A pH control optimization method for fishery experiments

Abstract

The invention relates to the technical field of control optimization, and discloses a fishery experiment pH control optimization method, comprising: S1, setting a target pH value of a fishery experiment environment, and inputting a real-time pH value into a pH controller of the fishery experiment; S2, adjusting the proportional coefficient, integral coefficient and differential coefficient of a traditional PID control algorithm by using an improved child learning optimizer, applying the optimized PID control algorithm to the pH controller of the fishery experiment, and obtaining an enhanced pH controller; S3, calculating a pH difference of an experimental environment between a real-time pH value and a target pH value, and inputting the difference into the enhanced pH controller, and outputting a pH control quantity signal; S4, the pH control quantity signal outputs an adjusted concentration control quantity of carbon dioxide through a control quantity signal conversion model, adjusting the carbon dioxide concentration of the fishery experiment environment, returning to execute S1-S4 to monitor the real-time pH value, adjusting the pH control quantity signal, and quickly and accurately reaching the target pH value.

Description

Technical Field

[0001] The invention relates to the technical field of control optimization, and relates to a pH control optimization method for fishery experiments. Background Art

[0002] In fishery experiments, pH is a key factor affecting water quality and fish health. An appropriate pH range can promote the growth and reproduction of fish and improve the accuracy and success rate of experiments. However, since the pH value of water changes under different environmental conditions, too high or too low pH values ​​may have a negative impact on the health of fish. An appropriate pH range (usually between 6.5-8.5) can promote the normal growth and metabolism of fish and help improve the accuracy of experiments; therefore, an effective pH controller is needed to stabilize the pH value of water.

[0003] The patent with publication number CN204883411U proposes a pH flow control system. The controller adjusts the flow rate of the metering pump according to the set target pH value through the signal fed back by the pH sensor, thereby adjusting the pH value of the seawater in the acidified stock solution pool to stabilize it at the set target value. Its pH control method has no feedback control, while the patent with publication number CN104898723A proposes a PID-based intelligent pH control system for aquaculture ponds. Although this method has negative feedback control and improves the control accuracy to a certain extent, the PID control algorithm relies on proportional, integral and differential control coefficients, resulting in poor control sensitivity and weak adaptability under different experimental environments. The control overshoot is large and the time to reach the target control value is long, resulting in unsatisfactory control effect.

[0004] At present, metaheuristic algorithms are on the rise. Metaheuristic algorithms can be used to optimize PID control algorithms and find the parameters of the PID control algorithm that is most suitable for pH value regulation in fishery experimental environments in the shortest time, including proportional coefficients, integral coefficients, and differential coefficients. Kids Learning Optimizer (KLO) is a new metaheuristic algorithm based on early human social and cognitive learning behaviors. This algorithm is inspired by the learning process of children in the family and social environment. It simulates the growth and learning process of children in stages to achieve the exploration and development of complex problem spaces. It has strong global search capabilities and fast convergence characteristics. However, KLO's staged learning mechanism is strong in global exploration. However, when the search space is large, it takes more time to find solutions in a wide area in the early stage, which affects the overall convergence speed. Summary of the invention

[0005] In response to the problems involved in the background technology, the present invention proposes a method for optimizing pH control in fishery experiments. The traditional PID control algorithm is optimized by improving the child learning optimizer, and the pH controller of the fishery experiment is adjusted using the optimized PID control algorithm, so that the pH controller can accurately and sensitively control the concentration of carbon dioxide in the experimental environment, and the fishery experiment environment is kept within the optimal pH value range by controlling the concentration of carbon dioxide.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a fishery experiment pH control optimization method, by improving the fishery experiment pH controller, to achieve accurate and stable control of the pH value of the fishery experiment environment, the specific steps are as follows:

[0007] S1. Setting the target pH value of the fishery experiment environment, inputting the real-time pH value into the pH controller of the fishery experiment, wherein the pH controller includes a pH error calculation model of the fishery experiment environment, a PID control algorithm and a control quantity signal conversion model;

[0008] S2. Use the improved child learning optimizer to adjust the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm, obtain the best proportional coefficient, integral coefficient and differential coefficient, input the traditional PID control algorithm model, obtain the optimized PID control algorithm for the pH controller of the fishery experiment, and obtain the enhanced pH controller;

[0009] S3, pH error calculation model of fishery experimental environment calculates the pH difference between the real-time pH value and the target pH value of the experimental environment, the pH difference of the experimental environment is input into the enhanced pH controller, and the pH control signal u(t) is output;

[0010] S4, the pH control quantity signal outputs the adjusted concentration control quantity of carbon dioxide through the control quantity signal conversion model, adjusts the carbon dioxide concentration of the experimental environment, returns to execute S1-S4 to continuously monitor the real-time pH value, adjusts the pH control quantity signal u(t), and reaches the target pH value of the fishery experiment.

[0011] Specifically, the pH controller is used to control the pH value of the fishery experimental environment. The pH controller controls the carbon dioxide solubility in the experimental environment to control the water quality pH required for the experiment within the set range. The pH controller is optimized. The core control model of the pH controller is the PID control algorithm. The optimized PID control algorithm is obtained by optimizing the proportional coefficient, integral coefficient and differential coefficient of the PID control algorithm, so that the control parameters of the control algorithm of the pH controller of the fishery experiment are more accurate, and the pH control accuracy and sensitivity of the pH controller are improved; the mathematical model of the PID control algorithm is:

[0012] (1);

[0013] In the mathematical model (1), is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the pH difference of the experimental environment, is the pH control signal value at the tth moment, Total adjustment time for the pH controller system.

[0014] Specifically, the control quantity signal conversion model is used to convert the pH control quantity signal u(t) output by the optimized PID control algorithm of the pH controller of the fishery experiment into the adjusted concentration control quantity of carbon dioxide, and adjust the pH value in the experimental environment by adjusting the carbon dioxide concentration in the experimental environment. The control quantity signal conversion model is:

[0015] (2);

[0016] Among them, in mathematical model (2), is the adjusted concentration control amount of carbon dioxide output by the control amount signal conversion model at time t, is the target pH value of the experimental environment, K is the solubility coefficient of carbon dioxide in water, is the carbon dioxide concentration in the experimental environment at time t.

[0017] Specifically, the improved child learning optimizer is used to adjust the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm. First, a mapping relationship is established between the improved child learning optimizer and the PID control algorithm. Specifically, the child individual position value of the improved child learning optimizer is mapped with the proportional coefficient, integral coefficient and differential coefficient of the PID control algorithm of the pH controller. The number of optimized coefficients is 3, so the problem dimension of the improved child learning optimizer is 3, denoted as problem dimension D=3. The child individual position of the improved child learning optimizer includes , , ;in Mapped to the i-th scale factor , Mapped to the i-th integral coefficient , Mapped to the i-th differential coefficient , the mathematical model is:

[0018] (3);

[0019] Among them, in mathematical model (3), is the position value of the j-dimensional attribute of the i-th child individual, is the position value of the first dimension attribute of the i-th child individual, is the position value of the second dimension attribute of the i-th child individual, is the position value of the third dimension attribute of the i-th child individual.

[0020] Specifically, the control parameter values ​​obtained by optimizing the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm by the improved child learning optimizer in this iteration process are input into the PID control algorithm and used in the pH controller of the fishery experiment, and the pH value of the experimental environment of the control effect of this iteration is recorded. The objective function is established by using the experimental environment pH value of the control effect of this iteration and the target pH value of the experimental environment. The objective function value is used as the fitness value of the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm of the improved child learning optimizer in this iteration. When the iteration reaches the maximum number of iterations, the proportional coefficient, integral coefficient and differential coefficient corresponding to the minimum fitness value are recorded as the optimal proportional coefficient, optimal integral coefficient and optimal differential coefficient. Among them, the mathematical model of the objective function J is:

[0021] (4);

[0022] Among them, in mathematical model (4), is the total adjustment time of the pH controller system, the value is 10, is the target pH value of the experimental environment, is the pH value of the experimental environment at time t, It is the adjusted concentration control amount of carbon dioxide output by the control amount signal conversion model at the tth moment.

[0023] Specifically, the child learning optimizer includes two parts in the process of optimizing the control coefficient of the PID control algorithm of the pH controller for fishery experiments: a global search strategy and a local development strategy. In this case, more possible high-quality solutions are obtained through global search, and the high-quality solutions are more accurate values ​​of the PID control coefficients. The local development finds the best value of the PID control coefficient within the range of the obtained high-quality solutions. In the optimization process of the standard child learning optimizer (KLO), the mathematical model of the global search stage updates the individual positions of the population by randomly generating new positions of the children of the population. The mathematical model is:

[0024] (5);

[0025] Among them, in mathematical model (5), Iter is the position value of the j-th dimension attribute of the ith child individual. Iter+1 is the position value of the j-dimensional attribute of the ith child individual, where r is a random number between 0 and 1. is the minimum value of the j-th dimension attribute.

[0026] Specifically, the mathematical model of the global search phase of the standard child learning optimizer (KLO) is improved, retaining the performance of the random search of mathematical model (5), while using the existing search information to guide the subsequent search direction, so that the search process of the child learning optimizer has directionality and goal-orientedness, improving the speed of the global search of the child learning optimizer, and avoiding KLO from falling into the local optimum, resulting in low precision of the high-quality solution. The improved mathematical model of the global search phase is:

[0027] (6);

[0028] In mathematical model (6), r is a random number between 0 and 1. is the maximum value of the j-th dimension attribute, is the minimum value of the j-th dimension attribute, is the maximum number of iterations, is the position value of the j-th dimension attribute of the current global optimal solution, is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the position value of the j-dimensional attribute of the ith child individual in the iter+1th iteration, || || is the Euclidean norm, and is calculated and The difference distance between .

[0029] Specifically, the improved mathematical model of the global search phase is The maximum search radius is the cosine function, which is used to dynamically adjust the search radius. As the number of iterations iter increases, the cosine function value changes periodically, making the search radius larger in the early stage of the iteration, which is convenient for extensive search; in the middle and late stages of the iteration, the radius gradually decreases, which is conducive to fine search and approaches the global optimal solution; the global optimal solution As the goal of the search process, calculate and the global optimal solution The difference distance between them can further fine-tune the search direction, enhance the flexibility of the child learning optimizer search, and improve the efficiency and accuracy of the child learning optimizer search.

[0030] Specifically, the Child Learning Optimizer (KLO) local development strategy simulates the learning behaviors of children in the neonatal stage, infant stage, toddler stage and preschool stage to establish a mathematical model of children's position update. The Child Learning Optimizer simulates the behaviors of different stages to adapt to the optimization of the pH controller in the fishery experiment, so that the PID control algorithm of the pH controller can maintain the control accuracy and sensitivity under different experimental conditions and experimental times; among them, the neonatal stage, infant stage and toddler stage are optimized by the mother interaction probability factor , learning probability factor and the non-family child interaction probability factor Select a different learning object location Update child individual location , the mathematical model of the individual position of children in the neonatal stage, infant stage, and toddler stage is:

[0031] (7);

[0032] Among them, in mathematical model (7), is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the position value of the j-th dimension attribute of the ith child individual in the iter+1th iteration, is a random value between 0 and 1. is a parameter oscillating between 0 and 2, is the location of the learning object, is the fitness function.

[0033] Specifically, the mathematical model of the individual position update of children in the preschool stage of the improved child learning optimizer (EKLO) local development strategy is determined by the learning passion factor LE of the individual child. The higher the learning passion factor, the better the current individual position of the child, and the smaller the corresponding fitness value. The mathematical model is:

[0034] (8);

[0035] Among them, in mathematical model (8), is the learning passion factor value of the i-th child, To maximize the learning passion factor, is the minimum value of the learning passion factor, The largest size for children.

[0036] Specifically, the learning passion factor LE is improved, and the improved mathematical model of the learning passion factor is:

[0037] (9);

[0038] Among them, in mathematical model (9), is the updated and improved learning passion factor value of the i-th child individual, is the improved learning passion factor value of the i-th child individual, is the average improved learning passion factor value, To improve the maximum value of learning passion factor, is the minimum value of the improved learning passion factor; is the competitiveness index of the i-th child individual, is the discreteness index value of the i-th child individual, is the weight index value.

[0039] Specifically, the specific method for improving the learning passion factor LE is:

[0040] S11. Calculate the average improved learning passion factor of N children in the improved children's learning optimizer , guiding the children by averaging the improved learning passion factor, if the L of the i-th child is lower than , then the L of the i-th child individual is improved, so that the corresponding child individual position is improved to the learning passion factor of the population average to get closer;

[0041] S12. Introducing competitiveness index and dispersion index Improve the learning passion factor, including the competitiveness index By ranking the fitness values ​​of each child's individual position in descending order, and by standardizing the fitness ranking, the value range is controlled to be within [0, 1], forming a fusion competitiveness index , the mathematical model is:

[0042] (10);

[0043] Among them, in mathematical model (10), is the competitiveness index of the ith child individual, N is the maximum size of the child individual, is the ranking of the individual position of the i-th child;

[0044] Among them, the dispersion index A mathematical model is established by mapping the standardized difference between the fitness value of the child's individual position and the average fitness value of N child's individual positions to the range of [0, 1];

[0045] (11);

[0046] Among them, in mathematical model (11), is the discreteness index value of the i-th child individual, is the fitness value of the i-th child individual position, is the average fitness value of N child individual positions, The standard deviation of the individual positions of N children, is the individual position of the i-th child, i=1,2,3,...,N.

[0047] Specifically, the improved learning passion factor value after the i-th child individual update is used The mathematical model for updating the individual position of children in the preschool stage of the local development strategy of the EKLO algorithm is improved. The individual position of children is updated according to the changing trend of the improved learning passion factor. The mathematical model is:

[0048] (12);

[0049] Among them, in mathematical model (12), is the position value of the j-th dimension attribute of the current global optimal solution, is the position value of the j-th dimension attribute of the global optimal solution of the iter-th iteration, is the teacher factor, which is a random number between 0 and 2. is the average value of the individual positions of N children in the iter-th iteration, The updated and improved learning passion factor value for the i-th child.

[0050] Specifically, the child learning optimizer is reconstructed using the improved global search strategy and the improved local development strategy of the children's individual position update mathematical model to obtain the improved child learning optimizer. The improved child learning optimizer is used to adjust the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm. The specific method is as follows:

[0051] S200, initializing the operating parameters of the improved child learning optimizer, including: the maximum number of iterations Tmax, the maximum size of individual children N, the problem dimension D, the maximum and minimum values ​​of the improved learning passion factor, and the upper bound ub and lower bound lb of the individual child position;

[0052] S201, initializing the individual position of each child of the improved child learning optimizer using the mathematical model (13);

[0053] (13);

[0054] In the formula, is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the maximum value of the j-th dimension attribute, is the minimum value of the j-th dimension attribute, is a uniform random number between 0 and 1; The largest size for individual children;

[0055] S202, encoding the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm into a space vector , using mathematical model (3) to establish a mapping relationship between the improved child learning optimizer and the traditional PID control algorithm;

[0056] S203, calculating the fitness values ​​of N child individual positions, arranging the fitness values ​​in reverse order and recording the minimum fitness value at the current iteration number and the child individual position corresponding to the minimum fitness value;

[0057] S204, the current number of iterations executes iter+1. If the iter value is greater than the maximum number of iterations Tmax, the individual child position corresponding to the current minimum fitness value is parsed into the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm, which is recorded as the optimal proportional coefficient , the optimal integration coefficient , the optimal differential coefficient ; Otherwise, execute S205;

[0058] S205. Update the individual child position using the improved global search strategy and the improved local development strategy of the mathematical model for updating the individual child position, limit the updated individual child position value to the range of [lb, ub], and return to S203.

[0059] Specifically, the optimal proportionality coefficient , the optimal integration coefficient , the optimal differential coefficient Used to optimize the PID control algorithm. The optimized PID control algorithm is used in the pH controller system. The control quantity signal conversion model outputs the carbon dioxide concentration control quantity. By controlling the CO2 valve, the main body of the CO2 valve is a brushless DC motor, and the CO2 concentration is adjusted to control the amount of CO2. The release and absorption speed of carbon dioxide is controlled by controlling the forward and reverse speeds of the brushless DC motor. The greater the forward speed of the brushless DC motor, the faster the release speed of carbon dioxide, and vice versa; the greater the reverse speed, the faster the absorption speed of carbon dioxide, and vice versa; the pH value of the fishery experimental environment is controlled by controlling the amount of carbon dioxide dissolved in water in a very short time; the mathematical model of the optimized PID control algorithm is:

[0060] (14);

[0061] Among them, in mathematical model (14), is the optimal proportionality factor, is the optimal integration coefficient, is the optimal differential coefficient, is the pH difference of the experimental environment, is the pH control signal value at the tth moment, Total adjustment time for the pH controller system.

[0062] The beneficial effects of the present invention are as follows: first, the improved child learning optimizer (EKLO) is used to optimize the proportional, integral and differential coefficients of the PID control algorithm of the pH controller of the fishery experiment, so that the tracking ability of the pH controller to the target pH value is enhanced, the overshoot phenomenon is effectively reduced, and the pH controller converges to the target value quickly, thereby improving the control accuracy and response speed; second, the improved EKLO is optimized in the global search and local development strategies, so that the pH controller can adapt to the pH fluctuations of the water body under different experimental conditions, and ensure that a stable pH value is maintained in a complex fishery experimental environment; third, the carbon dioxide concentration in the experimental environment is controlled by the pH controller, so that the pH control is more sensitive, the pH value is accurately regulated, the suitable environment for fish growth is guaranteed, and the success rate of the experiment is improved. Compared with the traditional PID control, the PID parameter setting after the EKLO optimization makes the control more efficient, reduces the frequency of use of the regulator, thereby reducing the experimental cost and realizing resource conservation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 Flow chart of the method for optimizing pH control in fishery experiments;

[0064] Figure 2 Specific steps for finding the best proportional coefficient, integral coefficient and differential coefficient of PID;

[0065] Figure 3 The concentration control curve for carbon dioxide adjustment at a target pH of 7.6;

[0066] Figure 4 It is a comparison curve of the fitness change of the standard child learning optimizer and the improved child learning optimizer;

[0067] Figure 5 A graph of the optimal proportional, integral, and differential coefficient values ​​for the improved child learning optimizer;

[0068] Figure 6 This is a comparison chart of the pH control effects of the standard pH controller, KLO-pH controller and EKLO-pH controller on fishery experiments. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0070] See also Figure 1-Figure 5The present invention provides a technical solution, including theoretical modeling and Matlab programming of a fishery experiment pH control optimization method, wherein the theoretical modeling is specifically implemented as follows Figure 1-Figure 2 As shown, it includes S1 to S4.

[0071] S1. Set the target pH value of the fishery experiment environment, and input the real-time pH value into the pH controller of the fishery experiment. The pH controller includes a pH error calculation model, a PID control algorithm and a control quantity signal conversion model of the fishery experiment environment.

[0072] Furthermore, the pH controller controls the carbon dioxide solubility in the experimental environment to adjust the water quality pH required for the experiment within the set range. The core control model of the pH controller is the PID control algorithm. The optimized PID control algorithm is obtained by optimizing the proportional coefficient, integral coefficient and differential coefficient of the PID control algorithm, so that the control parameters of the control algorithm of the pH controller in the fishery experiment are more accurate, and the pH control accuracy and sensitivity of the pH controller are improved; the mathematical model of the PID control algorithm is:

[0073] (1);

[0074] In the mathematical model (1), is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the pH difference of the experimental environment, is the pH control signal value at the tth moment, Total adjustment time for the pH controller system.

[0075] S2. Use the improved child learning optimizer to adjust the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm, and get the best proportional coefficient, integral coefficient and differential coefficient to input into the traditional PID control algorithm model, and get the optimized PID control algorithm for the pH controller of the fishery experiment, and get the enhanced pH controller.

[0076] Furthermore, firstly, a mapping relationship is established between the improved child learning optimizer and the PID control algorithm, specifically, the child individual position value of the improved child learning optimizer is mapped with the proportional coefficient, integral coefficient and differential coefficient of the PID control algorithm of the pH controller. The number of coefficients to be optimized is 3, so the problem dimension of the improved child learning optimizer is 3, which is recorded as problem dimension D=3. Then the child individual position of the improved child learning optimizer includes , , ;in Mapped to the i-th scale factor , Mapped to the i-th integral coefficient , Mapped to the i-th differential coefficient , the mathematical model is:

[0077] (3);

[0078] Among them, in mathematical model (3), is the position value of the j-dimensional attribute of the i-th child individual, is the position value of the first dimension attribute of the i-th child individual, is the position value of the second dimension attribute of the i-th child individual, is the position value of the third dimension attribute of the i-th child individual.

[0079] Furthermore, the control parameter values ​​obtained by optimizing the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm by the improved child learning optimizer in this iteration are input into the PID control algorithm and used in the pH controller of the fishery experiment, and the pH value of the experimental environment of the control effect of this iteration is recorded. The objective function is established by using the pH value of the experimental environment of the control effect of this iteration and the target pH value of the experimental environment. The objective function value is used as the fitness value of the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm of the improved child learning optimizer in this iteration. When the iteration reaches the maximum number of iterations, the proportional coefficient, integral coefficient and differential coefficient corresponding to the minimum fitness value are recorded as the optimal proportional coefficient, optimal integral coefficient and optimal differential coefficient. Among them, the mathematical model of the objective function J is:

[0080] (4);

[0081] Among them, in mathematical model (4), is the total adjustment time of the pH controller system, the value is 10, is the target pH value of the experimental environment, is the pH value of the experimental environment at time t, It is the adjusted concentration control amount of carbon dioxide output by the control amount signal conversion model at the tth moment.

[0082] Furthermore, the mathematical model of the global search phase of the standard child learning optimizer (KLO) is improved, retaining the performance of the random search of mathematical model (5), while using the existing search information to guide the subsequent search direction, so that the search process of the child learning optimizer has directionality and goal-orientedness, improving the speed of the global search of the child learning optimizer, and avoiding KLO from falling into the local optimum, resulting in low precision of the high-quality solution. The improved mathematical model of the global search phase is:

[0083] (6);

[0084] In mathematical model (6), r is a random number between 0 and 1. is the maximum value of the j-th dimension attribute, is the minimum value of the j-th dimension attribute, is the maximum number of iterations, is the position value of the j-th dimension attribute of the current global optimal solution, is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the position value of the j-dimensional attribute of the ith child individual in the iter+1th iteration, || || is the Euclidean norm, and is calculated and The difference distance between .

[0085] Furthermore, the improved child learning optimizer (EKLO) local development strategy simulates the learning behaviors of children in the neonatal stage, infant stage, toddler stage and preschool stage to establish a mathematical model of children's position update. The improved child learning optimizer simulates the behaviors of different stages to adapt to the optimization of the pH controller in the fishery experiment, so that the PID control algorithm of the pH controller can maintain the control accuracy and sensitivity under different experimental conditions and experimental times; among them, the neonatal stage, infant stage and toddler stage are optimized by the mother interaction probability factor , learning probability factor and the non-family child interaction probability factor Select a different learning object location Update child individual location , the mathematical model of the individual position of children in the neonatal stage, infant stage, and toddler stage is:

[0086] (7);

[0087] Among them, in mathematical model (7), is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the position value of the j-th dimension attribute of the ith child individual in the iter+1th iteration, is a random value between 0 and 1. is a parameter oscillating between 0 and 2, is the location of the learning object, is the fitness function.

[0088] Furthermore, the mother interaction probability factor and learning probability factor and the non-family child interaction probability factor Designed as a mathematical model with linear decrease from 1 to 0; in the neonatal stage, if the random number Less than , then the learning object position is the position value of the j-th dimension attribute of the global suboptimal solution up to the current point , otherwise, learn the object position is the position value of the j-th dimension attribute of the current global optimal solution ; In infancy, if the random number Less than , then the learning object position Random for the iterth iteration Position value of the j-th dimension attribute of the child individual , otherwise learn the object position Random for the iterth iteration Position value of the j-th dimension attribute of the child individual , in the toddler stage, if the random number Less than the probability factor of interaction with non-family children , then the learning object position Random for the iterth iteration Position value of the j-th dimension attribute of the child individual , otherwise learn the object position is the position value of the j-th dimension attribute of the global third best solution up to the current point .

[0089] Furthermore, the learning passion factor LE is improved, and the improved mathematical model of the learning passion factor is:

[0090] (9);

[0091] Among them, in mathematical model (9), is the updated and improved learning passion factor value of the i-th child individual, is the improved learning passion factor value of the i-th child individual, is the average improved learning passion factor value, To improve the maximum value of learning passion factor, is the minimum value of the improved learning passion factor; is the competitiveness index of the i-th child individual, is the discreteness index value of the i-th child individual, is the weight index value.

[0092] Furthermore, the specific method for improving the learning passion factor LE is as follows:

[0093] S11. Calculate the average improved learning passion factor of N children in the improved children's learning optimizer , guiding the children by averaging the improved learning passion factor, if the L of the i-th child is lower than , then the L of the i-th child individual is improved, so that the corresponding child individual position is improved to the learning passion factor of the population average to move closer;

[0094] S12. Introducing competitiveness index and dispersion index Improve the learning passion factor, including the competitiveness index By ranking the fitness values ​​of each child's individual position in descending order, and by standardizing the fitness ranking, the value range is controlled to be within [0, 1], forming a fusion competitiveness index , the mathematical model is:

[0095] (10);

[0096] Among them, in mathematical model (10), is the competitiveness index of the ith child individual, N is the maximum size of the child individual, is the ranking of the individual position of the i-th child;

[0097] Among them, the dispersion index A mathematical model is established by mapping the standardized difference between the fitness value of the child's individual position and the average fitness value of N child's individual positions to the range of [0, 1];

[0098] (11);

[0099] Among them, in mathematical model (11), is the discreteness index value of the i-th child individual, is the fitness value of the i-th child individual position, is the average fitness value of N child individual positions, The standard deviation of the individual positions of N children, is the individual position of the i-th child, i=1,2,3,...,N.

[0100] Furthermore, the improved learning passion factor value after the i-th child individual update is used The mathematical model for updating the individual position of children in the preschool stage of the local development strategy of the EKLO algorithm is improved. The individual position of children is updated according to the changing trend of the improved learning passion factor. The mathematical model is:

[0101] (12);

[0102] Among them, in mathematical model (12), is the position value of the j-th dimension attribute of the current global optimal solution, is the position value of the j-th dimension attribute of the global optimal solution of the iter-th iteration, is the teacher factor, which is a random number between 0 and 2. is the average value of the individual positions of N children in the iter-th iteration, The updated and improved learning passion factor value for the i-th child.

[0103] Furthermore, if Figure 2 As shown in the figure, the child learning optimizer is reconstructed by using the improved global search strategy and the improved local development strategy of the children's individual position update mathematical model to obtain the improved child learning optimizer. The improved child learning optimizer is used to adjust the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm. The specific method is as follows:

[0104] S200, initializing the operating parameters of the improved child learning optimizer, including: the maximum number of iterations Tmax, the maximum size of individual children N, the problem dimension D, the maximum and minimum values ​​of the improved learning passion factor, and the upper bound ub and lower bound lb of the individual child position;

[0105] S201, initializing the individual position of each child of the improved child learning optimizer using the mathematical model (13);

[0106] (13);

[0107] In the formula, is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the maximum value of the j-th dimension attribute, is the minimum value of the j-th dimension attribute, is a uniform random number between 0 and 1; The largest size for individual children;

[0108] S202, encoding the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm into a space vector , using mathematical model (3) to establish a mapping relationship between the improved child learning optimizer and the traditional PID control algorithm;

[0109] S203, calculating the fitness values ​​of N child individual positions, arranging the fitness values ​​in reverse order and recording the minimum fitness value at the current iteration number and the child individual position corresponding to the minimum fitness value;

[0110] S204, the current number of iterations executes iter+1. If the iter value is greater than the maximum number of iterations Tmax, the individual child position corresponding to the current minimum fitness value is parsed into the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm, which is recorded as the optimal proportional coefficient , the optimal integration coefficient , the optimal differential coefficient ; Otherwise, execute S205;

[0111] S205. Update the individual child position using the improved global search strategy and the improved local development strategy of the mathematical model for updating the individual child position, limit the updated individual child position value to the range of [lb, ub], and return to S203.

[0112] Furthermore, the optimal proportionality factor , the optimal integration coefficient , the optimal differential coefficient Used to optimize the PID control algorithm. The optimized PID control algorithm is used in the pH controller system. The control quantity signal conversion model outputs the carbon dioxide concentration control quantity. By controlling the CO2 valve, the main body of the CO2 valve is a brushless DC motor, and the CO2 concentration is adjusted to control the amount of CO2. The release and absorption speed of carbon dioxide is controlled by controlling the forward and reverse speeds of the brushless DC motor. The greater the forward speed of the brushless DC motor, the faster the release speed of carbon dioxide, and vice versa; the greater the reverse speed, the faster the absorption speed of carbon dioxide, and vice versa; the pH value of the fishery experimental environment is controlled by controlling the amount of carbon dioxide dissolved in water in a very short time; the mathematical model of the optimized PID control algorithm is:

[0113] (14);

[0114] Among them, in mathematical model (14), is the optimal proportionality factor, is the optimal integration coefficient, is the optimal differential coefficient, is the pH difference of the experimental environment, is the pH control signal value at the tth moment, Total adjustment time for the pH controller system.

[0115] S3. The pH error calculation model of the fishery experimental environment calculates the pH difference between the real-time pH value and the target pH value of the experimental environment. The pH difference of the experimental environment is input into the enhanced pH controller, and the pH control signal u(t) is output.

[0116] S4, the pH control quantity signal outputs the adjusted concentration control quantity of carbon dioxide through the control quantity signal conversion model, adjusts the carbon dioxide concentration of the experimental environment, returns to execute S1-S4 to continuously monitor the real-time pH value, adjusts the pH control quantity signal u(t), and reaches the target pH value of the fishery experiment.

[0117] Furthermore, the control quantity signal conversion model is used to convert the pH control quantity signal u(t) output by the optimized PID control algorithm of the pH controller of the fishery experiment into the adjusted concentration control quantity of carbon dioxide, and adjust the pH value in the experimental environment by adjusting the carbon dioxide concentration in the experimental environment. The control quantity signal conversion model is:

[0118] (2);

[0119] Among them, in mathematical model (2), is the adjusted concentration control amount of carbon dioxide output by the control amount signal conversion model at time t, is the target pH value of the experimental environment, K is the solubility coefficient of carbon dioxide in water, is the carbon dioxide concentration in the experimental environment at time t.

[0120] Furthermore, the traditional PID control algorithm, the PID control algorithm optimized by the standard child learning optimizer and the PID control algorithm optimized by the improved child learning optimizer are introduced for the pH controller of the fishery experiment. In Matlab, the codes of the traditional PID control algorithm, the PID control algorithm optimized by the standard child learning optimizer and the PID control algorithm optimized by the improved child learning optimizer are completed. The maximum number of iterations of the standard child learning optimizer (KLO) and the improved child learning optimizer (EKLO) are set to Tmax=80, the maximum number of child individuals N=70, the problem dimension D=3, the maximum value of the improved learning passion factor is 2 and the minimum value is 1, the upper bound ub=100 and the lower bound lb=0 of the child individual position; the control coefficient of the PID control algorithm is optimized according to S200-S205, and the fitness value of each child individual is calculated by the objective function of formula (4), as follows: Figure 4 As shown in the figure, the fitness value of EKLO drops rapidly at the beginning, indicating that a better solution is found quickly in the early iterations, and then the fitness value tends to stabilize and finally remains at around 10; the fitness value of KLO decreases more slowly and finally stabilizes and remains at around 16. This shows that its efficiency and effect in the optimization process are not as good as EKLO; the improved child learning optimizer (EKLO) is significantly better than the standard child learning optimizer (KLO) in the optimization of the control coefficients of this PID control algorithm. It finds a better solution faster and has a lower fitness value, indicating that its solution is more accurate.

[0121] Furthermore, the optimal proportional coefficient, optimal integral coefficient and optimal differential coefficient values ​​obtained by the improved child learning optimizer are as follows: Figure 5 As shown, after finally becoming stable, the optimal proportional coefficient The optimal integral coefficient is 6.84081. is 6.36506, the best differential coefficient It is 0.74611.

[0122] Furthermore, in MTALAB, the target pH value of the experimental environment is set to 7.6, and the solubility coefficient K of carbon dioxide in water is set to 0.1; according to the change of the pH control signal u(t), the relationship between the target pH value of the experimental environment and the adjusted concentration control amount of carbon dioxide output by the control signal conversion model at the tth moment is obtained, such as Figure 3 As shown, when the target pH value is reached, the adjusted concentration of carbon dioxide is controlled to 7; the code is as follows:

[0123] %Parameter settings

[0124] K = 0.1; %Solubility coefficient of carbon dioxide in water

[0125] pH_target = 7.6; % Target pH value

[0126] pH_initial = 11; % Initial pH value

[0127] u_t_max = 2; Maximum value of %pH control signal u(t)

[0128] C_env_t = 0.5; % Ambient concentration of carbon dioxide in the experimental environment

[0129] %CO2 adjustment concentration control range

[0130] Delta_C_t = 0:0.1:10;

[0131] % Initialize pH value array

[0132] pH_values ​​= zeros(size(Delta_C_t));

[0133] % Calculate the quadratic change of pH value with Delta_C_t

[0134] for i = 1:length(Delta_C_t)

[0135] % Use quadratic equation to simulate pH changes

[0136] adjustment = Delta_C_t(i)^2;

[0137] pH_values(i) = pH_initial - adjustment * (pH_initial - pH_target) / max(Delta_C_t)^2;

[0138] if pH_values(i) <pH_target

[0139] pH_values(i) = pH_target;

[0140] end

[0141] end.

[0142] Furthermore, if Figure 6 As shown in the figure, when the target pH value is reached, the required carbon dioxide concentration control amount is 7, and the EKLO-pH controller shows a very fast response speed. In about 2 seconds, the pH value is quickly adjusted from an initial value close to 1 to an adjusted carbon dioxide concentration control amount close to the target, and remains extremely stable in the subsequent time. This shows that the EKLO algorithm not only has a fast response speed, but also can effectively suppress over-adjustment and oscillation, and keep the pH value within a very tight range; although the KLO-pH controller has a fast adjustment speed, it fluctuates significantly after reaching the target pH value, showing poor stability, affecting the overall performance of the system and the durability of the equipment; the traditional PID control algorithm has the slowest response speed, and it takes a long time to adjust the pH value to a higher stable state. In addition, the pH value has obvious excessive drops and rises during the adjustment process, indicating that the adjustment accuracy and efficiency are low.

Claims

1. A fishery experiment pH control optimization method, characterized in that: The specific steps include: S1, setting the target pH value of the fishery experiment environment, and inputting the real-time pH value into the pH controller of the fishery experiment, wherein the pH controller includes a pH error calculation model of the fishery experiment environment, a PID control algorithm, and a control quantity signal conversion model; S2. Using the improved children's learning optimizer to adjust the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm, the optimal proportional coefficient, integral coefficient and differential coefficient are obtained and input into the traditional PID control algorithm model, and the optimized PID control algorithm is used for the pH controller of the fishery experiment, and the enhanced pH controller is obtained; the improved children's learning optimizer includes the improvement of the learning passion factor of the preschool stage of the global search strategy mathematical model of the standard children's learning optimizer and the local development strategy, wherein the improved learning passion factor value is used to improve the mathematical model of the preschool stage of the children's individual position update of the local development strategy; The specific steps for improving the mathematical model of the global search strategy of the standard child learning optimizer are: retaining the performance of the random search of the original global search strategy, while using the existing search information to guide the subsequent search direction. The improved mathematical model of the global search strategy is: （6）； In mathematical model (6), r is a random number between 0 and 1. is the maximum value of the j-th dimension attribute, is the minimum value of the j-th dimension attribute, is the maximum number of iterations, is the position value of the j-th dimension attribute of the current global optimal solution, is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the position value of the j-dimensional attribute of the ith child individual in the iter+1th iteration, || || is the Euclidean norm, and is calculated and The difference distance between The specific method for improving the learning passion factor is: S11. Calculate the average improved learning passion factor of N children in the improved children's learning optimizer , guiding the children by averaging the improved learning passion factor, if the i-th child Lower than , then improve the i-th child individual , which makes the corresponding individual child position improve towards the population average learning passion factor to move closer; S12. Introducing competitiveness index and dispersion index Improve the learning passion factor, including the competitiveness index By ranking the fitness values ​​of each child's individual position in descending order, and by standardizing the fitness ranking, the value range is controlled to be within [0, 1], forming a fusion competitiveness index , the mathematical model is: （10）； Among them, in mathematical model (10), is the competitiveness index of the ith child individual, N is the maximum size of the child individual, is the ranking of the individual position of the i-th child; Among them, the dispersion index A mathematical model is established by mapping the standardized difference between the fitness value of the child's individual position and the average fitness value of N child's individual positions to the range of [0, 1]; （11）； Among them, in mathematical model (11), is the discreteness index value of the i-th child individual, is the fitness value of the i-th child individual position, is the average fitness value of N child individual positions, The standard deviation of the individual positions of N children, is the individual position of the i-th child, i=1,2,3,...,N; S13. The improved mathematical model of learning passion factor is: （9）； Among them, in mathematical model (9), is the updated and improved learning passion factor value of the i-th child individual, is the improved learning passion factor value of the i-th child individual, is the average improved learning passion factor value, To improve the maximum value of learning passion factor, is the minimum value of the improved learning passion factor; is the competitiveness index of the i-th child individual, is the discreteness index value of the i-th child individual, is the weight index value; Among them, the improved mathematical model of individual position update of children in the preschool stage is: （12）； Among them, in mathematical model (12), is the position value of the j-th dimension attribute of the current global optimal solution, is the position value of the j-th dimension attribute of the global optimal solution of the iter-th iteration, is the teacher factor, which is a random number between 0 and 2. is the average value of the individual positions of N children in the iter-th iteration, The updated and improved learning passion factor value of the i-th child; S3, the pH error calculation model of the fishery experimental environment calculates the pH difference of the experimental environment between the real-time pH value and the target pH value, and the pH difference of the experimental environment is input into the enhanced pH controller, which outputs the pH control amount signal u(t); S4, the pH control signal outputs the adjusted concentration control amount of carbon dioxide through the control signal conversion model, adjusts the carbon dioxide concentration of the experimental environment, returns to execute S1-S4 to continuously monitor the real-time pH value, adjusts the pH control signal u(t), and reaches the target pH value of the fishery experiment.

2. A fishery experiment pH control optimization method according to claim 1, characterized in that: The control quantity signal conversion model is used to convert the pH control quantity signal u(t) output by the optimized PID control algorithm of the pH controller of the fishery experiment into the adjusted concentration control quantity of carbon dioxide, and adjust the pH value in the experimental environment by adjusting the carbon dioxide concentration in the experimental environment, wherein the control quantity signal conversion model is: （2）； Among them, in mathematical model (2), is the adjusted concentration control amount of carbon dioxide output by the control amount signal conversion model at time t, is the pH value of the experimental environment at the tth moment, and K is the solubility coefficient of carbon dioxide in water.

3. A fishery experiment pH control optimization method according to claim 2, characterized in that: The improved child learning optimizer is guided by the objective function in the process of adjusting the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm. The objective function includes: inputting the control parameter value obtained by optimizing the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm by the improved child learning optimizer in this iteration into the pH controller of the traditional PID control algorithm and used in the fishery experiment, recording the pH value of the experimental environment of the control effect of this iteration, and establishing the objective function by using the pH value of the experimental environment of the control effect of this iteration and the target pH value of the experimental environment; the value of the objective function is used as the fitness value of the improved child learning optimizer in this iteration for adjusting the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm, and the proportional coefficient, integral coefficient and differential coefficient corresponding to the minimum fitness value are the optimal control parameter values. The mathematical model of the objective function J is: （4）； Among them, in mathematical model (4), is the total adjustment time of the pH controller system, the value is 10, is the target pH value of the experimental environment, is the pH value of the experimental environment at time t, It is the adjusted concentration control amount of carbon dioxide output by the control amount signal conversion model at the tth moment.

4. A fishery experiment pH control optimization method according to claim 1, characterized in that: The improved child learning optimizer is used to adjust the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm. The specific method is as follows: S200, initializing the operating parameters of the improved child learning optimizer, including: the maximum number of iterations Tmax, the maximum size of individual children N, the problem dimension D, the maximum and minimum values ​​of the improved learning passion factor, and the upper bound ub and lower bound lb of the individual child position; S201, initializing the individual position of each child of the improved child learning optimizer using the mathematical model (13); （13）； In the formula, is the position value of the j-th dimension attribute of the i-th child individual in the iter-th iteration, is the maximum value of the j-th dimension attribute, is the minimum value of the j-th dimension attribute, is a uniform random number between 0 and 1; The largest size for children; S202, encoding the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm into a space vector ,use Establish a mapping relationship between the improved child learning optimizer and the traditional PID control algorithm; S203, calculating the fitness values ​​of N child individual positions, arranging the fitness values ​​in reverse order and recording the minimum fitness value at the current iteration number and the child individual position corresponding to the minimum fitness value; S204, the current number of iterations executes iter+1. If the iter value is greater than the maximum number of iterations Tmax, the individual child position corresponding to the current minimum fitness value is parsed into the proportional coefficient, integral coefficient and differential coefficient of the traditional PID control algorithm, which is recorded as the optimal proportional coefficient , the optimal integration coefficient , the optimal differential coefficient ; Otherwise, execute S205; S205, using the child individual position update mathematical model of the improved global search strategy and the improved local development strategy to update the child individual position, limiting the updated child individual position value within the range of [lb, ub], and returning to S203; Wherein, in said S202, is the position value of the j-dimensional attribute of the i-th child individual, is the position value of the first dimension attribute of the i-th child individual, is the position value of the second dimension attribute of the i-th child individual, is the position value of the third dimension attribute of the i-th child individual.

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