Temperature self-regulation control method for sipunculus nudus larva culture water body

Abstract

The invention discloses a sipunculus nudus larva culture water body temperature self-regulation control method, and relates to the technical field of temperature PID control, and the method comprises the steps: S1, setting a target temperature value of a temperature control system for sipunculus nudus culture; s2, calculating a temperature error e (k) between a real-time temperature value and a target temperature value of sipunculus nudus culture water, and inputting the temperature error e (k) into an enhanced PID controller; the enhanced PID is realized by setting a control parameter of a standard PID through an improved gooseneck barnacle optimization algorithm; s3, the enhanced PID controller outputs a real-time temperature value control variable u (k), the u (k) is used for controlling a temperature driver, the temperature driver adjusts the water temperature, the temperature sensor feeds back the real-time temperature value to the enhanced PID controller, and the step S2 is executed again; and S4, repeatedly executing the steps S2 to S4, adjusting the enhanced PID controller to output a real-time temperature value control variable u (k), and realizing the adaptive adjustment of the culture water temperature of the sipunculus nudus larvae.

Description

Technical Field

[0001] The invention belongs to the technical field of temperature PID control, and in particular relates to a method for self-regulating the temperature of water for cultivating naked siphon larvae. Background Art

[0002] Sipuncula larvae are a kind of aquatic organisms that are relatively sensitive to temperature. During the cultivation process of Sipuncula larvae, the stability of water temperature is one of the key factors to ensure the success of cultivation. Sipuncula larvae can maintain good physiological state and growth rate under suitable water temperature conditions. When the water temperature changes sharply, especially when it exceeds its tolerance range, the survival rate of Sipuncula larvae will drop significantly.

[0003] Traditional methods of controlling water temperature during the cultivation of naked Sipuncula larvae often rely on manual intervention, which is not only time-consuming and labor-intensive, but also difficult to achieve accuracy and timeliness. Currently available temperature control methods for naked Sipuncula cultivation mainly focus on the accuracy of temperature control, while ignoring the response speed of temperature control. Secondly, the growth process of naked Sipuncula often has nonlinear characteristics, that is, its growth rate and response to water temperature will be different at different growth stages or under different environmental conditions. Traditional PID controllers usually lack adaptive capabilities and have prominent time lag problems, making it difficult to quickly and automatically adjust their control strategies to adapt to these changes.

[0004] Gooseneck Barnacle Optimization Algorithm (GBO) is a new type of optimization algorithm. Inspired by the natural mating behavior of gooseneck barnacles, it simulates the static and dynamic mating behavior of gooseneck barnacles and establishes a mathematical model for algorithm optimization. In some cases, the GBO algorithm quickly converges to a local optimal solution rather than a global optimal solution. The standard gooseneck barnacle optimization algorithm has the problem of easily falling into local optimal solutions when optimizing multi-parameter PID control algorithms, and the problem of poor algorithm optimization accuracy due to the excessive concentration of candidate sets, which leads to insufficient accuracy of PID control parameters and affects the accuracy and response speed of self-regulating control of the culture temperature of naked star larvae. Summary of the invention

[0005] Based on the problems of water temperature control in the process of cultivating naked siphon larvae and the standard PID and gooseneck barnacle optimization algorithms in the background technology, the present invention dynamically adjusts the value of the adaptive factor v to generate a higher diversity gooseneck barnacle individual position sequence generated by Chebyshev mapping, so that the population is more widely distributed in the search space, and the risk of the algorithm falling into the local optimum is reduced. At the same time, the improved significant wave height Hs is introduced to increase the diversity and exploration ability of the gooseneck barnacle optimization algorithm group. The standard temperature PID controller is improved by using the improved gooseneck barnacle optimization algorithm (HGBO), and is used in the temperature control system of naked siphon larvae cultivation, so as to improve the temperature control accuracy and sensitivity of the temperature control system of naked siphon larvae cultivation.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a method for self-regulating and controlling the temperature of water for cultivating naked Sipunculus larvae, which comprises the following specific steps: S1, setting a target temperature value of a water temperature control system for cultivating naked Sipuncula larvae, the temperature control system comprising a temperature sensor, an enhanced PID controller and a temperature driver; S2, calculate the temperature error e(k) between the real-time temperature value of the water body for larvae culture and the target temperature value, and input the temperature error e(k) into the enhanced PID controller; the enhanced PID controller is realized by adjusting the control parameters Kp, Ki, Kd of the standard PID through the improved gooseneck barnacle optimization algorithm; S3, the enhanced PID controller outputs the real-time temperature value control variable △u(k), uses the real-time temperature value control variable △u(k) to control the temperature driver, the temperature driver adjusts the water temperature, the temperature sensor feeds back the detected real-time temperature value to the enhanced PID controller, and returns to execute S2; S4, repeatedly executing steps S2 to S4, adjusting the enhanced PID controller to output the real-time temperature value control variable △u(k), realizing adaptive regulation of the water temperature for culturing naked Sipuncula larvae, and achieving the target temperature value for culturing naked Sipuncula larvae.

[0007] Preferably, the core of the water temperature control system for culturing naked Sipuncula larvae is to control the water temperature for culturing naked Sipuncula larvae to reach the target temperature value for larval growth through the self-regulating ability of the enhanced PID, wherein the enhanced PID controller and the temperature driver are used to accurately control the temperature value, and the temperature sensor is used to collect the real-time water temperature value, wherein the dynamic response characteristics of the enhanced PID controller are represented by the transfer function, and the mathematical model of the enhanced PID controller is designed according to formula (1); (1); In formula (1), , , are the optimal proportional coefficient, optimal integral coefficient, and optimal differential coefficient of the enhanced PID, respectively; △e(k) is the difference between the temperature errors at the kth second and the k-1th second; △u(k) is the real-time temperature value control variable; △e(k-1) is the difference between the temperature errors at the k-1th second and the k-2th second; Among them, the temperature driver adjusts the water temperature of naked Sipunculus larvae according to the real-time temperature value control variable △u(k), and establishes the dynamic model of the temperature driver. The mathematical model is: (2); In formula (2), is the temperature of the water body in which the naked Sipunculus is cultured at the k+1th second, is the temperature of the water body in which the naked Sipuncula are cultured at the kth second, is the sampling time interval, Target temperature value for water culture of naked Sipuncula.

[0008] Preferably, the enhanced PID controller requires the standard PID controller to achieve optimal robust control. The standard PID controller needs to obtain the optimal Kp, Ki, and Kd parameter values. In the process of adjusting the control parameters Kp, Ki, and Kd of the standard PID using the improved gooseneck barnacle optimization algorithm, a real number mapping is established between the Kp, Ki, and Kd of the standard PID controller and the individual positions of the gooseneck barnacles. The process of updating the individual positions of the gooseneck barnacles is recorded as the process of optimizing the Kp, Ki, and Kd of the standard PID controller. The real number mapping mathematical model is: (3); In formula (3), is the current position of the i-th gooseneck barnacle individual, , , are the Kp, Ki, and Kd values ​​corresponding to the position of the i-th gooseneck barnacle individual; Among them, the improved gooseneck barnacle optimization algorithm (HGBO) updates the individual position of gooseneck barnacles during the iteration process. The update of the individual position of gooseneck barnacles is guided by the objective function. The input of the objective function is the individual position of gooseneck barnacles. The smaller the objective function, the better the individual position of gooseneck barnacles, that is, the more accurate the Kp, Ki, and Kd parameters are.

[0009] Preferably, by dynamically adjusting the value of the adaptive factor v, the higher diversity gooseneck barnacle individual position sequence generated by the Chebyshev map is made to distribute the population more widely in the search space, avoiding excessive concentration of the population, so as to enhance the global exploration ability of the gooseneck barnacle optimization algorithm and improve the convergence to the global optimal solution. The mathematical model of the adaptive factor v is: (4); In formula (4), is the adaptive factor value of the tth iteration, is the initial adaptive factor value, t is the current iteration number, is the maximum number of iterations, is the average Euclidean distance between gooseneck barnacle individuals in the current iteration, is the maximum average Euclidean distance threshold; represents the base of natural logarithms; The adaptive factor is used to improve the Chebyshev mapping strategy. The mathematical model of the improved Chebyshev mapping strategy is: (5); In formula (5), is the new improved Chebyshev map value, is the current improved Chebyshev map value.

[0010] Preferably, the gooseneck barnacle optimization algorithm is initialized using an improved Chebyshev mapping strategy, specifically including: initializing the position matrix of gooseneck barnacle species individuals using an improved Chebyshev mapping strategy, and the mathematical model is: (6); In formula (6), is the current position of the i-th gooseneck barnacle species individual, is the upper boundary of the search space, is the lower boundary of the search space, for the new improved Chebyshev map value.

[0011] Preferably, in the gooseneck barnacle optimization algorithm (GBO), the GBO algorithm can adjust the position of the gooseneck barnacles in each iteration through the significant wave height. The role of the significant wave height Hs is to simulate the influence of waves in the actual ocean environment on the position update of the gooseneck barnacles. The appropriate significant wave height Hs can make the gooseneck barnacle optimization algorithm more adaptive and have better optimization accuracy. The accuracy in the optimization process of the Kp, Ki, and Kd control parameters of the standard PID controller is higher, and the control accuracy and sensitivity of the PID controller of the water temperature control system for the cultivation of naked starfish larvae to the larval cultivation water temperature are further improved.

[0012] Preferably, the significant wave height of the gooseneck barnacle optimization algorithm is improved. The improved significant wave height Hs formula improves the performance of Hs in balancing global exploration and local development in the GBO algorithm by designing a piecewise function and combining the fitness function value, thereby avoiding the GBO algorithm from falling into the local optimum. The mathematical model of the significant wave height Hs of the improved gooseneck barnacle optimization algorithm is: (7); In formula (7), is the improved significant wave height value of the tth iteration, is the improved maximum value of significant wave height, is the improved minimum significant wave height, is the maximum number of iterations, t is the current number of iterations, , and are the average fitness value, minimum fitness value and maximum fitness value of the current population in the tth iteration, and r is a random number between 0 and 1.

[0013] Preferably, the improved significant wave height from Nonlinear decrease, considering the influence of the time square term, the specific decrease is Absolutely, the descending mode simulates the process in which the improved significant wave height decreases rapidly in the initial stage; secondly, the fitness performance of the gooseneck barnacle individuals in the optimization process is considered, simulating the process of continuous adjustment and optimization of the gooseneck barnacle individuals in the search space. The term realizes the gradual decrease of wave height and simulates the gradual convergence of the optimization process.

[0014] Preferably, the improved significant wave height The first part adopts the form of quadratic function, which can simulate the rapid decrease of significant wave height in the initial stage, increasing the exploration ability of the algorithm in the initial stage. The second part combines fitness evaluation, which can dynamically adjust the wave height, increasing the adaptive ability and fine search ability of the GBO algorithm.

[0015] Preferably, the enhanced PID controller of the water temperature control system for culturing naked siphon larvae is implemented by adjusting the control parameters Kp, Ki, and Kd through an improved gooseneck barnacle optimization algorithm, wherein the Kp, Ki, and Kd parameters of the PID controller are encoded as three-dimensional real vectors, and a real number mapping is established between the real vectors and the individual positions of gooseneck barnacles; the optimal individual positions of gooseneck barnacles, i.e., the optimal control parameters of the PID controller, are found in the following specific steps: Step 1: Set the size of the gooseneck barnacle population N and the maximum number of iterations T max , and the dimension d of each gooseneck barnacle species, where the dimension d corresponds to the three parameters of the PID controller: the proportional coefficient Kp , integral coefficient Ki and the differential coefficient Kd ; Step 2: Use the improved Chebyshev mapping strategy to initialize the position of each individual in the gooseneck barnacle population and set the upper and lower boundaries of the search space. and , randomly initialize the position of each individual, where the position matrix is ​​the current position of the i-th gooseneck barnacle species individual ; Step 3. Calculate the tth iteration through the objective function of the temperature control system model under different PID control parameters The fitness value of the minimum fitness value is stored. value; Step 4: Determine whether the current number of iterations t is equal to the maximum number of iterations T max If yes, then the individual position of the gooseneck barnacle corresponding to the minimum fitness value is output and parsed into the Kp, Ki, and Kd control parameters of the PID controller; otherwise, return to execute step 5; Step 5. Establish an improved gooseneck barnacle optimization algorithm position update mathematical model and calculate the fitness value of the current individual position through the objective function. Find the individual position corresponding to the minimum fitness value in each iteration, and optimize the Kp, Ki, and Kd control parameters of the PID controller; the individual position corresponding to the minimum fitness value is the best gooseneck barnacle individual position; Step 6: Repeat the global exploration and local development steps until the current number of iterations t is equal to the maximum number of iterations T. max , output the position of the gooseneck barnacle individual with the smallest fitness {W}_{best}=\left [ {{Kp}_{best},{Ki}_{best},{Kd}_{best}} \right ] , which is interpreted as the optimal parameter combination value of the enhanced PID controller for the water temperature control system for the cultivation of naked Sipuncula larvae.

[0016] Preferably, the objective function Temperature regulation time of water temperature control system for culture of naked Sipuncula larvae And steady-state error design, the mathematical model is: (8); In formula (8), is the adjustment time weight of the temperature control system, and its value is 0.3. is the temperature steady-state error weight, which is 0.7. is the temperature control system at the kth second, is the target temperature value of the temperature control system.

[0017] Preferably, the specific steps of establishing the improved gooseneck barnacle optimization algorithm position update mathematical model are: P51. Calculate the significant wave height value after the tth iteration improvement according to the mathematical model formula (7) ; P52, simulated the movement of gooseneck barnacles in water and the distribution of sperm regions, and proposed an adjacent logarithmic spiral region , the mathematical model is: (9); In formula (9), is the position value of the i-th gooseneck barnacle individual in the t-th iteration, is the distance between the ith barnacle and the yth sperm region; and is a constant that defines the shape of the logarithmic spiral, r is a random number between 0 and 1, is the yth sperm region; P53, update the position of the new gooseneck barnacle in the search space, simulate the movement to the new individual, introduce a motion step, and establish an improved motion step mathematical model according to formula (10), which is defined as follows: (10); In formula (10), is the movement step length of the i-th gooseneck barnacle individual in the t+1th iteration, is the movement step length of the i-th gooseneck barnacle individual in the t-th iteration, is the ambient wind direction of the tth iteration, is the target dimension value; P54, if the sperm area If the number is 1, that is, the maximum value of y is 1, then execute formula (11), otherwise execute formula (12); P55. By simulating sperm throwing behavior, a new simulated updated position of a gooseneck barnacle individual is generated, wherein the updated mathematical model of the position is established according to the number of sperm areas: (11); (12); In formula (11) and formula (12), is the position value of the i-th gooseneck barnacle individual in the t+1th iteration, that is, the updated position value, is the position value of the i-th gooseneck barnacle individual in the t-th iteration, is the Levy flight function.

[0018] The beneficial effects of the present invention are as follows: by adopting an improved gooseneck barnacle optimization algorithm (HGBO) and an enhanced PID controller, the present invention significantly improves the adaptive control accuracy and sensitivity of the water temperature for culturing naked star worm larvae, and this method can more accurately adjust the water temperature to adapt to the temperature target values ​​of different growth stages; at the same time, by dynamically adjusting the adaptive factor and using Chebyshev mapping to generate a high-diversity gooseneck barnacle individual position sequence, the present invention reduces the risk of falling into local optimality during the process of optimizing the PID controller of the water temperature control system by the gooseneck barnacle optimization algorithm, and enhances the global exploration ability of the gooseneck barnacle optimization algorithm; in summary, the present invention improves the response speed of the water temperature control system through a more accurate naked star worm larvae culture water temperature control method, so that the naked star worm larvae culture water can adapt to environmental changes and the temperature requirements of naked star worm larvae at different stages more quickly, thereby improving the survival rate of naked star worm larvae culture. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the temperature adaptive control method during the culture of naked Sipuncula larvae; Figure 2 The change diagram of the fitness value of the individual position of gooseneck barnacles in the process of optimizing the improved gooseneck barnacle optimization algorithm and the standard gooseneck barnacle optimization algorithm; Figure 3 The change diagram of PID control parameters in the optimization process of the improved gooseneck barnacle optimization algorithm and the standard gooseneck barnacle optimization algorithm; Figure 4 A diagram showing the effect of water temperature control for cultivating naked Sipuncula larvae. DETAILED DESCRIPTION

[0020] 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.

[0021] The present invention provides a technical solution: a method for self-regulating the temperature of water for cultivating naked Sipuncula larvae. The technical solution is implemented in steps S1 to S4. Figure 1 shown.

[0022] S1. Setting a target temperature value of a water temperature control system for cultivating naked Sipuncula larvae, wherein the temperature control system comprises a temperature sensor, an enhanced PID controller and a temperature driver.

[0023] Furthermore, the enhanced PID controller and the temperature driver are used to accurately control the temperature value, and the temperature sensor is used to collect the real-time water temperature value, wherein the dynamic response characteristics of the enhanced PID controller are represented by the transfer function, and the mathematical model of the enhanced PID controller is designed according to formula (1); (1); In formula (1), , , They are the optimal proportional coefficient, optimal integral coefficient, and optimal differential coefficient of the enhanced PID, respectively; △e(k) is the difference in temperature error between the current moment and the previous moment; △u(k) is the real-time temperature value control variable.

[0024] Furthermore, the temperature driver adjusts the water temperature of the naked Sipunculus larvae according to the real-time temperature value control variable △u(k), and establishes a dynamic model of the temperature driver. The mathematical model is: (2); In formula (2), is the temperature of the water body in which the naked Sipunculus is cultured at the k+1th second, is the temperature of the water body in which the naked Sipuncula are cultured at the kth second, is the sampling time interval, Target temperature value for water culture of naked Sipuncula.

[0025] S2. Calculate the temperature error e(k) between the real-time temperature value of the water body where the larvae are cultured and the target temperature value. The temperature error e(k) is input into the enhanced PID controller. The enhanced PID controller is implemented by adjusting the control parameters Kp, Ki, and Kd of the standard PID through the improved gooseneck barnacle optimization algorithm.

[0026] Furthermore, using the formula The temperature error e(k) between the temperature value of the water body for culturing naked Sipuncula at the kth second and the temperature value of the water body for culturing naked Sipuncula at the k-1th second is calculated, and the temperature error e(k) of the water body is input into the enhanced PID controller. At the same time, the difference △e(k) between the temperature error at the current moment and the previous moment and the difference △e(k-1) between the temperature error at the previous moment and the temperature error at the previous moment are calculated. The real-time temperature value control variable △u(k) is calculated using formula (1).

[0027] Furthermore, a real number mapping is established between the Kp, Ki, and Kd of the standard PID controller and the individual positions of the gooseneck barnacles. The process of updating the individual positions of the gooseneck barnacles is recorded as the process of optimizing the Kp, Ki, and Kd of the standard PID controller. The mathematical model of the real number mapping is: (3); In formula (3), is the current position of the i-th gooseneck barnacle individual, , , are the Kp, Ki, and Kd values ​​corresponding to the individual position of the i-th gooseneck barnacle.

[0028] Furthermore, the improved gooseneck barnacle optimization algorithm (HGBO) updates the individual positions of gooseneck barnacles in the iterative process. The update of the individual positions of gooseneck barnacles is guided by the objective function. The input of the objective function is the individual positions of gooseneck barnacles. The smaller the objective function, the better the individual positions of gooseneck barnacles, that is, the more accurate the Kp, Ki, and Kd parameters are.

[0029] Furthermore, by dynamically adjusting the value of the adaptive factor v, the higher diversity of the individual position sequence of gooseneck barnacles generated by the Chebyshev map can make the population more widely distributed in the search space, avoiding excessive concentration of the population, so as to enhance the global exploration ability of the gooseneck barnacle optimization algorithm and improve the convergence to the global optimal solution. The mathematical model of the adaptive factor v is: (4); In formula (4), is the adaptive factor value of the tth iteration, is the initial adaptive factor value, t is the current iteration number, is the maximum number of iterations, is the average Euclidean distance between gooseneck barnacle individuals in the current iteration, is the maximum average Euclidean distance threshold; represents the base of natural logarithms; The adaptive factor is used to improve the Chebyshev mapping strategy. The mathematical model of the improved Chebyshev mapping strategy is: (5); In formula (5), is the new improved Chebyshev map value, is the current improved Chebyshev map value.

[0030] Furthermore, the gooseneck barnacle optimization algorithm is initialized using the improved Chebyshev mapping strategy, which specifically includes: initializing the position matrix of the gooseneck barnacle species using the improved Chebyshev mapping strategy, and the mathematical model is: (6); In formula (6), is the current position of the i-th gooseneck barnacle species individual, is the upper boundary of the search space, is the lower boundary of the search space, for the new improved Chebyshev map value.

[0031] Furthermore, the significant wave height of the gooseneck barnacle optimization algorithm is improved. The improved significant wave height Hs formula improves the performance of Hs in balancing global exploration and local development in the GBO algorithm through the design of piecewise functions and combines the fitness function value, thus avoiding the GBO algorithm from falling into the local optimum. The mathematical model of the significant wave height Hs of the improved gooseneck barnacle optimization algorithm is: (7); In formula (7), is the improved significant wave height value of the tth iteration, is the improved maximum value of significant wave height, is the improved minimum significant wave height, is the maximum number of iterations, t is the current number of iterations, , and are the average fitness value, minimum fitness value and maximum fitness value of the current population in the tth iteration, and r is a random number between 0 and 1.

[0032] Furthermore, the enhanced PID controller of the water temperature control system for the cultivation of naked siphonophore larvae is realized by adjusting the control parameters Kp, Ki, and Kd through the improved gooseneck barnacle optimization algorithm, wherein the Kp, Ki, and Kd parameters of the PID controller are encoded as three-dimensional real vectors, and a real number mapping is established between the real vector and the individual position of the gooseneck barnacle; the optimal individual position of the gooseneck barnacle, that is, the optimal control parameter of the PID controller, is found, and the specific steps are as follows: Step 1: Set the size of the gooseneck barnacle population N and the maximum number of iterations T max , and the dimension d of each gooseneck barnacle species, where the dimension d corresponds to the three parameters of the PID controller: the proportional coefficient Kp , integral coefficient Ki and the differential coefficient Kd ; Step 2: Use the improved Chebyshev mapping strategy to initialize the position of each individual in the gooseneck barnacle population and set the upper and lower boundaries of the search space. and , randomly initialize the position of each individual, where the position matrix is ​​the current position of the i-th gooseneck barnacle species individual ; Step 3. Calculate the tth iteration through the objective function of the temperature control system model under different PID control parameters The fitness value of the minimum fitness value is stored. value; Step 4: Determine whether the current number of iterations t is equal to the maximum number of iterations T max If yes, then the individual position of the gooseneck barnacle corresponding to the minimum fitness value is output and parsed into the Kp, Ki, and Kd control parameters of the PID controller; otherwise, return to execute step 5; Step 5. Establish an improved gooseneck barnacle optimization algorithm position update mathematical model and calculate the fitness value of the current individual position through the objective function. Find the individual position corresponding to the minimum fitness value in each iteration, and optimize the Kp, Ki, and Kd control parameters of the PID controller; the individual position corresponding to the minimum fitness value is the best gooseneck barnacle individual position; Step 6: Repeat the global exploration and local development steps until the current number of iterations t is equal to the maximum number of iterations T. max , output the position of the gooseneck barnacle individual with the smallest fitness {W}_{best}=\left [ {{Kp}_{best},{Ki}_{best},{Kd}_{best}} \right ] , which is interpreted as the optimal parameter combination value of the enhanced PID controller for the water temperature control system for the cultivation of naked Sipuncula larvae.

[0033] Furthermore, the objective function Temperature regulation time of water temperature control system for culture of naked Sipuncula larvae And steady-state error design, the mathematical model is: (8); In formula (8), is the adjustment time weight of the temperature control system, and its value is 0.3. is the temperature steady-state error weight, which is 0.7. is the temperature control system at the kth second, is the target temperature value of the temperature control system.

[0034] Furthermore, the specific steps of establishing the improved gooseneck barnacle optimization algorithm position update mathematical model are as follows: P51. Calculate the significant wave height value after the tth iteration improvement according to the mathematical model formula (7) ; P52, simulated the movement of gooseneck barnacles in water and the distribution of sperm regions, and proposed an adjacent logarithmic spiral region , the mathematical model is: (9); In formula (9), is the position value of the i-th gooseneck barnacle individual in the t-th iteration, is the distance between the ith barnacle and the yth sperm region; and is a constant that defines the shape of the logarithmic spiral, r is a random number between 0 and 1, is the yth sperm region; P53, update the position of the new gooseneck barnacle in the search space, simulate the movement to the new individual, introduce a motion step, and establish an improved motion step mathematical model according to formula (10), which is defined as follows: (10); In formula (10), is the movement step length of the i-th gooseneck barnacle individual in the t+1th iteration, is the movement step length of the i-th gooseneck barnacle individual in the t-th iteration, is the ambient wind direction value of the tth iteration, is the target dimension value; P54, if the sperm area If the number is 1, that is, the maximum value of y is 1, then execute formula (11), otherwise execute formula (12); P55. By simulating sperm throwing behavior, a new simulated updated position of a gooseneck barnacle individual is generated, wherein the updated mathematical model of the position is established according to the number of sperm areas: (11); (12); In formula (11) and formula (12), is the position value of the i-th gooseneck barnacle individual in the t+1th iteration, that is, the updated position value, is the position value of the i-th gooseneck barnacle individual in the t-th iteration, is the Levy flight function.

[0035] S3, the enhanced PID controller outputs the real-time temperature value control variable △u(k), and uses the real-time temperature value control variable △u(k) to control the temperature driver. The temperature driver adjusts the water temperature. The temperature sensor feeds back the detected real-time temperature value to the enhanced PID controller and returns to execute S2.

[0036] Furthermore, the temperature of the water in which the naked Sipunculus larvae are cultured is adjusted by calculating △u(k) using formula (2) to control the temperature actuator.

[0037] S4, repeatedly executing steps S2 to S4, adjusting the enhanced PID controller to output the real-time temperature value control variable △u(k), realizing adaptive regulation of the water temperature for culturing naked Sipuncula larvae, and achieving the target temperature value for culturing naked Sipuncula larvae.

[0038] Furthermore, in MATLAB, we set the total number of gooseneck barnacles population N = 100, the maximum number of iterations T = 100, and the dimension of each gooseneck barnacle individual d = 3, setting the upper and lower boundaries of the search space =[400,400,400] and =[0,0,0], the maximum value of the improved significant wave height is 2, the minimum value of the improved significant wave height is 0, the reflection intensity factor β=2, the receiving weight factor =0.5, receiving strength factor =1, follow steps P51 to P55 to write the improved gooseneck barnacle optimization algorithm (HGBO) and the standard gooseneck barnacle optimization algorithm (GBO) mathematical model code, the fitness value is calculated using the objective function J, and the Kp, Ki, and Kd values ​​corresponding to the minimum fitness value are used as the optimal PID control parameters. , , Input into the complex frequency domain form of the system closed-loop model, set the system gain U=10, and the time constant s=50; the specific closed-loop model of the system is obtained as follows: , where z is the complex frequency domain parameter value.

[0039] Furthermore, the fitness value reflects the temperature adjustment time of the water temperature control system for the culture of naked Sipuncula larvae and steady-state error. The smaller the fitness value, the shorter the temperature adjustment time and the smaller the steady-state error. Figure 2 As shown in the figure, it can be found that the fitness value of the improved gooseneck barnacle optimization algorithm (HGBO) gradually decreases with the increase of the number of iterations, and the optimization effect is significant. Compared with the standard gooseneck barnacle optimization algorithm (GBO), the fitness value of GBO decreases rapidly in the early iterations, but then tends to stabilize, and its final minimum fitness value is larger than the fitness value of HGBO. Therefore, HGBO has better optimization effect than GBO, especially in the later iterations, the fitness value is significantly lower than GBO, indicating that HGBO is better for optimizing the PID controller of the water temperature control system for the cultivation of naked star worm larvae.

[0040] Furthermore, the target temperature value is set to 28 degrees, and the value is 28. The real-time temperature value control variable △u(k) is calculated by inverse operation of formula (2), that is, the temperature value control variable at the kth second is 4, and the Kp, Ki, and Kd parameters of the PID corresponding to the minimum fitness values ​​of the improved gooseneck barnacle optimization algorithm (HGBO) and the standard gooseneck barnacle optimization algorithm (GBO) are as follows: Figure 3 As shown in Part A, the optimal parameters of the improved gooseneck barnacle optimization algorithm (HGBO) are: =10.268, =1.578, =115.475; Figure 3 As shown in Part B, the optimal parameters of the standard gooseneck barnacle optimization algorithm (GBO) are: =17.541, =1.135, =278.365; input into the simulation model of the temperature control system, and obtain Figure 4 The water temperature control effect diagram of naked starfish larvae culture is shown in the figure; it can be obviously found that the improved gooseneck barnacle optimization algorithm (HGBO) is superior to the standard gooseneck barnacle optimization algorithm (GBO) in response speed, overshoot, stability and adjustment time. The improved gooseneck barnacle optimization algorithm (HGBO) can reach the temperature value control variable corresponding to the target temperature value more quickly and smoothly, and the overshoot and oscillation are small, showing higher control accuracy and stability.

Claims

1. A method for self-regulating the temperature of water for cultivating naked Sipuncula larvae, characterized in that: The specific steps are: S1, setting a target temperature value of a water temperature control system for cultivating naked Sipuncula larvae, wherein the temperature control system includes a temperature sensor, an enhanced PID controller and a temperature driver; S2, calculating the temperature error e(k) between the real-time temperature value of the water body for larvae cultivation and the target temperature value, and inputting the temperature error e(k) into the enhanced PID controller; the enhanced PID controller is realized by adjusting the control parameters Kp, Ki, and Kd of the standard PID through the improved gooseneck barnacle optimization algorithm; S3, the enhanced PID controller outputs a real-time temperature value control variable △u(k), uses the real-time temperature value control variable △u(k) to control the temperature driver, the temperature driver adjusts the water temperature, the temperature sensor feeds back the detected real-time temperature value to the enhanced PID controller, and returns to execute S2; S4, repeatedly executing steps S2 to S4, adjusting the enhanced PID controller to output the real-time temperature value control variable △u(k), realizing adaptive regulation of the water temperature for culturing naked Sipuncula larvae, and achieving the target temperature value for culturing naked Sipuncula larvae.

2. The method for self-regulating the temperature of water for cultivating naked Sipuncula larvae according to claim 1, characterized in that: The dynamic response characteristics of the enhanced PID controller in S1 are represented by a transfer function, and the mathematical model of the enhanced PID controller is designed according to formula (1); （1）； In formula (1), , , are the optimal proportional coefficient, optimal integral coefficient, and optimal differential coefficient of the enhanced PID, respectively; △e(k) is the difference between the temperature errors at the kth second and the k-1th second; △u(k) is the real-time temperature value control variable; △e(k-1) is the difference between the temperature errors at the k-1th second and the k-2th second; Among them, the temperature driver adjusts the water temperature of naked Sipuncula culture according to the real-time temperature value control variable △u(k), and establishes the dynamic model of the temperature driver. The mathematical model is: （2）； In formula (2), is the temperature of the water body in which the naked Sipunculus is cultured at the k+1th second, is the temperature of the water body in which the naked Sipuncula are cultured at the kth second, is the sampling time interval, Target temperature value for water culture of naked Sipuncula.

3. The method for self-regulating the temperature of water for cultivating naked Sipuncula larvae according to claim 2, characterized in that: The improved gooseneck barnacle optimization algorithm in S2 is specifically improved in the following manner: in the first step, an adaptive factor v is designed by calculating the average Euclidean distance between each gooseneck barnacle individual in the gooseneck barnacle population, the Chebyshev mapping is dynamically improved using the adaptive factor, and the gooseneck barnacle optimization algorithm is initialized using the improved Chebyshev mapping; in the second step, the significant wave height of the gooseneck barnacle optimization algorithm is improved by designing a piecewise function in combination with the fitness function value, and the individual motion step length of the gooseneck barnacle in the gooseneck barnacle optimization algorithm is improved using the improved significant wave height Hs. .

4. The method for self-regulating the temperature of water for cultivating naked Sipuncula larvae according to claim 3, characterized in that: The mathematical model of the adaptive factor v is: （4）； In formula (4), is the adaptive factor value of the tth iteration, is the initial adaptive factor value, t is the current iteration number, is the maximum number of iterations, is the average Euclidean distance between gooseneck barnacle individuals in the current iteration, is the maximum average Euclidean distance threshold; represents the base of natural logarithms; The adaptive factor is used to improve the Chebyshev mapping strategy. The mathematical model of the improved Chebyshev mapping strategy is: （5）； In formula (5), is the new improved Chebyshev map value, is the current improved Chebyshev map value.

5. The method for self-regulating the temperature of water for cultivating naked Sipuncula larvae according to claim 3, characterized in that: The significant wave height improvement of the gooseneck barnacle optimization algorithm, the significant wave height Hs mathematical model of the improved gooseneck barnacle optimization algorithm is: （7）； In formula (7), is the improved significant wave height value of the tth iteration, is the improved maximum value of significant wave height, is the improved minimum significant wave height, is the maximum number of iterations, t is the current number of iterations, , and are the average fitness value, minimum fitness value and maximum fitness value of the current population in the tth iteration, and r is a random number between 0 and 1.

6. The method for self-regulating the temperature of water for cultivating naked Sipuncula larvae according to claim 5, characterized in that: Improve the individual movement step length of gooseneck barnacles by using the significant wave height of the improved gooseneck barnacle optimization algorithm , the specific mathematical model is: （10）； In formula (10), is the movement step length of the i-th gooseneck barnacle individual in the t+1th iteration, is the movement step length of the i-th gooseneck barnacle individual in the t-th iteration, is the ambient wind direction of the tth iteration, is the target dimension value, is the improved significant wave height value of the tth iteration.

7. The method for self-regulating the temperature of water for cultivating naked Sipuncula larvae according to claim 6, characterized in that: In S2, the enhanced PID controller is implemented by adjusting the control parameters Kp, Ki, and Kd of the standard PID through the improved gooseneck barnacle optimization algorithm, wherein the Kp, Ki, and Kd parameters of the standard PID controller are encoded as a three-dimensional real vector, and a real number mapping is established between the real vector and the individual position of the gooseneck barnacle; the optimal individual position of the gooseneck barnacle, that is, the optimal control parameters of the PID controller, is found, and the specific steps are as follows: Step 1: Set the size of the gooseneck barnacle population N and the maximum number of iterations T max , and the dimension d of each gooseneck barnacle species, where the dimension d corresponds to the three parameters of the PID controller: the proportional coefficient Kp , integral coefficient Ki and the differential coefficient Kd ; Step 2: Use the improved Chebyshev mapping strategy to initialize the position of each individual in the gooseneck barnacle population and set the upper and lower boundaries of the search space. and , randomly initialize the position of each individual, where the position matrix is ​​the current position of the i-th gooseneck barnacle species individual ; The mathematical model is: （6）； In formula (6), is the current position of the i-th gooseneck barnacle species individual, is the upper boundary of the search space, is the lower boundary of the search space, is the new improved Chebyshev mapping value; Step 3. Calculate the tth iteration through the objective function of the temperature control system model under different PID control parameters The fitness value of the minimum fitness value is stored. value; Step 4: Determine whether the current number of iterations t is equal to the maximum number of iterations T max If yes, then the individual position of the gooseneck barnacle corresponding to the minimum fitness value is output and parsed into the Kp, Ki, and Kd control parameters of the PID controller; otherwise, return to execute step 5; Step 5, establish an improved gooseneck barnacle optimization algorithm position update mathematical model and calculate the fitness value of the current individual position through the objective function, find the individual position corresponding to the minimum fitness value in each iteration, and optimize the Kp, Ki, Kd control parameters of the PID controller; the individual position corresponding to the minimum fitness value is the best gooseneck barnacle individual position; Step 6: Repeat the global exploration and local development steps until the current number of iterations t is equal to the maximum number of iterations T. max , output the position of the gooseneck barnacle individual with the minimum fitness , which is the optimal parameter combination value of the enhanced PID controller for the temperature control system of naked siphon culture.

8. The method for self-regulating the temperature of water for cultivating naked Sipuncula larvae according to claim 7, characterized in that: The specific steps of step 5 to establish the improved gooseneck barnacle optimization algorithm position update mathematical model are: P51. Calculate the significant wave height value after the tth iteration improvement according to the mathematical model formula (7) ; P52, simulate the movement of gooseneck barnacles in water and the distribution of sperm areas, and propose an adjacent logarithmic spiral area. The mathematical model is : (9）； In formula (9), is the position value of the i-th gooseneck barnacle individual in the t-th iteration, is the distance between the ith barnacle and the yth sperm region; and is a constant that defines the shape of the logarithmic spiral, r is a random number between 0 and 1, is the yth sperm region; P53, update the position of the new gooseneck barnacle in the search space, simulate the movement to the new individual, introduce a motion step, and establish an improved motion step mathematical model according to formula (10); P54, if the sperm area If the number is 1, that is, the maximum value of y is 1, then execute formula (11), otherwise execute formula (12); P55. By simulating sperm throwing behavior, a new simulated updated position of a gooseneck barnacle individual is generated, wherein the updated mathematical model of the position is established according to the number of sperm areas: （11）； （12）； In formula (11) and formula (12), is the position value of the i-th gooseneck barnacle individual in the t+1th iteration, that is, the updated position value, is the position value of the i-th gooseneck barnacle individual in the t-th iteration, is the Levy flight function.

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