A glue liquid temperature control optimization method for processing donkey-hide gelatin

Abstract

The application discloses a glue liquid temperature control optimization method for processing donkey-hide gelatin, and belongs to the technical field of swarm intelligence optimization, and comprises the following steps: constructing a glue liquid temperature closed-loop PID control system of a donkey-hide gelatin boiling process, configuring an improved side handstand spider optimization algorithm as a parameter setting core, executing an adaptive setting process of the PID control parameter, and mapping a global optimal control parameter vector obtained through optimization to a donkey-hide gelatin glue liquid temperature PID controller module. The application focuses on the PID parameter setting problem of donkey-hide gelatin glue liquid temperature control, introduces the improved side handstand spider optimization algorithm as a swarm intelligence algorithm to set the PID control parameter of the donkey-hide gelatin glue liquid temperature, and the improved side handstand spider optimization algorithm can effectively overcome the defect that the side handstand spider optimization algorithm is prone to falling into local optimization, so that the donkey-hide gelatin glue liquid temperature can obtain relatively more excellent control effect.

Description

Technical Field

[0001] This invention belongs to the technical field of swarm intelligence optimization, and particularly relates to an optimization method for controlling the temperature of the gelatin solution used in the processing of donkey-hide gelatin. Background Technology

[0002] Temperature control of the gelatin solution is a core aspect of the gelatin production process, directly determining its physicochemical properties and the stability of its medicinal efficacy. Precise temperature control during the boiling process is the cornerstone of ensuring product quality. However, the gelatin solution exhibits significant inertia and time lag during heating, and its viscosity changes non-linearly with water evaporation, easily causing temperature fluctuations. Excessively high temperatures can lead to protein denaturation, destroying active ingredients, and even causing production safety hazards such as overflow. Currently, the commonly used solution is based on traditional PID control strategies, adjusting heating power through temperature feedback. While PID control is simple in structure, it often faces challenges such as difficult parameter tuning, large response lag, and low control accuracy when dealing with complex time-varying characteristics like sudden viscosity changes during gelatin boiling, making it difficult to meet the constant temperature requirements for high-quality gelatin production. Summary of the Invention

[0003] To overcome the technical problems described in the background section, this invention provides an optimization method for controlling the temperature of gelatin solution in donkey-hide gelatin processing. Focusing on the PID parameter tuning problem for donkey-hide gelatin solution temperature control, an improved side-hands-turning spider optimization algorithm, a swarm intelligence algorithm, is introduced to tune the PID control parameters for the gelatin solution temperature. The improved side-hands-turning spider optimization algorithm surpasses the traditional side-hands-turning spider optimization algorithm in convergence speed, optimization accuracy, and overshoot suppression and adjustment speed of the controlled system. The improved algorithm effectively overcomes the side-hands-turning spider optimization algorithm's tendency to get trapped in local optima, enabling a relatively superior control effect for the temperature of the donkey-hide gelatin solution.

[0004] The technical solution of the present invention is: a method for optimizing the temperature control of gelatin solution in the processing of donkey-hide gelatin, comprising the following steps: S1. Construct a closed-loop PID control system for the temperature of the gelatin solution during the gelatin boiling process, including a gelatin solution temperature error calculation module, a gelatin solution temperature PID controller module, an improved side-hand-turning spider optimization algorithm module, a gelatin solution temperature regulation execution module, and a gelatin solution temperature real-time monitoring module. S2. Configure the improved side-hand-turning spider optimization algorithm as the core of parameter tuning. The specific algorithm improvement strategy is configured as follows: S21. For the global exploration stage where individual spiders are stuck in deep stagnation, a spider web vibration triangulation strategy is embedded. The individual position is reconstructed by introducing the mutation mechanism of the differential evolution operator. The vector difference information between the population is used to guide the stagnant individual to perform directional jumps, so as to enhance the algorithm's ability to escape local extreme value traps in complex fitness landscapes. S22. For spiders in a normal exploration state, the Levy flight predation and attack strategy is integrated. The long-tailed distribution random step size generated by the Mantegna algorithm is used to replace the linear translation component, and a translation guidance term with non-uniform variable step size characteristics is constructed to improve the global traversal efficiency and coverage of the algorithm in the broad PID parameter solution space. S23. For the local development stage where individual spiders have sufficient energy, a chaotic silk-spinning micro-weaving strategy is implanted. The ergodicity and randomness of the Tent chaotic mapping are used to generate chaotic perturbation factors, and the current solution is refined for search and neighborhood fine-tuning to improve the algorithm's accuracy and convergence speed in the global optimal solution region. S3. Execute the adaptive tuning process for the PID control parameters, and use the improved side-flipping spider optimization algorithm to iteratively optimize the control parameter space of the adhesive temperature PID control system to obtain the optimal control parameter vector that balances system response speed and stability. , , ; S4. Map the globally optimal control parameter vector obtained through optimization to the PID controller module for the temperature of the donkey-hide gelatin solution, update the internal parameters of the controller, thereby achieving precise adjustment and anti-disturbance control optimization of the temperature of the gelatin solution during the donkey-hide gelatin cooking process.

[0005] Furthermore, in the PID control system for the temperature of the gelatin liquid in the gelatin processing constructed in step S1, historical operating data is collected through the gelatin liquid temperature monitoring module, and a transfer function model of the gelatin liquid temperature control system is established. The improved side-hand-turning spider optimization algorithm module performs iterative optimization based on the transfer function model, and calculates the PID controller parameters in a virtual simulation environment. After the iteration termination condition is met, the optimal global control parameters obtained by optimization are output and configured into the adhesive temperature PID controller module. The adhesive temperature PID controller module calculates the precise control quantity based on the real-time temperature error signal and the configured optimal control parameters, and outputs the control quantity to the adhesive temperature adjustment module to drive the actuator to accurately adjust the adhesive temperature.

[0006] Furthermore, the spiderweb vibration triangulation localization strategy in step S21 includes the following steps: S211. In global exploration mode, first check the stagnation counter of the current spider individual. Does it exceed the threshold of 5? S212, if satisfied Three distinct individuals are randomly selected from the current population, and their position vectors are denoted as follows: , and ; S213. Based on the principle of differential evolution, using scaling factors The differences between the last two individuals are weighted and then added to the first individual to generate a new position. The calculation formula is as follows: , In the formula For the generated new position vector, Let the vectors be the position vectors of three distinct individuals randomly selected from the current population. This is the scaling factor for differential evolution.

[0007] Furthermore, the Levi's flight predator-prey attack strategy in step S22 includes the following steps: S221. When an individual spider enters a directional somersault state, that is, when... At that time, based on the Mantegna algorithm, the Lévy index was first used. Generate a random step-size vector that follows a Lévy distribution. ; S222, Calculate the rotation component , , In the formula For rotational components, For the intensity of a side handspring, A random number between [0, 1] and Let be the upper and lower bound vectors of the search space, respectively. This is the rotation range coefficient; S223, Using Levy step size vector The translation guiding term is calculated using the following formula: , In the formula As a translation guide item, This is the scaling factor for Levy's flight step size. Let Lévy's flight random step size vector be . This is the current global optimal position. The symbol represents the current individual's position. This represents element-wise multiplication of vectors along corresponding dimensions; S224. Superimpose the rotation component and the Levy translation component onto the current position. Up, complete the location update.

[0008] Furthermore, the chaotic micro-weaving strategy in step S23 includes the following steps: S231, When the energy level of an individual spider At this time, it enters the chaotic local search mode and initializes the chaotic variables. And set the number of internal search iterations. ; S232, Calculate the development factor Dynamically adjusted current chaotic search radius The calculation formula is: , In the formula The radius of the chaotic search is... The development factor is the factor that increases with the number of iterations. and Let be the upper and lower bound vectors of the search space, respectively. This is the base scaling factor for the search radius; S233, in In the next iteration, the chaotic variables are iteratively updated using the Tent mapping formula. By utilizing its traversal properties, a more uniformly distributed perturbation factor can be generated. S234. Based on the current local optimal position The candidate solution is generated using the following formula: ; If a candidate solution has better fitness, the local position is updated immediately.

[0009] Furthermore, the optimization objective function of the PID control system for the temperature of donkey-hide gelatin solution is... The mathematical expression is: , In the formula Indicates the total simulation duration. For time, To determine the real-time deviation between the set temperature and the actual adhesive temperature, This represents the actual overshoot of the system's step response. The maximum allowable overshoot threshold, The penalty weight for violating the constraint.

[0010] The beneficial effects of this invention due to the adoption of the above-mentioned technology are as follows: 1. This invention focuses on the PID parameter tuning problem of donkey-hide gelatin liquid temperature control. It introduces an improved side-hand-turning spider optimization algorithm as a swarm intelligence algorithm to tune the PID control parameters of donkey-hide gelatin liquid temperature. The improved side-hand-turning spider optimization algorithm surpasses the side-hand-turning spider optimization algorithm in terms of convergence speed, optimization accuracy, and overshoot suppression and adjustment speed of the controlled system. The improved side-hand-turning spider optimization algorithm can effectively overcome the defect of the side-hand-turning spider optimization algorithm that is prone to getting trapped in local optima, and can achieve a relatively better control effect on the temperature of donkey-hide gelatin liquid. 2. This invention configures a spider web vibration triangulation localization strategy in the improved side-flipping spider optimization algorithm module, reconstructs the position of spider individuals trapped in a deep stagnation state using the mutation mechanism of the differential evolution operator, and guides stagnant individuals to perform directional jumps using the vector difference information between different individuals in the population, thereby disrupting the equilibrium state of local extreme points. This enables the algorithm to force the solution vector to escape from the local optimum trap in the multi-peak PID parameter fitness landscape, ensuring the algorithm's continuous evolution ability in complex solution space. 3. This invention integrates the Levy flight predator-attack strategy and uses the random step size generated by the Mantegna algorithm that conforms to the long-tail distribution characteristics to replace the linear translation component in the original algorithm. The non-uniform variable step size feature of the Levy flight, which alternates between long and short step sizes, enables the search agent to perform long-distance cross-regional jumps and short-distance fine searches in the vast PID parameter solution space. This expands the traversal range of the algorithm in the three-dimensional solution space composed of proportional coefficients, integral coefficients, and differential coefficients, and reduces the probability of the algorithm missing the region where the global optimal solution is located. 4. This invention incorporates a chaotic micro-weaving strategy, utilizing the ergodicity and randomness of the Tent chaotic map to generate a chaotic perturbation factor, replacing the single cosine rolling mechanism in the original algorithm. This applies chaotic perturbation to the current solution during the local development phase, enabling the algorithm to perform high-density traversal search in the neighborhood of candidate solutions and improving the algorithm's accuracy in discovering the global optimal control parameters, thereby reducing the steady-state error of the adhesive temperature control system. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of the adhesive temperature control optimization method of the present invention.

[0012] Figure 2 This is a comparison of the optimal fitness convergence curves of the side-hand-turning spider optimization algorithm of the present invention and the improved side-hand-turning spider optimization algorithm.

[0013] Figure 3 This is a comparison of the step response curves of the side-flip spider optimization algorithm of the present invention and the improved side-flip spider optimization algorithm.

[0014] Figure 4 This is a comparison chart of the PID parameter search trajectories of the side-hand-turning spider optimization algorithm of the present invention and the improved side-hand-turning spider optimization algorithm. Detailed Implementation

[0015] Example 1: As Figure 1 As shown, the present invention provides a method for optimizing the temperature control of the gelatin solution in the processing of donkey-hide gelatin, comprising the following steps: S1. Construct a closed-loop PID control system for the temperature of the gelatin solution during the gelatin boiling process, including a gelatin solution temperature error calculation module, a gelatin solution temperature PID controller module, an improved side-hand-turning spider optimization algorithm module, a gelatin solution temperature regulation execution module, and a gelatin solution temperature real-time monitoring module. The gelatin solution temperature monitoring module collects historical operating data and establishes a transfer function model for the gelatin solution temperature control system. The improved side-hand-turning spider optimization algorithm module performs iterative optimization based on the transfer function model, calculating the PID controller parameters in a virtual simulation environment. After the iteration termination condition is met, the best global control parameters obtained by optimization are output and configured into the adhesive temperature PID controller module. The adhesive temperature PID controller module calculates the precise control quantity based on the real-time temperature error signal and the configured best control parameters, and outputs the control quantity to the adhesive temperature adjustment module to drive the actuator to precisely adjust the adhesive temperature. S2. Configure the improved side-hand-turning spider optimization algorithm as the core of parameter tuning. The specific algorithm improvement strategy is configured as follows: S21. For the global exploration stage where individual spiders are stuck in deep stagnation, a spider web vibration triangulation strategy is embedded. The individual position is reconstructed by introducing the mutation mechanism of the differential evolution operator. The vector difference information between the population is used to guide the stagnant individual to perform directional jumps, so as to enhance the algorithm's ability to escape local extreme value traps in complex fitness landscapes. S22. For spiders in a normal exploration state, the Levy flight predation and attack strategy is integrated. The long-tailed distribution random step size generated by the Mantegna algorithm is used to replace the linear translation component, and a translation guidance term with non-uniform variable step size characteristics is constructed to improve the global traversal efficiency and coverage of the algorithm in the broad PID parameter solution space. S23. For the local development stage where individual spiders have sufficient energy, a chaotic silk-spinning micro-weaving strategy is implanted. The ergodicity and randomness of the Tent chaotic mapping are used to generate chaotic perturbation factors, and the current solution is refined for search and neighborhood fine-tuning to improve the algorithm's accuracy and convergence speed in the global optimal solution region. S3. Execute the adaptive tuning process for the PID control parameters, and use the improved side-flipping spider optimization algorithm to iteratively optimize the control parameter space of the adhesive temperature PID control system to obtain the optimal control parameter vector that balances system response speed and stability. , , ; S4. Map the globally optimal control parameter vector obtained through optimization to the PID controller module for the temperature of the donkey-hide gelatin solution, update the internal parameters of the controller, thereby achieving precise adjustment and anti-disturbance control optimization of the temperature of the gelatin solution during the donkey-hide gelatin cooking process.

[0016] In step S3, the improved side-hand-turning spider optimization algorithm is used to iteratively optimize the control parameter space of the gelatin liquid temperature PID control system to obtain the optimal PID control parameter vector of the gelatin liquid temperature control system. Specifically, it includes the following steps: Step 1: Initialization of PID parameter optimization for the PID control system for donkey-hide gelatin liquid temperature; Step 11: Optimization objective function of the PID control system for the temperature of donkey-hide gelatin solution. The mathematical expression is: , In the formula This represents the total simulation duration and is set to 2500 seconds. For time, To determine the real-time deviation between the set temperature and the actual adhesive temperature, This represents the actual overshoot of the system's step response. The maximum allowable overshoot threshold is set to 2.0. The penalty weight for violating the constraint is set to 100, where the objective function is... A smaller function value indicates better control performance; Simultaneously set the population size and maximum number of iterations ; Step 12: Considering the high viscosity and large time delay characteristics of the donkey-hide gelatin liquid during the boiling process, and combining the static gain characteristics of the controlled object, set the upper bound vector of the search space for the PID control parameters. = With the lower bound vector of the search space = ; Step 13: Initialize the key hyperparameters of the improved algorithm; Differential Evolution Scaling Factor Set to 0.5, Levi's Flight Index Set to 1.5 for the number of iterations of the chaotic local search. Set it to 5, and also set the side handspring strength coefficient. The rotation range coefficient is 0.6. The scaling factor for the Levy flight step is 0.1. It is 0.01; Step 14: Randomly initialize within the search space... Spider population location of PID control parameters The initial energy level of each spider Set it to 1.0 to set the stall counter. Set to 0; Step 15: Calculate the fitness value of each individual in the initial population, and mark the individual with the best fitness as the initial globally optimal PID parameter scheme. .

[0017] Step 2: Iterative optimization stage of spider population driven by hybrid strategies; Step 21: Start the main loop; Current iteration number From 1 to Calculate the exploration factor that changes dynamically over time. and development factors , , , In the formula Indicates the current iteration number. Indicates the maximum number of iterations; Step 22: Iterate through every individual spider in the population. Calculate the threshold that triggers the behavior transition. , , In the formula Indicates the first The current energy level of each individual; Step 23: Generate a random number ,if Less than If so, enter global exploration mode and execute step 24; otherwise, enter local development mode and execute step 25. Step 24: Implement the improved side handspring global exploration strategy; Step 241: Check the current individual's stagnation counter. ,if If the value is greater than 5, then the spider web vibration triangulation positioning strategy will be executed; Among them, the spiderweb vibration triangulation strategy utilizes population differences to guide individuals out of stagnation, and the calculation formula is as follows: , In the formula For the new location, For the positions of three distinct individuals randomly selected from the population, This is the difference scaling factor; Step 242, if If the value is less than or equal to 5, then the Levi flight predator-prey attack strategy will be executed; First, based on the Mantegna algorithm, each dimension of the PID parameters is analyzed. Calculate the Levy flight step size components independently, using the following formula: , , In the formula For gamma function, The value is 1.5. and For dimensions Independently generated random numbers that follow a normal distribution; From each component Composition of three-dimensional column vectors Then update the individual's location; the new location is shown below. The formula is: , In the formula For the current individual position, It is a random number. and This refers to the upper and lower bound vectors of the search space for the corresponding dimension, and the multiplication operation is performed dimension-by-dimensionally. These are the coefficients set above, with the following symbols: This represents element-wise multiplication of vectors along corresponding dimensions; Step 243: Analyze the newly generated position. Perform boundary constraint processing to ensure lie in Within the range; Step 25: Implement the improved rolling partial development strategy; Step 251: Check the current individual's energy level. ,if If the value is less than 0.2, an energy-restorative dormancy adjustment strategy will be implemented, and the new position will be determined. The formula is: , In the formula The optimal position globally; Step 252, if If the value is greater than or equal to 0.2, then the chaotic micro-weaving strategy is executed; First, randomly initialize the chaotic variables. , Belongs to the interval Ensure initial values The values ​​are not equal to 0.25, 0.5, or 0.75 to avoid a fixed point; Subsequently The iteration formula for the Tent mapping is as follows: (The text is incomplete and requires further context.) , In the formula For the first The values ​​of the chaotic variables in the next iteration; Candidate solutions are generated based on the updated chaotic variables, and the calculation formula is as follows: , In the formula This is the current local optimum. The radius of the chaotic search; in In the formula The development factor is the factor that increases with the number of iterations. and Let be the upper and lower bound vectors of the search space, respectively. This is the base scaling factor for the search radius, and its value is 0.05. if Better than Then update the local optimum, and the final output is ; Step 26: Evaluate the new location fitness value ; if Superior to the old fitness If the new solution is accepted, energy is increased and the stagnation count is reset. The calculation formula is as follows: , ; At the same time if If the fitness is better than the global best, then update. ; Step 27, if Not better than If the old solution is retained, the energy is reduced and the stagnation count is accumulated. The calculation formula is: , ; Step 28: If an individual has performed step 251 in the current iteration, then after completing the evaluation and state update, force the individual's energy to be reset to [value missing]. .

[0018] Step 3: Optimal temperature control parameter output stage; Step 31: Determine if the maximum number of iterations has been reached. If the target is not reached, return to step 21 and continue to the next iteration; Step 32: If the termination condition is met, output the global optimal position. , This is the optimal PID control parameter vector for the temperature control system of donkey-hide gelatin solution. .

[0019] Based on the high viscosity and thermal inertia characteristics of the gelatin solution during the gelatin boiling process, a transfer function of the controlled object in the PID control system for gelatin solution temperature is constructed. The mathematical expression is: , In the formula It is the Laplace operator, after system identification. This represents the system's static gain and has a value of 50.0. This represents the system time constant and has a value of 500.0. This represents the pure time delay of the system and has a value of 30.0.

[0020] Correspondingly, the control law of the PID controller for the temperature of donkey-hide gelatin solution is... It adopts a parallel structure, and its time-domain expression is: , In the formula To account for the deviation between the set temperature and the actual output temperature, This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients; these three parameters constitute the decision variable vector for algorithm optimization. .

[0021] To verify the difference in performance between the side-turning spider optimization algorithm and the improved side-turning spider optimization algorithm in optimizing PID parameters for a donkey-hide gelatin liquid temperature control system, the project code was implemented in the Matlab environment, and the general operating parameters of the optimization algorithm and population size were set. The value is 50, representing the maximum number of iterations. The value was set to 200, and the random number seed rng was set to 1 to ensure the reproducibility of the comparative experiment. The side-hand-turning spider optimization algorithm and the improved side-hand-turning spider optimization algorithm were run under the same experimental environment, and the optimal fitness convergence curve and the optimal PID parameter solution were recorded. And the corresponding step response curve; like Figure 2The comparison of the best fitness convergence curves shown in the figure reveals that the final fitness value of the improved side-hand-flipping spider optimization algorithm is 1016.9036, which is significantly lower than the 4336.4848 of the side-hand-flipping spider optimization algorithm. The lower fitness value directly proves that the improved side-hand-flipping spider optimization algorithm has higher optimization accuracy. The improved side-hand-flipping spider optimization algorithm shows a sharp downward trend in the early stages of iteration within about 20 generations, quickly approaching the global optimum, demonstrating strong evolutionary pressure and exploration ability. In contrast, the side-hand-flipping spider optimization algorithm has a slower convergence speed and eventually falls into a local optimum trap, which is premature convergence and fails to effectively explore a better parameter space. like Figure 3 The comparison of step response curves shown in the figure demonstrates that the system response after tuning with the improved side-flip spider optimization algorithm almost achieves "quasi-overshoot-free" characteristics, with a smooth waveform. In contrast, the response curve corresponding to the side-flip spider optimization algorithm exhibits overshoot. The significant overshoot, accompanied by obvious oscillations, indicates that the parameter combination failed to effectively balance the damping characteristics of the system. Furthermore, the improved side-handrolling spider optimization algorithm system... The system enters a steady-state range within a certain timeframe, but due to severe oscillations, the adjustment time of the side-hand-turning spider optimization algorithm is significantly delayed, resulting in poor dynamic recovery performance of the system. like Figure 4 The comparison chart of PID parameter search trajectories shown illustrates the optimization process. Driven by the improved side-hand-turning spider optimization algorithm, the three dimensions exhibit good collaborative convergence characteristics. In the later stages of iteration, the parameter search trajectory of the improved side-hand-turning spider optimization algorithm quickly tends to a stable straight line, verifying that the algorithm has a strong local fine-tuning ability in the later stages, ensuring the numerical stability of the final output results. In summary, the improved side-flip spider optimization algorithm surpasses the traditional side-flip spider optimization algorithm in convergence speed, optimization accuracy, and overshoot suppression and adjustment speed of the controlled system. The improved algorithm effectively overcomes the side-flip spider optimization algorithm's tendency to get trapped in local optima, and the optimal PID control parameter vector output is... =[0.2463,0.0005,2.8423], which enables relatively better control over the temperature of the donkey-hide gelatin solution.

Claims

1. A method for optimizing the temperature control of the gelatin solution used in the processing of donkey-hide gelatin, characterized in that: Includes the following steps: S1. Construct a closed-loop PID control system for the temperature of the gelatin solution during the gelatin boiling process, including a gelatin solution temperature error calculation module, a gelatin solution temperature PID controller module, an improved side-hand-turning spider optimization algorithm module, a gelatin solution temperature regulation execution module, and a gelatin solution temperature real-time monitoring module. S2. Configure the improved side-hand-turning spider optimization algorithm as the core of parameter tuning. The specific algorithm improvement strategy is configured as follows: S21. For the global exploration stage where individual spiders are stuck in deep stagnation, a spider web vibration triangulation strategy is embedded. The individual position is reconstructed by introducing the mutation mechanism of the differential evolution operator. The vector difference information between the population is used to guide the stagnant individual to perform directional jumps, so as to enhance the algorithm's ability to escape local extreme value traps in complex fitness landscapes. S22. For spiders in a normal exploration state, the Levy flight predation and attack strategy is integrated. The long-tailed distribution random step size generated by the Mantegna algorithm is used to replace the linear translation component, and a translation guidance term with non-uniform variable step size characteristics is constructed to improve the global traversal efficiency and coverage of the algorithm in the broad PID parameter solution space. S23. For the local development stage where individual spiders have sufficient energy, a chaotic silk-spinning micro-weaving strategy is implanted. The ergodicity and randomness of the Tent chaotic mapping are used to generate chaotic perturbation factors, and the current solution is refined for search and neighborhood fine-tuning to improve the algorithm's accuracy and convergence speed in the global optimal solution region. S3. Execute the adaptive tuning process for the PID control parameters, and use the improved side-flipping spider optimization algorithm to iteratively optimize the control parameter space of the adhesive temperature PID control system to obtain the optimal control parameter vector that balances system response speed and stability. , , During the algorithm iteration process, the spider web vibration triangulation localization strategy, the Levy flight predator-prey attack strategy, and the chaotic silk-spinning micro-weaving strategy are adaptively switched and scheduled based on individual energy levels, stagnation counters, exploration factors, and behavior transition thresholds. Specifically, in each iteration, the exploration factor that dynamically changes over time is calculated. and according to the first Current energy level of an individual Calculate the behavior conversion threshold Generate random numbers ,when The individual enters global exploration mode when the timer is active, otherwise it enters local development mode; in global exploration mode, when the individual's stall counter... The spiderweb vibration triangulation positioning strategy is executed when... The Levi flight predator-snatching strategy is executed at the specified time. In the localized development model, when the individual's energy level When an energy-restorative dormancy adjustment strategy is implemented, The chaotic microweaving strategy is executed; after completing the position update and fitness evaluation, if the fitness value of the new solution is better than that of the old solution, the energy of that individual is increased. And reset the stall counter to Simultaneously, the global best position is updated when the fitness value of the new solution is better than that of the global best solution; if the fitness value of the new solution is not better than that of the old solution, the energy of that individual is reduced. And increment the stall counter Furthermore, individuals who implemented the energy-restorative dormancy adjustment strategy were forcibly reset to their energy levels after evaluation. ; S4. Map the globally optimal control parameter vector obtained through optimization to the PID controller module for the temperature of the donkey-hide gelatin solution, update the internal parameters of the controller, thereby achieving precise adjustment and anti-disturbance control optimization of the temperature of the gelatin solution during the donkey-hide gelatin cooking process.

2. The method for optimizing the temperature control of the gelatin solution in the processing of donkey-hide gelatin according to claim 1, characterized in that: In the PID control system for the temperature of the gelatin liquid in the donkey-hide gelatin processing constructed in step S1, historical operating data is collected through the gelatin liquid temperature monitoring module, and a transfer function model of the gelatin liquid temperature control system is established. The improved side-hand-turning spider optimization algorithm module performs iterative optimization based on the transfer function model and calculates the PID controller parameters in a virtual simulation environment. After the iteration termination condition is met, the optimal global control parameters obtained by optimization are output and configured into the adhesive temperature PID controller module. The adhesive temperature PID controller module calculates the precise control quantity based on the real-time temperature error signal and the configured optimal control parameters, and outputs the control quantity to the adhesive temperature adjustment module to drive the actuator to accurately adjust the adhesive temperature.

3. The method for optimizing the temperature control of the gelatin solution in the processing of donkey-hide gelatin according to claim 1, characterized in that, The spiderweb vibration triangulation strategy in step S21 is as follows: three distinct individuals are randomly selected from the current population, and their position vectors are denoted as follows: , and ; Based on the principle of differential evolution, using scaling factors The differences between the last two individuals are weighted and then added to the first individual to generate a new position. The calculation formula is as follows: In the formula For the generated new position vector, Let the vectors be the position vectors of three distinct individuals randomly selected from the current population. This is the scaling factor for differential evolution.

4. The method for optimizing the temperature control of the gelatin solution in the processing of donkey-hide gelatin according to claim 1, characterized in that, The Levy flight predation and attack strategy in step S22 is specifically as follows: based on the Mantegna algorithm, using the Levy index... Generate a random step-size vector that follows a Lévy distribution. ; Calculate the rotational components , In the formula For rotational components, For the intensity of a side handspring, for Random numbers between and Let be the upper and lower bound vectors of the search space, respectively. This is the rotation range coefficient; Using Lévy step size vector The translation guiding term is calculated using the following formula: In the formula As a translation guide item, This is the scaling factor for Levy's flight step size. Let Lévy's flight random step size vector be . This is the current global optimal position. The current position, symbol This represents element-wise multiplication of vectors along corresponding dimensions; it also adds the rotation component and the Lévy translation component to the current position. Up, complete the location update.

5. The method for optimizing the temperature control of the gelatin solution in the processing of donkey-hide gelatin according to claim 1, characterized in that, The chaotic micro-weaving strategy in step S23 specifically involves: initializing chaotic variables. And set the number of internal search iterations. ; Calculate the development factor Dynamically adjusted current chaotic search radius The calculation formula is: In the formula The radius of the chaotic search is... The development factor is the factor that increases with the number of iterations. and Let be the upper and lower bound vectors of the search space, respectively. The basic scaling factor for the search radius; in In the next iteration, the chaotic variables are iteratively updated using the Tent mapping formula. It utilizes its traversal properties to generate a more uniformly distributed perturbation factor; based on the current local optimum position The candidate solution is generated using the following formula: ， If a candidate solution has better fitness, the local position is updated immediately.

6. The method for optimizing the temperature control of the gelatin solution in the processing of donkey-hide gelatin according to claim 1, characterized in that, The optimization objective function of the PID control system for the temperature of donkey-hide gelatin solution The mathematical expression is: ， In the formula Indicates the total simulation duration. For time, To determine the real-time deviation between the set temperature and the actual adhesive temperature, This represents the actual overshoot of the system's step response. The maximum allowable overshoot threshold. The penalty weight for violating the constraint.

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