Humidity control optimization method for dry-type ripening cabinet

Abstract

The invention discloses a humidity control optimization method for a dry-type ripening cabinet, and belongs to the technical field of PID (Proportion Integration Differentiation) control optimization. A PID controller is optimized by improving a fishing optimization algorithm (ICFOA), accurate control over the humidity of the dry-type ripening cabinet is achieved, and the improvement point comprises the steps that S1, a self-adaptive parameter beta and a disturbance factor delta are introduced to improve a capture rate parameter of the algorithm; s2, introducing a leader guidance strategy to improve a group capture mathematical model; according to the algorithm, various behaviors of human in the fishing process are simulated, an individual optimal solution is searched in a search space of the algorithm, values of the individual optimal solution on each latitude are decoded into Kp, Ki and Kd parameters, the parameters are transmitted into a PID controller, accurate control over the humidity of the dry-type ripening cabinet is achieved, a stable environment needed in the meat ripening process is maintained, and the meat quality is improved. And the quality and stability of the ripening process are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of PID control optimization, and in particular relates to a humidity control optimization method for a dry aging cabinet. Background Art

[0002] Dry-aging cabinets are equipment used for dry-aging food. They are commonly used in the dry-aging process of meat (especially beef). During the dry-aging process, the humidity is usually maintained between 80% and 85%. The working principle of the dry-aging cabinet is to control the temperature, humidity and air flow of the cabinet, dehydrate the meat and decompose the protein over an appropriate period of time, thereby making the meat more tender and the flavor more intense. The key components of the dry-aging cabinet usually include: cabinet structure, temperature control system, humidity control system and air circulation system. Among them, the humidity control system is crucial to the dry-aging cabinet. Through precise humidity control, the aging cabinet can maintain an ideal aging environment for a long time, thereby ensuring the texture and flavor of the meat.

[0003] In the humidity control system of the dry aging cabinet, a PID controller is usually used to adjust the humidity inside the cabinet to maintain the stable humidity environment required during the meat aging process; however, the effect of the traditional PID controller depends on the precise setting of the Kp, Ki, and Kd parameters. The control requirements of the dry aging cabinet are complex, and it is difficult to achieve the desired effect by manual parameter adjustment. Parameters that are too high or too low will lead to problems such as unstable control and long adjustment time. In the aging cabinet, the humidity control requirements may vary with the changes in the meat state, aging cycle, and external environment, but the traditional PID control cannot adaptively adjust the parameters according to these changes, and it is difficult to ensure long-term stable control effects; to solve the shortcomings of traditional PID control, a fishing optimization algorithm can be introduced to automatically adjust the PID parameters to make the control more adaptive and robust.

[0004] The fishing optimization algorithm (CFOA) is inspired by traditional rural fishing methods and simulates human behaviors such as independent exploration, team capture and collective capture in the fishing process. It is a new human behavior optimization algorithm divided into two stages: exploration stage and development stage. Although CFOA shows strong optimization effects on clustering problems, there are still some shortcomings. For example, when dealing with multi-peak optimization problems, it is easy to fall into local optimal solutions. In the face of complex problems and dynamically changing environments, CFOA has weak adaptability and is prone to unsatisfactory optimization effects. Summary of the invention

[0005] The purpose of the present invention is to: In view of the shortcomings of the prior art, the present invention proposes a method for optimizing humidity control in a dry aging cabinet, optimizes the PID controller by an improved fishing optimization algorithm (ICFOA), and automatically adjusts the control parameters of the humidity PID controller according to the real-time humidity data of the aging cabinet fed back, so as to achieve precise control of the humidity of the dry aging cabinet and rapid response to environmental changes, so as to maintain the stable environment required in the meat aging process, reduce the impact of humidity fluctuations on the meat, and ensure the quality and stability of the aging process.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a dry aging cabinet humidity control optimization method, including: improved fishing optimization algorithm (ICFOA) and parallel PID control algorithm, the specific steps are:

[0007] Step 1: determine the optimization target of the humidity control of the aging cabinet, and establish a mathematical model of the humidity control system of the aging cabinet, wherein the mathematical model includes an objective function model and a dry aging cabinet humidity control system model.

[0008] Step 2: Introduce adaptive parameter β, disturbance factor δ and leader guidance strategy to improve the fishing optimization algorithm. The improvements are as follows: S1, introduce adaptive parameter β and disturbance factor δ to improve the capture rate parameter of the algorithm; S2. Introduce leader-guided strategy to improve the mathematical model of group capture.

[0009] Step 3: Automatically optimize the parameters of the PID controller through the ICFOA algorithm. During the global iteration process, the algorithm simulates various human behaviors in the fishing process, searches for the individual optimal solution of the improved fishing optimization algorithm in the algorithm's search space, and decodes the values ​​of the individual optimal solution at each latitude into Kp, Ki, and Kd parameters to obtain the optimal humidity PID control method, namely the ICFOA-PID control method.

[0010] Step 4: The internal environmental humidity of the dry aging cabinet is precisely controlled by the ICFOA-PID control method. The deviation value e(t) between the real-time humidity and the set humidity is calculated based on the real-time humidity data of the aging cabinet fed back. e(t) is input into the ICFOA-PID controller. The control parameters of the PID controller are automatically adjusted according to the size and change trend of the deviation. The PID controller optimized by ICFOA outputs the corresponding humidification or dehumidification power signal P(t). The system adjusts the power of the humidification and dehumidification system in time according to the current humidity deviation and the power signal output by the controller, so that the humidity in the cabinet gradually approaches the set value.

[0011] Furthermore, in the mathematical model, the objective function is used as the basis of the ICFOA optimization algorithm to continuously update and optimize the parameters of the PID controller. The model is also used to measure the quality of the PID controller parameters to make the humidity control more accurate. Considering the system deviation and system energy consumption, the objective function model is selected as follows: (1); In formula (1), J is the fitness value calculated by the objective function, e(t) is the deviation between the real-time humidity and the target humidity at time t, P(t) is the power signal value output by the PID controller at time t, , is the weight coefficient, which is used to balance the control accuracy and energy consumption of humidity, t is the actual operation time of the system, and T is the total operation time of the system.

[0012] Furthermore, in the mathematical model of the aging cabinet humidity control system in step 1, the dry aging cabinet humidity control system model includes: a subtractor module, a PID controller module, a controlled object module, and a humidity signal acquisition module, wherein in the controlled object module, a first-order transfer function is used to simulate the humidity change of the dry aging cabinet, and the mathematical model of the first-order transfer function is: (2); In formula (2), G(s) is the transfer function value, s is the complex frequency domain variable after Laplace transformation, K is the gain of the system, which indicates the influence of input power on humidity change, and τ is the time constant of the system, which indicates the speed of system response. A smaller τ value indicates a faster system response, and a larger τ value indicates a slower system response; Furthermore, for the humidity control system of the dry aging cabinet, the humidity T(s) can be expressed as a function of the input power P(s). Assuming that the input power P(s) directly affects the humidity in the cabinet through humidification or dehumidification, the first-order transfer function of the system can be expressed as: (3); In formula (3), the meanings of the parameters are the same as above. Assuming the gain K of the dry-aging cabinet system is 1 and the time constant τ is 60, the transfer function of the dry-aging cabinet can be written as: (4); In formula (4), the parameters have the same meaning as above.

[0013] Furthermore, in the control system model, a position PID controller is used to calculate the real-time humidification / dehumidification power output P(t) to achieve precise control of humidity. The mathematical model of the parallel PID controller is: (5); In formula (5), P(t) is the power signal value output by the PID controller at time t, e(t) is the deviation between the real-time humidity and the target humidity at time t, Kp is the proportional parameter, Ki is the integral parameter, Kd is the differential parameter, and the other parameters have the same meanings as above; further, the control system model is discretized, and the mathematical model of the parallel PID controller after discretization is obtained as follows: (6); In formula (6), e(t-1) is the humidity value in the aging cabinet collected at the previous moment, and the other parameters have the same meanings as above.

[0014] Furthermore, in the control system model, the humidity signal acquisition module is used to collect the humidity data in the aging cabinet in real time and feed the humidity data back to the control system. The subtractor module performs difference calculation between the real-time humidity information and the set humidity to obtain the real-time humidity deviation e(t), and inputs e(t) into the ICFOA-PID controller to realize closed-loop control of the dry aging cabinet humidity control system.

[0015] Furthermore, the step 2 introduces the adaptive parameter β, the disturbance factor δ and the leader guidance strategy to improve the fishing optimization algorithm, and the specific steps are as follows: S1. The capture rate parameters of the improved algorithm are improved by introducing the adaptive parameter β and the disturbance factor δ. The improved mathematical model is: (7); In formula (7), EFs is the current iteration number of the algorithm, MaxEFs is the maximum iteration number of the algorithm, β is an adaptive parameter that changes dynamically with the iteration number and is used to control the balance between exploration and development. δ is a disturbance factor to prevent the algorithm from falling into the local optimum. The mathematical model of β and δ is: (8); (9); In formula (8), the meanings of the parameters are the same as above. In formula (9), is a small constant used to control the amplitude of the disturbance, and the other parameters have the same meaning as above; S2. The leader-guided strategy is introduced to improve the mathematical model of group capture. The improved mathematical model is: (10); In formula (10), is the position of the i-th individual in the updated group, c is a group of 3-4 individuals in group capture, i=1…SearchAgents, SearchAgents is the number of algorithm populations, EFs is the current iteration number of the algorithm, MaxEFs is the maximum iteration number of the algorithm, is the position of the i-th individual in the group in the current iteration, The group's target. is the best individual position in the iteration, r2 and r4 are random unit vectors of dimension Dim with values ​​between [0, 1], and r3 is a random unit vector of dimension Dim with values ​​between [-1, 1]; The mathematical model is: (11); In formula (11), mean() takes the average of the positions of each member of the group after grouping as the position of the capture target, and the other parameters have the same meanings as above.

[0016] Furthermore, in step 3, the parameters of the PID controller are automatically optimized by the ICFOA algorithm. During the global iteration of the algorithm, the algorithm simulates various behaviors of humans in the fishing process, searches for the individual optimal solution of the improved fishing optimization algorithm in the algorithm's search space, and decodes the values ​​of the individual optimal solution at each latitude into Kp, Ki, and Kd parameters to obtain the optimal humidity PID control method. The specific steps are: S1. Initialize the population size SearchAgents of the improved fishing optimization algorithm (ICFOA), the maximum number of iterations MaxEFs, the problem dimension Dim, and the algorithm search space [Ub, Lb], where Ub and Lb are unit vectors of dimension Dim; S2. Initialize the individual positions of the improved fishing optimization algorithm, generate the initial positions of the population individuals, and the mathematical model of the initial individual positions is: (12); In formula (12), is the initial position of the fisherman generated randomly by the pseudo-random number method, r1 is a random unit vector with a value between [0, 1], and Ub and Lb have the same meaning as above; S3. Check whether the positions of all fishermen are out of bounds and calculate their fitness. Update the positions and fitness of all fishermen by greedy selection, select the individual with the smallest fitness as the best individual, and record the fitness of the individual as the best fitness. The mathematical model of greedy selection is: (13); In formula (13), To select the updated individual fisherman positions through greedy selection, is the individual position of the fisherman in the current iteration, is the new individual position of the fisherman calculated by the algorithm in the iteration, for The fitness of the position, for Fitness of the position; S4. The algorithm enters the exploration stage. When EFs < MaxEFs / 2, a mathematical model is established by simulating the behavior of a fisherman independently exploring the surrounding area according to the water surface disturbance. The mathematical model of independent exploration is: (14); In formula (14), is the updated individual position, is the individual position in the current iteration, is the individual position randomly selected from the algorithm population in the current iteration, r s is a random number with a value in [0, 1], s is a random unit vector with a dimension of Dim and a value in [0, 1], Exp represents the fisherman's experience value, R is the range of the fisherman's independent exploration, and the mathematical model of Exp is: (15); In formula (15), is the fitness of the position, is the fitness of the position, is the maximum fitness value, is the minimum fitness value, that is, the best fitness; Furthermore, the mathematical model of R is: (16); In formula (16), Dis is the Euclidean distance between the position and the position, and the meanings of other parameters are the same as above; S5. When EFs < MaxEFs / 2 and p ≥ α, where p is a random number with a value in [0, 1] and the mathematical model of α is the same as above, simulate the behavior of fishermen forming groups to search around the target point and establish a mathematical model of group capture for the behavior of the group cooperating to catch fish schools. Introduce a leader guidance strategy to improve the mathematical model of group capture, and update the individual positions through the improved mathematical model; (17); In formula (17), is the updated individual position, is the best individual fisherman position, GD() is the Gaussian distribution function, r5 is a random unit vector of dimension Dim with a value between [0, 1], mean(F) is the position of the average of all members of the population through the mean() function, is the overall variance of the algorithm, The mathematical model is: (18); In formula (18), the parameters have the same meaning as above; S7. Check whether the current number of iterations EFs is greater than the maximum number of iterations MaxEFs. If so, output the individual optimal solution of the improved fishing optimization algorithm, and decode the values ​​of the individual optimal solution at each latitude into Kp, Ki, and Kd parameters and output them to the dry aging cabinet humidity control system. If not, execute EFs=EFs+1 and return to step S3 to continue the algorithm iteration and optimization.

[0017] The present invention proposes a method for optimizing humidity control in a dry aging cabinet, and optimizes the parallel humidity PID controller by using an improved fishing optimization algorithm (ICFOA). Due to the adoption of the above technical solution, the present invention has the following beneficial effects: P1. The capture rate parameter of the algorithm is improved by introducing the adaptive parameter β and the disturbance factor δ. In the early stage of the algorithm iteration, the value of β is small, which makes the search more inclined to global exploration. As the number of iterations increases, β gradually increases, and the search gradually turns to local development. δ provides a certain disturbance in the early stage of the algorithm iteration to help jump out of the local optimum. As the number of iterations increases, δ gradually decreases, making the algorithm easier to converge. This enables the algorithm to dynamically balance the global exploration and local development capabilities according to the iteration process, which not only ensures the global exploration capability in the early stage, but also enhances the depth of algorithm development in the later stage. P2. Introduce a leader-guiding strategy to improve the mathematical model of group capture. In the initial exploration stage, the leader's guiding role can be weakened to enhance the diversity of exploration; in the later development stage, the leader's guiding role should be enhanced to accelerate the convergence of the algorithm; P3. Optimize the PID controller through the improved fishing optimization algorithm (ICFOA), and automatically adjust the control parameters of the humidity PID controller according to the real-time humidity data of the aging cabinet, so as to achieve precise control of the humidity of the dry aging cabinet and rapid response to environmental changes, so as to maintain the stable environment required in the meat aging process, reduce the impact of humidity fluctuations on meat, and ensure the quality and stability of the aging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Diagram of the optimization steps for the humidity PID controller of the dry aging cabinet.

[0019] Figure 2Optimize the dry aging cabinet humidity PID controller flow chart to improve the fishing optimization algorithm.

[0020] Figure 3 To improve the fishing optimization algorithm, the Kp, Ki, and Kd parameter curves of the dry aging cabinet humidity PID controller are optimized.

[0021] Figure 4 Comparison chart of the fitness value curves for optimizing the humidity PID controller of the dry aging cabinet using the improved fishing optimization algorithm and the ordinary fishing optimization algorithm.

[0022] Figure 5 A comparison chart of the effects of optimizing the humidity PID controller of the dry aging cabinet using the improved fishing optimization algorithm and the ordinary fishing optimization algorithm. DETAILED DESCRIPTION

[0023] 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.

[0024] The present invention provides a dry aging cabinet humidity control optimization method, including: an improved fishing optimization algorithm (ICFOA) and a parallel PID control algorithm, such as Figure 1 As shown, the specific steps are:

[0025] Step 1: determine the optimization target of the humidity control of the aging cabinet, and establish a mathematical model of the humidity control system of the aging cabinet, wherein the mathematical model includes an objective function model and a dry aging cabinet humidity control system model.

[0026] Furthermore, in the mathematical model of the aging cabinet humidity control system in step 1, the dry aging cabinet humidity control system model includes: a subtractor module, a PID controller module, a controlled object module, and a humidity signal acquisition module, wherein in the controlled object module, a first-order transfer function is used to simulate the humidity change of the dry aging cabinet, and the mathematical model of the first-order transfer function is: (2); In formula (2), G(s) is the transfer function value, s is the complex frequency domain variable after Laplace transformation, K is the gain of the system, which indicates the influence of input power on humidity change, and τ is the time constant of the system, which indicates the speed of system response. A smaller τ value indicates a faster system response, and a larger τ value indicates a slower system response; Furthermore, for the humidity control system of the dry aging cabinet, the humidity T(s) can be expressed as a function of the input power P(s). Assuming that the input power P(s) directly affects the humidity in the cabinet through humidification or dehumidification, the first-order transfer function of the system can be expressed as: (3); In formula (3), the meanings of the parameters are the same as above. Assuming the gain K of the dry-aging cabinet system is 1 and the time constant τ is 60, the transfer function of the dry-aging cabinet can be written as: (4); In formula (4), the parameters have the same meaning as above.

[0027] Furthermore, in the control system model, a position PID controller is used to calculate the real-time humidification / dehumidification power output P(t) to achieve precise control of humidity. The mathematical model of the parallel PID controller is: (5); In formula (5), P(t) is the power signal value output by the PID controller at time t, e(t) is the deviation between the real-time humidity and the target humidity at time t, Kp is the proportional parameter, Ki is the integral parameter, Kd is the differential parameter, and the other parameters have the same meanings as above; further, the control system model is discretized, and the mathematical model of the discretized parallel PID controller is obtained as follows: (6); In formula (6), e(t-1) is the humidity value in the aging cabinet collected at the previous moment, and the other parameters have the same meanings as above.

[0028] Furthermore, in the control system model, the humidity signal acquisition module is used to collect the humidity data in the aging cabinet in real time and feed the humidity information back to the control system. The subtractor module performs difference calculation between the real-time humidity data and the target humidity to obtain the real-time humidity deviation e(t), and inputs e(t) into the ICFOA-PID controller to realize closed-loop control of the dry aging cabinet humidity control system.

[0029] Step 2: Introduce adaptive parameter β, disturbance factor δ and leader guidance strategy to improve the fishing optimization algorithm. The specific steps are: S1. The capture rate parameters of the improved algorithm are improved by introducing the adaptive parameter β and the disturbance factor δ. The improved mathematical model is: (7); In formula (7), EFs is the current iteration number of the algorithm, MaxEFs is the maximum iteration number of the algorithm, β is an adaptive parameter that changes dynamically with the iteration number and is used to control the balance between exploration and development. δ is a disturbance factor to prevent the algorithm from falling into the local optimum. The mathematical model of β and δ is: (8); (9); In formula (8), the meanings of the parameters are the same as above. In formula (9), is a small constant used to control the amplitude of the disturbance, set to 0.001, and the other parameters have the same meaning as above; S2. The leader-guided strategy is introduced to improve the mathematical model of group capture. The improved mathematical model is: (10); In formula (10), is the position of the i-th individual in the updated group, c is a group of 3-4 individuals in group capture, i=1…SearchAgents, EFs is the current iteration number of the algorithm, MaxEFs is the maximum iteration number of the algorithm, is the position of the i-th individual in the group in the current iteration, The group's target. is the best individual position in the iteration, r2 and r4 are random unit vectors of dimension Dim with values ​​between [0, 1], and r3 is a random unit vector of dimension Dim with values ​​between [-1, 1]; The mathematical model is: (11); In formula (11), mean() takes the average of the positions of each member of the group after grouping as the position of the capture target, and the other parameters have the same meanings as above.

[0030] Step 3: Automatically optimize the parameters of the PID controller through the ICFOA algorithm. During the global iteration process, the algorithm simulates various human behaviors in the fishing process, searches for the individual optimal solution of the improved fishing optimization algorithm in the algorithm's search space, and decodes the values ​​of the individual optimal solution at each latitude into Kp, Ki, and Kd parameters to obtain the optimal humidity PID control method, namely the ICFOA-PID control method.

[0031] Further, in step 3, the parameters of the PID controller are automatically optimized by the ICFOA algorithm. During the global iteration of the algorithm, by simulating various behaviors of humans during the fishing process, the individual optimal solution of the improved fishing optimization algorithm is searched in the search space of the algorithm. The values of each dimension of the individual optimal solution are decoded into the Kp, Ki, and Kd parameters to obtain the optimal humidity PID control method, as Figure 2 shown, the specific steps are as follows: S1. Initialize the population size SearchAgents, the maximum number of iterations MaxEFs, the problem dimension Dim of the improved fishing optimization algorithm (ICFOA), and the algorithm search space [Ub, Lb], where Ub and Lb are unit vectors of dimension Dim; S2. Initialize the individual positions of the improved fishing optimization algorithm, generate the initial positions of the population individuals, and the mathematical model for initializing the individual positions is: (12); In formula (12), t is the initial position of the fisherman individual randomly generated by the pseudo-random number method, r1 is a random unit vector with a value in [0, 1], and the meanings of Ub and Lb are the same as above; S3. Check whether all the fisherman individual positions are out of bounds and calculate their fitness; update all the fisherman individual positions and fitness by the method of greedy selection, select the individual with the minimum fitness as the best individual, and record the fitness of this individual as the best fitness. The mathematical model of greedy selection is: (13); In formula (13), is the position of the fisherman individual updated by greedy selection, is the position of the fisherman individual in the current iteration, is the new position of the fisherman individual calculated by the algorithm in the iteration, is the fitness of the position, is the fitness of the position; S4. The algorithm enters the exploration stage. When EFs < MaxEFs / 2, simulate the behavior of fishermen independently exploring the surrounding area according to the water surface disturbance to establish a mathematical model. The mathematical model of independent exploration is: (14); In formula (14), is the updated individual position, is the individual position in the current iteration, is the individual position randomly selected from the algorithm population in the current iteration, r sis a random number with a value between [0, 1], s is a random unit vector with a dimension of Dim and a value between [0, 1], Exp represents the fisherman's experience value, R is the range of independent exploration by the fisherman, and the mathematical model of Exp is: (15); In formula (15), is the fitness of the position, is the fitness of the position, is the maximum fitness value, is the minimum fitness value, that is, the best fitness; Further, the mathematical model of R is: (16); In formula (16), Dis is the position and the Euclidean distance between positions, and the meanings of other parameters are the same as above; S5. When EFs < MaxEFs / 2 and p ≥ α, p is a random number with a value between [0, 1], and the mathematical model of α is the same as above. Simulate the behavior of fishermen forming groups to search around the target point, establish a mathematical model of group capture for the behavior of group cooperation to catch fish schools, introduce a leader guidance strategy to improve the mathematical model of group capture, and update the position of individuals through the improved mathematical model; S6. When EFs ≥ MaxEFs / 2, simulate the behavior of all fishermen cooperating to concentrate the fish school in a region for collective capture, establish a mathematical model of collective capture to update individuals, and the mathematical model is: (17); In formula (17), is the updated individual position, is the position of the best fisherman individual, GD() is the Gaussian distribution function, r5 is a random unit vector with a dimension of Dim and a value between [0, 1], mean(F) is the position obtained by taking the average of the positions of all members of the population through the mean() function, is the overall variance of the algorithm, The mathematical model of is: (18); In formula (18), the meanings of each parameter are the same as above; S7. Check whether the current number of iterations EFs is greater than the maximum number of iterations MaxEFs. If so, output the individual optimal solution of the improved fishing optimization algorithm, and decode the values ​​of the individual optimal solution at each latitude into Kp, Ki, and Kd parameters and output them to the dry aging cabinet humidity control system. If not, execute EFs=EFs+1 and return to step S3 to continue the algorithm iteration and optimization.

[0032] Step 4: Use the ICFOA-PID control method to accurately control the internal humidity of the dry aging cabinet. The specific steps are as follows: S1. Use Siumulink simulation software to establish a simulation model for the dry aging cabinet humidity PID controller, convert the aging cabinet humidity control optimization problem into a mathematical problem to be solved, set the humidity PID controller simulation model running time to 50s, the sampling time to 0.5s, set the initial humidity to 80%, and the target humidity to 81%; S2. Use Matlab mathematical software to establish a mathematical model of the improved fishing optimization algorithm, initialize the population size SearchAgents of the improved fishing optimization algorithm to 30, the maximum number of iterations MaxEFs to 20, the algorithm search space dimension Dim to 3, and the upper and lower bounds Ub of the algorithm space to 200 and Lb to 0; S3. Use Siumulink simulation software to build a simulation model for the objective function, write a connection function, and connect the dry-aging cabinet humidity PID controller with the improved fishing optimization algorithm mathematical model; S4, running the improved fishing optimization algorithm mathematical model, the algorithm iteratively seeks the best, outputs the individual solution found in each iteration, and decodes the values ​​of the individual solution at each latitude into Kp, Ki, and Kd parameters; S5, outputting the Kp, Ki, and Kd parameters to the dry-aging cabinet humidity control system through a connection function, and running the dry-aging cabinet humidity PID controller; S6. According to the real-time humidity data of the ripening cabinet fed back, the subtractor calculates the deviation value e(t) between the real-time humidity and the set humidity, and inputs e(t) into the ICFOA-PID controller; S7, the PID controller optimized by ICFOA outputs the corresponding humidification or dehumidification power signal P(t). The system adjusts the power of the humidification and dehumidification system in a timely manner according to the current humidity deviation and the power signal output by the controller, so that the humidity in the cabinet gradually approaches the set value; S8, the objective function model calculates the fitness of the individual solution, and transmits the fitness back to the improved fishing optimization algorithm mathematical model, and the algorithm continues to iterate to determine whether the iteration is terminated; S9. If the iteration is terminated, the individual optimal solution of the improved fishing optimization algorithm is output, and the values ​​of each latitude of the individual optimal solution are decoded into Kp, Ki, and Kd parameters, such as Figure 3 As shown, the optimal Kp, Ki, and Kd parameters are: 62.673, 0.303, and 38.985.

[0033] like Figure 4 As shown in the figure, the fitness value curves of the improved fishing optimization algorithm and the ordinary improved fishing optimization algorithm for optimizing the dry-aging cabinet humidity PID controller are compared and analyzed. It can be seen that in the first few iterations, the fitness value of the improved fishing optimization algorithm drops rapidly, showing a faster convergence speed, and tends to a smaller fitness value earlier than the ordinary algorithm. When the number of iterations is between 4 and 12, the ordinary algorithm has obvious stagnation, showing that it is trapped in the local optimal dilemma, while the improved algorithm can continue to search for the optimal solution globally in the middle and late stages of the iteration, and does not fall into the local optimal state like the ordinary algorithm. As the algorithm enters the late stage of the iteration, the fitness values ​​of the two algorithms gradually stabilize. The improved fishing optimization algorithm finally converges to 6.738, while the ordinary algorithm converges to 7.8789. It can be concluded that when optimizing the Kp, Ki, and Kd parameters of the dry-aging cabinet humidity PID controller, the performance of the improved fishing optimization algorithm is significantly stronger than that of the ordinary fishing optimization algorithm.

[0034] like Figure 5 As shown in the figure, the response curves of the improved fishing optimization algorithm and the ordinary improved fishing optimization algorithm for optimizing the dry aging cabinet humidity PID controller are compared and analyzed. It can be seen that the response curve of the improved fishing optimization algorithm quickly approaches the target value of 81% in the first few seconds, and the response speed of the ordinary fishing optimization algorithm is slightly slower, indicating that the PID controller optimized by the improved algorithm has a faster response speed. The response curves of the two algorithms have a slight overshoot near the target value, but the overshoot of the improved fishing optimization algorithm is smaller and quickly returns to the vicinity of the target value, which shows that both algorithms can control the overshoot well, but the improved algorithm is better. The response curve of the improved fishing optimization algorithm stably reaches the target value after about 5 seconds, showing a shorter dynamic stabilization time, and the curve of the ordinary fishing optimization algorithm completely reaches a stable state after about 6 seconds, with a slight lag. In summary, the improved fishing optimization algorithm shows better effects in response speed, overshoot control and dynamic stabilization time when optimizing the humidity PID controller, so that the system can reach the target humidity more quickly and smoothly, which shows that it is effective to optimize the dry aging cabinet humidity PID controller by the improved fishing optimization algorithm.

[0035] In summary, the present invention provides a method for optimizing humidity control in a dry-aging cabinet. The method optimizes the humidity PID controller of the dry-aging cabinet by using an improved integrated circuit optimization algorithm (ICFA), so that the system can automatically adjust the parameters of the PID controller according to the real-time humidity feedback of the aging cabinet, thereby achieving precise control of the humidity of the dry-aging cabinet. This method can quickly respond to environmental changes, maintain the stable environment required during the meat aging process, reduce the impact of humidity fluctuations on the meat, and thus ensure the quality and consistency of the aging process.

Claims

1. A method for optimizing humidity control in a dry aging cabinet, comprising: Improve the fishing optimization algorithm and parallel PID control algorithm. The specific steps are as follows: Step 1, determining the optimization target of the humidity control of the aging cabinet, and establishing a mathematical model of the humidity control system of the aging cabinet, wherein the mathematical model includes an objective function model and a dry aging cabinet humidity control system model; Step 2, introducing adaptive parameter β, disturbance factor δ and leader guidance strategy to improve the fishing optimization algorithm; Step 3: Automatically optimize the parameters of the PID controller through the ICFOA algorithm. The algorithm iterates to find the individual optimal solution, and decodes the values ​​of each latitude of the individual optimal solution into Kp, Ki, and Kd parameters to obtain the optimal humidity PID control method, namely the ICFOA-PID control method. Step 4. The internal environmental humidity of the dry aging cabinet is precisely controlled by the ICFOA-PID control method. According to the real-time humidity data of the aging cabinet fed back, the deviation value e(t) between the real-time humidity and the target humidity is calculated, and e(t) is input into the ICFOA-PID controller. The control parameters of the PID controller are automatically adjusted according to the size and change trend of the deviation. The PID controller optimized by ICFOA outputs the corresponding humidification or dehumidification power signal P(t). The system adjusts the power of the humidification and dehumidification system in time according to the current humidity deviation and the power signal output by the controller, so that the humidity in the cabinet gradually approaches the set value.

2. The method for optimizing humidity control in a dry aging cabinet according to claim 1, characterized in that: In step 1, the objective function model of the dry-aging cabinet humidity control system is: （1）； In formula (1), J is the fitness value calculated by the objective function, e(t) is the deviation between the real-time humidity and the target humidity at time t, P(t) is the power signal value output by the PID controller at time t, , is the weight coefficient, which is used to balance the control accuracy and energy consumption of humidity, t is the actual operation time of the system, and T is the total operation time of the system.

3. The method for optimizing humidity control in a dry aging cabinet according to claim 2, characterized in that: In step 2, the capture rate parameter of the algorithm is improved by introducing the adaptive parameter β and the disturbance factor δ. The mathematical model of the improved capture rate parameter is: （2）； In formula (2), EFs is the current iteration number of the algorithm, MaxEFs is the maximum iteration number of the algorithm, β is an adaptive parameter that changes dynamically with the iteration number, δ is a disturbance factor, and the mathematical model of β and δ is: （3）； （4）； In formula (3), the parameters have the same meaning as above. In formula (4), is a small constant used to control the amplitude of the disturbance. The other parameters have the same meaning as above.

4. A dry aging cabinet humidity control optimization method according to claim 3, characterized in that: In step 2, the leader-guided strategy is introduced to improve the mathematical model of group capture. The improved mathematical model is: （5）； In formula (5), is the position of the i-th individual in the updated group, c is the number of groups consisting of 3-4 individuals in group capture, i=1…SearchAgents, SearchAgents is the number of algorithm populations, EFs is the current iteration number of the algorithm, MaxEFs is the maximum iteration number of the algorithm, is the position of the i-th individual in the group in the current iteration, The group's target. is the best individual position in the iteration, r2 and r4 are random unit vectors of dimension Dim with values ​​between [0, 1], r3 is a random unit vector of dimension Dim with values ​​between [-1, 1], The mathematical model is: （6）； In formula (6), mean() takes the average position of each member of the group after grouping as the position of the capture target, and the other parameters have the same meaning as above.

5. The method for optimizing humidity control in a dry-aging cabinet according to claim 4, characterized in that: In step 3, the parameters of the PID controller are automatically optimized by the ICFOA algorithm, and the algorithm iteratively searches for the individual optimal solution. The specific steps are: S1, initialize the population size SearchAgents of the improved fishing optimization algorithm, the maximum number of iterations MaxEFs, the problem dimension Dim, and the algorithm search space [Ub, Lb]; S2. Initialize the individual positions of the improved fishing optimization algorithm. The mathematical model of the initial individual positions is: （7）； In formula (7), is the initial position of the fisherman generated randomly by the pseudo-random number method, r1 is a random unit vector with a value between [0, 1], and Ub and Lb have the same meaning as above; S3, check whether the positions of all fishermen are out of bounds, calculate their fitness, update the positions and fitness of all fishermen by greedy selection, select the individual with the smallest fitness as the best individual, and record the fitness of the individual as the best fitness; S4, the algorithm enters the exploration phase. When EFs < MaxEFs / 2, the individual position is updated through independent exploration of the mathematical model, and EFs is the current iteration number; S5. When EFs < MaxEFs / 2 and p≥α, the leader-guided strategy is introduced to improve the mathematical model of group capture. The position of the individual is updated through the improved mathematical model, and p is a random number between [0, 1]. S6, when EFs ≥ MaxEFs / 2, the individual positions are updated through the collective capture mathematical model; S7. Check whether EFs is greater than the maximum number of iterations MaxEFs. If so, output the individual optimal solution of the improved fishing optimization algorithm, and decode the values ​​of each latitude of the individual optimal solution into Kp, Ki, and Kd parameters and output them to the dry aging cabinet humidity control system. If not, execute EFs=EFs+1 and return to S3 to continue the algorithm iteration and optimization.

6. A method for optimizing humidity control in a dry-aging cabinet according to claim 5, characterized in that: In step 3, the mathematical model for independent exploration is: （8）； In formula (8), is the updated individual position, is the individual position in the current iteration, is the position of an individual randomly selected from the algorithm population in the current iteration, r s is a random number between [0, 1], s is a random unit vector of dimension Dim between [0, 1], Exp represents the fisherman's experience, and R is the range of the fisherman's independent exploration. The mathematical model of Exp and R is: （9）； （10）； In formula (9), for The fitness of the position, for The fitness of the position, is the maximum fitness value, is the minimum fitness value, in formula (10), Dis is Location and The Euclidean distance between locations. Other parameters have the same meaning as above.

7. The method for optimizing humidity control in a dry aging cabinet according to claim 5, characterized in that: In step 3, the mathematical model is: （11）； In formula (11), is the updated individual position, is the best individual fisherman position, GD() is the Gaussian distribution function, r5 is a random unit vector of dimension Dim between [0, 1], mean(F) is the position of all members of the population averaged by the mean() function, is the overall variance of the algorithm, The mathematical model is: （12）； In formula (12), the parameters have the same meaning as above.

8. The method for optimizing humidity control in a dry aging cabinet according to claim 5, characterized in that: In step 3, the positions and fitness of all fishermen are updated by greedy selection. The mathematical model of greedy selection is: （13）； In formula (13), To select the updated individual fisherman positions through greedy selection, is the individual position of the fisherman in the current iteration, is the new individual position of the fisherman calculated by the algorithm in the iteration, for The fitness of the position, for The fitness of the position.

Images