module, an improved barber optimization algorithm module, a dissolved oxygen regulation module, and a dissolved oxygen monitoring module; S2, introducing an improved barber optimization algorithm, and the specific improvement strategy is: S201, introducing a creative proportional parameter Cr in the exploration stage. If the random number is less than Cr, a hybrid guidance strategy combining the global optimal solution and the random solution is adopted, otherwise the original strategy is followed; S202. In the utilization phase, an elite-guided precise pruning strategy is adopted. The new position update formula includes a learning part toward the global optimal solution and a random perturbation part that decays with iterations. S203, introduce stagnation detection and population reset mechanism, when the global optimal solution is continuous When there is no update, the proportion Reset inferior individuals; S3. Use the improved barber optimization algorithm to adjust the dissolved oxygen PID control parameters in the dissolved oxygen PID control system for fishery water management, and obtain the best control parameters by searching for the best control parameters. 、 、 ; S4. The three optimal control parameters obtained by the improved barber optimization algorithm are set as the parameters of the dissolved oxygen PID controller in the dissolved oxygen PID control system for fishery water management to optimize the dissolved oxygen regulation control effect.

[0005] Furthermore, in the dissolved oxygen PID control system for fishery water management constructed in step S1, the actual dissolved oxygen value is collected by the dissolved oxygen monitoring module and transmitted to the dissolved oxygen error calculation module. The dissolved oxygen error calculation module receives the set target dissolved oxygen and calculates the error between the target dissolved oxygen and the actual dissolved oxygen, and then outputs a real-time error signal to the dissolved oxygen PID controller module. The internal parameters of the PID controller module are optimized by the improved barber optimization algorithm IBaOA module. 、 、 Continuous optimization is performed to optimize the control performance. The optimized PID controller module calculates the control quantity based on the error signal and then outputs it to the dissolved oxygen regulation module to perform the dissolved oxygen concentration regulation operation.

[0006] Furthermore, in the exploration phase of step S201, a guidance solution is generated for each current individual to guide its position update. , guide solution The generation strategy is determined by the creative ratio parameter Decision, specific steps include: C2011, the random number randomly selected from the interval [0,1] is less than When using creative strategies to generate guided solutions , the specific formula is: ; in is the generated guiding solution, is a random weight in the interval [0,1], is the current global optimal solution, is a randomly selected individual in the population; C2012, the random number from the interval [0, 1] is not less than , the guide solution is generated by using the conventional strategy, and one of all the solutions with better fitness values than the current individual in the population is randomly selected as the guide solution , if there is no better solution, one individual in the whole population except the current individual is randomly selected as the guide solution .

[0007] Further, in the utilization stage of step S202, the improved algorithm uses a new position update formula: ; wherein is the learning step size to the global optimal solution, is a random disturbance term.

[0008] Further, the calculation formula of the learning step size to the global optimal solution is: ; wherein is an elite guide learning factor, is a random vector in the interval [0, 1].

[0009] Further, the calculation formula of the random disturbance term is: ; wherein is a random disturbance learning factor, is the current iteration number, is the maximum iteration number, is a random vector in the interval [0, 1].

[0010] Further, a stagnation detection and population reset mechanism is added in step S203, including the following steps: C2031, after each iteration, the global optimal solution update state is detected, and if the optimal fitness value has not been improved continuously, the stagnation counter is increased by 1; C2032, when , the reset mechanism is triggered, and the first poor individuals are selected in descending order of fitness value, wherein is the population size; C2033, the poor individual positions are reinitialized and the fitness values are evaluated according to the reset formula , wherein lb is the lower limit of the search space, ub is the upper limit of the search space, dim is the problem dimension and takes the value 3, and the rand function is used to generate a random vector with 1 row and dim columns, and each element is uniformly distributed in the interval (0, 1). C2034, update the global optimal solution after reset and clear the stall counter .

[0011] Further, in step S3, the specific steps of optimizing the PID controller parameters using the improved barber optimization algorithm are as follows: S301, initialize the core parameters of the improved barber optimization algorithm, including the number of populations , problem dimension , maximum number of iterations , lower limit of search space and upper limit , creativity ratio , stall tolerance upper limit , reset ratio , and randomly generate initial population positions in three-dimensional space; S302, establish the mapping relationship between the algorithm solution space and the PID controller parameters, and the three components of the individual position vector correspond to the proportional coefficient , integral coefficient , and differential coefficient of the PID controller, respectively; S303, define the fitness function for evaluating the performance of the PID control , the mathematical expression is: ; Among them is the time multiplied by the absolute error integral, is the overshoot of the system response, is the overshoot penalty term, is the overshoot upper limit, is the settling time of the system response, is the settling time penalty term, , , is the weight coefficient; S304, execute the algorithm core update mechanism, loop iteration, and in each iteration, update each individual in turn, and execute the stall detection and population reset mechanism at the end of each iteration; S305, terminate the optimization when the number of iterations reaches , and output the global optimal individual position vector as the optimal parameter combination of the PID controller , , .

[0012] Further, in step S4, the specific process of applying the optimized parameters to the control system is as follows: S401. When a preset periodic tuning timing is met or a significant degradation in system performance is detected, the improved barber optimization algorithm module is automatically triggered to start a parameter optimization process; S402, improved barber optimization algorithm module based on fitness function Evaluate individual fitness and iteratively update the population through exploration, exploitation, and stagnation reset strategies; S403: When the algorithm reaches the termination condition, the three component values ​​are sequentially extracted from the final output global optimal individual position vector and respectively resolved into proportional gain parameters: , integral coefficient and differential coefficients Specific value of S404, through the system parameter configuration interface, the parsed 、 and The value is written into the corresponding parameter register of the dissolved oxygen PID controller module so that the controller immediately adopts the new parameters for optimized control.

[0013] Due to the adoption of the above-mentioned technology, the present invention has the following beneficial effects: by setting up a creative exploration and stagnation reset mechanism, the present invention achieves higher optimization accuracy and faster convergence speed, and finds a PID controller parameter solution that is better than the barber optimization algorithm in comprehensive evaluation indicators. In particular, by identifying a differential coefficient Kd that is much larger than the conventional value, the PID controller is directly given powerful damping characteristics and predictive capabilities: when facing a controlled object with complex characteristics such as large time lag and initial reverse response, the optimized closed-loop system exhibits smaller overshoot and weak oscillation, smoother response, and stronger robustness, thereby being able to maintain the dissolved oxygen concentration in the water body more accurately and stably, providing a reliable technical guarantee for the management of dissolved oxygen in fishery water bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic flow diagram of the present invention; Figure 2 is a flow chart of applying the optimized parameters of the present invention to a control system; Figure 3 This is a model diagram of the dissolved oxygen PID control system for fishery water management of the present invention; Figure 4 1 is a comparison chart of the optimal fitness values ​​of the improved barber optimization algorithm of the present invention and the barber optimization algorithm; Figure 5 This is a comparison chart of the Kp parameters of the improved barber optimization algorithm of the present invention and the barber optimization algorithm; Figure 6 This is a comparison chart of the Ki parameters of the improved barber optimization algorithm of the present invention and the barber optimization algorithm; Figure 7 This is a comparison chart of the Kd parameters of the improved barber optimization algorithm of the present invention and the barber optimization algorithm; Figure 8 This is a comparison chart of the system responses of the improved barber optimization algorithm of the present invention and the barber optimization algorithm. DETAILED DESCRIPTION

[0015] Example 1: Figure 1 As shown, the present invention provides a method for optimizing dissolved oxygen control in a fishery water environment, comprising the following steps: S1. Construct a dissolved oxygen PID control system for fishery water management, including a dissolved oxygen error calculation module, a dissolved oxygen PID controller module, an improved barber optimization algorithm module, an aeration pump module, and a dissolved oxygen probe module; S2. Introduce the improved barber optimization algorithm. The specific improvement strategy is: S201, introducing a creative ratio parameter Cr in the exploration phase, and adopting a hybrid guidance strategy combining the global optimal solution and the random solution when the random number is less than Cr, otherwise the original strategy is used; S202. In the utilization phase, an elite-guided precise pruning strategy is adopted. The new position update formula includes a learning part toward the global optimal solution and a random perturbation part that decays with iterations. S203, introduce stagnation detection and population reset mechanism, when the global optimal solution is continuous When there is no update, the proportion Reset inferior individuals; S3. Use the improved barber optimization algorithm to adjust the dissolved oxygen PID control parameters in the dissolved oxygen PID control system for fishery water management, and obtain the best control parameters by searching for the optimal control parameters. 、 、 ; S4. The three optimal control parameters obtained by the improved barber optimization algorithm are set as the parameters of the dissolved oxygen PID controller in the dissolved oxygen PID control system for fishery water management to optimize the dissolved oxygen regulation control effect.

[0016] In the dissolved oxygen PID control system for fishery water management constructed in step S1, the actual dissolved oxygen value is collected by the dissolved oxygen probe module and transmitted to the dissolved oxygen error calculation module. The dissolved oxygen error calculation module receives the set target dissolved oxygen and calculates the error between the target dissolved oxygen and the actual dissolved oxygen, and then outputs a real-time error signal to the dissolved oxygen PID controller module. The internal parameters of the PID controller module are optimized by the improved barber optimization algorithm IBaOA module. 、 、 The PID controller module is optimized by continuous optimization to optimize the control performance, and the optimized PID controller module calculates the control amount according to the error signal, and then outputs the control amount to the aeration pump module to perform the adjustment operation of the dissolved oxygen concentration.

[0017] In the exploration phase of step S201, a guide solution for guiding the position update of each current individual is generated The generation strategy of the guide solution is determined by the creative proportion parameter , and the specific steps include: C2011, when the random number randomly selected from the interval [0, 1] is less than , the guide solution is generated by the creative strategy, and the specific formula is: ; Wherein is the generated guide solution, is a random weight in the interval [0, 1], is the current global optimal solution, is a randomly selected individual in the population; C2012, when the random number randomly selected from the interval [0, 1] is not less than , the guide solution is generated by the conventional strategy, and then a solution with a better fitness value than the current individual is randomly selected from the population as the guide solution , and if there is no better solution, a individual is randomly selected from the entire population except itself as the guide solution .

[0018] In the utilization phase of step S202, the improved algorithm uses a new position update formula: ; Wherein is the learning step size to the global optimal solution, is a random disturbance term.

[0019] The calculation formula of the learning step size to the global optimal solution is: ; Wherein is the elite guide learning factor, is a random vector in the interval [0, 1].

[0020] The calculation formula of the random disturbance term is: ; Wherein is the random disturbance learning factor, is the current iteration number, is the maximum number of iterations, is a random vector in the interval [0,1].

[0021] In step S203, the stagnation detection and population reset mechanism is added, including the following steps: C2031, after each iteration, detect the global optimal solution update state, if the optimal fitness value is not improved continuously, the stagnation counter is increased by 1; C2032, when , trigger the reset mechanism, then sort by fitness value in descending order, select the first bad individuals, where is the population size; C2033, reinitialize the position of the bad individuals according to the reset formula , and evaluate the fitness, where lb is the lower limit of the search space, ub is the upper limit of the search space, dim is the problem dimension and takes the value 3, and the rand function is used to generate a 1-row dim-column random vector with each element uniformly distributed in the interval (0,1); C2034, after resetting, update the global optimal solution and clear the stagnation counter .

[0022] In step S3, the specific steps of optimizing the PID controller parameters using the improved barber optimization algorithm are as follows: Step one, construct and define the fishery water dissolved oxygen PID control system model, the controlled object is the fishery aquaculture oxygenation system, the specific transfer function model is , where s is a complex variable.

[0023] Among them, the system decomposition and synthesis method is adopted, the entire oxygenation control system is decomposed into actuator, controlled process and sensor three main links, and each link is modeled respectively. The total transfer function of the system is the product of the transfer functions of each link, that is .

[0024] Among them, for the actuator model , the actuator is the aeration pump for adjusting the dissolved oxygen of the water body. When the controller issues a power adjustment command, the motor of the pump has a short delay in response due to its own electrical and mechanical inertia, and it cannot be completed instantaneously. This dynamic process can be accurately described by a first-order lag element, and the transfer function model is: , by setting the actuator gain (normalized), the time constant seconds, the actuator model according to the response characteristics of the aeration pump is: .

[0025] where the controlled process model The controlled process is the aquaculture water body itself, and the change of its dissolved oxygen concentration is a complex dynamic balance process, mainly affected by two factors: one is the amount of oxygen injected by the aeration pump, and the other is the oxygen consumption caused by biological respiration and organic matter decomposition in the water body. In addition, it takes time for oxygen to diffuse from the aeration point to the sensor measurement point, which constitutes the pure delay of the system. This process can be described by a first-order lag element in series with a pure delay element, and the transfer function model is: According to the water quality balance principle, the process gain is set as , which reflects the relationship between the aeration rate and the dissolved oxygen concentration at steady state; the time constant is set as seconds, which reflects the large time delay characteristics of the slow change of dissolved oxygen concentration in large water bodies; the pure delay time is set as seconds, which represents the transmission delay of oxygen in the water body, and thus the controlled process model is: .

[0026] where the dissolved oxygen sensor model The sensor is a dissolved oxygen probe used to measure the real-time dissolved oxygen concentration, and due to the time required for internal chemical or optical reactions and material diffusion, the measured value lags behind the true value. The transfer function model is: By setting the sensor gain , the time constant seconds, the sensor model is: .

[0027] The transfer functions of the above three elements are multiplied in series to obtain the total open-loop transfer function of the system: In order to facilitate calculation in the simulation environment, the pure delay term in the model needs to be approximated by a rational function. A first-order Pade approximation is used, and its expression is Substitute the Pade approximation into the total model and perform polynomial expansion and simplification to finally obtain a fourth-order transfer function model that can completely describe the dynamic characteristics of the controlled object : which can relatively accurately reflect the complex characteristics of the fishery oxygenation system such as slow response, large time delay, and initial inverse response.

[0028] Step two, configure the core parameters and fitness function of the improved barber optimization algorithm IBaOA, where the population size N = 50; the problem dimension dim = 3, the maximum number of iterations max iter = 100, the lower limit of the search space lb = [0.5, 0.001, 0.1], the upper limit of the search space ub = [15.0, 0.5, 10.0], the creativity ratio Cr = 0.2, the upper limit of the stagnation tolerance = 15, the reset ratio = 0.15; the fitness function is a cost function to be minimized, whose expression is where the formula of the integral of time multiplied by absolute error ITAE is , which is used to comprehensively evaluate the rapidity and stability of the system, and the simulation is performed on a step response with a set value of 2.0, with a total simulation time of 6000 seconds; the formula of the overshoot penalty term is , which is applied when the overshoot exceeds the upper limit , and the penalty weight is set to 10000; the formula of the settling time penalty term : is , which is applied when the settling time exceeds the upper limit of 100 seconds. The penalty weight is set to 10000; the weight coefficient is set to 1.0; and before calculating the fitness, it is checked whether the closed-loop system is stable, and if not, a huge cost value is returned to guide the algorithm to abandon this set of parameters.

[0029] Step three, execute the improved barber optimization algorithm for PID parameter optimization algorithm After starting, first generate 50 individuals randomly in the preset search space according to the formula as the initial population, and evaluate their fitness; in the subsequent 100 iterations, the position vector of each individual is updated through the following exploration, utilization and stagnation reset stages.

[0030] (1) Exploration stage: for each individual , first generate a random number in the interval and compare it with the creativity ratio ; if the random number is less than , adopt the creativity strategy to generate a new guided solution by mixing the global optimal solution and the random solution according to the formula​​ Otherwise, the conventional strategy is adopted to select an individual with better fitness from the population as the guiding solution. ; Determine the guided solution Then, according to the exploration update formula Calculate the new position where for A random vector in the interval, A random integer vector of 1 or 2. Calculate the fitness of the new position and accept the update if it is better than the current one.

[0031] (2) Utilization phase: For the same individual, the position is updated by using the elite-guided precise pruning strategy; the new position is determined by the current position. , a solution towards the global optimal solution Learning items , and a random perturbation term that decays with the number of iterations The three are added together, namely , where the calculation formula for the learning item is , where the learning factor Take 1.8, for The random vector of the interval; the calculation formula of the random disturbance term is , where the learning factor Take 0.5, is the current iteration number, is the maximum number of iterations (100), for A random vector in the interval; calculate the fitness of the new position, and accept the update again if it is better than the current one.

[0032] (3) Stagnation reset mechanism: After each iteration, check whether the fitness value of the global optimal solution has improved. If it has not improved for 15 consecutive times, that is, , then the reset mechanism is activated and the 15% individuals with the worst performance in the population, i.e. , according to the formula Re-initialize randomly to increase population diversity and help the algorithm escape from local optimality.

[0033] Step 4: In Matlab, the duration is set to 6000 seconds, the step signal amplitude is 2.0, and after 100 iterations, the improved barber optimization algorithm IBaOA converges to an optimal fitness value of 9823.7864, which is significantly lower than BaOA's 12280.0780. Output the optimal fitness value and the corresponding optimal PID parameter combination. =2.1646, =0.0010, =10.0000.

[0034] where, the improved barber optimization algorithm IBaOA is compared with the barber optimization algorithm BaOA, as shown in Figure 4 The optimal fitness value to which the improved barber optimization algorithm IBaOA converges is 9823.7864, which is significantly lower than 12280.0780 of the barber optimization algorithm BaOA, and the optimization accuracy is higher, and a more optimal solution is found. As can be seen from the fitness convergence curve, the improved barber optimization algorithm IBaOA has a very fast decline in the early iterations, and quickly converges to the vicinity of the final optimal value in about 11 iterations, while the barber optimization algorithm BaOA is also rapidly declining at the beginning, but after about 15 iterations it falls into a local optimum, and the fitness value has no obvious improvement. The rapid decline of the improved barber optimization algorithm IBaOA curve near the 8th-10th iteration may reflect the effectiveness of the stagnation reset and creative exploration mechanism, which successfully helps the algorithm to jump out of the early trap and find a better solution space.

[0035] As shown in Figure 5 The proportional coefficient $K_p$ values finally found by the two algorithms are very close, both converging to about 2.16, with the barber optimization algorithm BaOA being 2.1624 and the improved barber optimization algorithm IBaOA being 2.1646, indicating that for this controlled object, the optimal proportional gain is in a relatively clear interval.

[0036] As shown in Figure 6 The integral coefficient $K_i$ finally found by the two algorithms both converges to the minimum value 0.0010, which is represented as a nearly zero straight line in the figure, indicating that for the dissolved oxygen PID control system of fishery water management, the integral action needs to be very weak in order to avoid integral saturation and oscillation.

[0037] As shown in Figure 7 The differential coefficient $K_d$ finally found by the two algorithms, the barber optimization algorithm BaOA converges to the value of 5.4507, and the improved barber optimization algorithm IBaOA finds a value much larger than the former, reaching the search upper limit of 10.0000, indicating that the barber optimization algorithm BaOA is relatively conservative and converges to a suboptimal value early.

[0038] As shown in Figure 8 The PID controller optimized by the barber optimization algorithm BaOA produces a larger overshoot, and the response curve oscillates more, while the PID controller optimized by the improved barber optimization algorithm IBaOA produces a smaller overshoot, and the response curve oscillates less, and can be more quickly stabilized near the set value, showing better damping characteristics and robustness.

[0039] AsFigure 2 As shown, in step S4, the specific process of applying the optimized parameters to the control system is as follows: S401, when the preset periodic tuning opportunity is met or the system performance is detected to be significantly reduced, the improved barber optimization algorithm module is automatically triggered to start the parameter optimization process; S402, the improved barber optimization algorithm module is based on the fitness function Evaluate individual fitness, and iteratively update the population through exploration, utilization and stagnation reset strategies; S403, when the algorithm reaches the termination condition, extract the three component values from the final output global optimal individual position vector in order, and respectively analyze them into specific numerical values of proportional gain parameter , integral coefficient and differential coefficient ; S404, through the parameter configuration interface of the fishery water body management dissolved oxygen PID control system, write the numerical values of , and analyzed into the corresponding parameter registers of the dissolved oxygen PID controller module, so that the controller immediately adopts new parameters for optimization control.

Claims

1. A method for optimizing dissolved oxygen control in a fishery water environment, characterized by: The steps include: S1. Construct a dissolved oxygen PID control system for fishery water management, including a dissolved oxygen error calculation module, a dissolved oxygen PID controller module, an improved barber optimization algorithm module, a dissolved oxygen regulation module, and a dissolved oxygen monitoring module; S2. Introduce the improved barber optimization algorithm. The specific improvement strategy is: S201, introducing a creative ratio parameter Cr in the exploration phase, and adopting a hybrid guidance strategy combining the global optimal solution and the random solution when the random number is less than Cr, otherwise the original strategy is used; S202. In the utilization phase, an elite-guided precise pruning strategy is adopted. The new position update formula includes a learning part toward the global optimal solution and a random perturbation part that decays with iterations. S203, introduce stagnation detection and population reset mechanism, when the global optimal solution is continuous When there is no update, the proportion Reset inferior individuals; S3. Use the improved barber optimization algorithm to adjust the dissolved oxygen PID control parameters in the dissolved oxygen PID control system for fishery water management, and obtain the best control parameters by searching for the optimal control parameters. 、 、 ; S4. The three optimal control parameters obtained by the improved barber optimization algorithm are set as the parameters of the dissolved oxygen PID controller in the dissolved oxygen PID control system for fishery water management to optimize the dissolved oxygen regulation control effect.

2. The method for optimizing dissolved oxygen control in a fishery water environment according to claim 1, characterized in that: In the dissolved oxygen PID control system for fishery water management constructed in step S1, the actual dissolved oxygen value is collected by the dissolved oxygen monitoring module and transmitted to the dissolved oxygen error calculation module. The dissolved oxygen error calculation module receives the set target dissolved oxygen and calculates the error between the target dissolved oxygen and the actual dissolved oxygen, and then outputs a real-time error signal to the dissolved oxygen PID controller module. The internal parameters of the PID controller module are optimized by the improved barber optimization algorithm IBaOA module. 、 、 Continuous optimization is performed to optimize the control performance. The optimized PID controller module calculates the control quantity based on the error signal and then outputs it to the dissolved oxygen regulation module to perform the dissolved oxygen concentration regulation operation.

3. The method for optimizing dissolved oxygen control in a fishery water environment according to claim 1, wherein: In the exploration phase of step S201, a guidance solution is generated for each current individual to guide its position update. , guide solution The generation strategy is determined by the creative ratio parameter Decision, specific steps include: C2011, the random number randomly selected from the interval [0,1] is less than When using creative strategies to generate guided solutions , the specific formula is: ； in is the generated guiding solution, is a random weight in the interval [0,1], is the current global optimal solution, is a randomly selected individual in the population; C2012, the random number randomly selected from the interval [0,1] is not less than When the conventional strategy is used to generate the guide solution, a random selection is made from all the solutions in the population whose fitness values ​​are better than the current individual as the guide solution. If there is no better solution, randomly select an individual from the entire population except itself as the guide solution .

4. The method for optimizing dissolved oxygen control in a fishery water environment according to claim 1, wherein: In the utilization phase of step S202, the improved algorithm adopts a new position update formula: ； in is the learning step size towards the global optimal solution, is a random disturbance term.

5. The method for optimizing dissolved oxygen control in a fishery water environment according to claim 4, characterized in that: The calculation formula for learning the step length towards the global optimal solution is: ； in To guide the learning factors for the elites, is a random vector in the interval [0,1].

6. A method for optimizing dissolved oxygen control in a fishery water environment according to claim 4, characterized in that: Random disturbance term The calculation formula is: ； in is the random perturbation learning factor, is the current iteration number, is the maximum number of iterations, is a random vector in the interval [0,1].

7. The method for optimizing dissolved oxygen control in a fishery water environment according to claim 1, characterized in that: In step S203, a stagnation detection and population reset mechanism is added, including the following steps: C2031, after each iteration, the global optimal solution update status is detected. If the optimal fitness value does not improve continuously, the counter is stopped. Increase by 1; C2032, when When the reset mechanism is triggered, the fitness value is sorted in descending order, and the first inferior individuals, among which is the population size; C2033, according to the reset formula , reinitialize the inferior individual position and evaluate the fitness, where lb is the lower limit of the search space, ub is the upper limit of the search space, dim is the problem dimension and its value is 3, and the rand function is used to generate a random vector with 1 row and dim columns and each element is uniformly distributed in the interval (0,1); C2034, update the global optimal solution after reset and clear the stagnation counter .

8. The method for optimizing dissolved oxygen control in a fishery water environment according to claim 1, wherein: In step S3, the specific steps of optimizing the PID controller parameters using the improved barber optimization algorithm are as follows: S301. Initialize the core parameters of the improved barber optimization algorithm, including the population size , problem dimension , maximum number of iterations , the lower limit of the search space and upper limit , creative ratio , stagnation tolerance upper limit , reset the scale , and randomly generate the initial population position in three-dimensional space; S302, establish the mapping relationship between the algorithm solution space and the PID controller parameters, the three components of the individual position vector correspond to the proportional coefficients of the PID controller , integral coefficient , differential coefficient ; S303. Define the fitness function for evaluating PID control performance , its mathematical expression is: ； in is time multiplied by the integral of the absolute error, is the overshoot of the system response, is the overshoot penalty term, is the upper limit of overshoot, is the stabilization time of the system response, is the stable time penalty term, 、 、 is the weight coefficient; S304: Execute the algorithm core update mechanism, iterate in a loop, and perform exploration phase update and exploitation phase update for each individual in each iteration, and perform stagnation detection and population reset mechanism at the end of each iteration; S305, when the number of iterations reaches The optimization is terminated when , and the global optimal individual position vector is output as the optimal parameter combination of the PID controller 、 、 .

9. The method for optimizing dissolved oxygen control in a fishery water environment according to claim 1, characterized in that: In step S4, the specific process of applying the optimized parameters to the control system is as follows: S401. When a preset periodic tuning timing is met or a significant degradation in system performance is detected, the improved barber optimization algorithm module is automatically triggered to start a parameter optimization process; S402, improved barber optimization algorithm module based on fitness function Evaluate individual fitness and iteratively update the population through exploration, exploitation, and stagnation reset strategies; S403: When the algorithm reaches the termination condition, the three component values ​​are sequentially extracted from the final output global optimal individual position vector and respectively resolved into proportional gain parameters: , integral coefficient and differential coefficients Specific value of S404, through the system parameter configuration interface, the parsed 、 and The value is written into the corresponding parameter register of the dissolved oxygen PID controller module so that the controller immediately adopts the new parameters for optimized control.

Images