Heat collecting coil temperature adjusting device and control method thereof

Abstract

The invention provides a heat collecting coil temperature adjusting device and a control method thereof, and relates to the field of temperature adjusting equipment, and the heat collecting coil temperature adjusting device comprises a temperature sensor, a controller, a heating module and a cooling module. The temperature sensor is installed at a key temperature measuring point of the heat collecting coil, the measuring precision is + / -0.1 DEG C, and data are collected according to the characteristics of a thermistor and wirelessly transmitted to the controller. The controller receives the data, calculates deviation and change rate according to a preset target, and obtains a control quantity by using a fuzzy adaptive control algorithm; the heating module adjusts current according to control quantity to change power temperature rise, and the cooling module adjusts rotating speed of a water pump to control flow speed temperature reduction. The control method comprises the steps of target presetting, data acquisition and transmission, deviation and fuzzy control quantity calculation, temperature adjustment, parameter adjustment and the like, fuzzy control rule parameters are optimized through a genetic algorithm, and the problems that an existing device is insufficient in precision, complex in structure, inflexible in control strategy and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature regulation equipment, and particularly relates to a hot coiling temperature regulation device and a control method thereof. Background Art

[0002] In many industrial production processes, the temperature control of hot coiling is crucial. At present, the existing temperature regulation devices either have insufficient regulation accuracy and cannot meet the requirements of high-precision production, or have complex structures, high costs, and difficult maintenance. For example, some traditional temperature regulation devices use a simple combination of heating wires and fans, with a slow response speed in temperature control and unable to quickly adapt to the dynamic adjustment requirements of the hot coiling temperature during the production process. Moreover, most of the existing control methods are control strategies based on fixed parameters and cannot be flexibly adjusted according to the real-time working conditions of the hot coiling, resulting in large temperature fluctuations and affecting the consistency and stability of products. Summary of the Invention

[0003] In view of this, the present invention provides the purpose and efficacy of a hot coiling temperature regulation device, specifically including: a temperature sensor, a controller, a heating module, and a cooling module; characterized in that:

[0004] The temperature sensor is closely attached and installed at the key temperature measurement points on the surface of the hot coiling, with a measurement accuracy of up to ±0.1 °C, for real-time collecting the temperature data of the hot coiling and accurately transmitting the temperature data to the controller through a wireless transmission method; the temperature acquisition principle is based on the characteristic that the resistance value of the thermistor changes with temperature, following the formula R = R 0 (1 + α(T - T 0 ))), where R is the resistance value at temperature T, R 0 is the resistance value at the reference temperature T 0 and α is the temperature coefficient of the thermistor;

[0005] The controller receives the temperature data transmitted by the temperature sensor, quickly calculates the temperature deviation and the rate of change of the temperature deviation according to the preset target temperature of the hot coiling and the temperature data, and adopts a fuzzy adaptive control algorithm to obtain the control quantity according to the temperature deviation and the rate of change of the temperature deviation; in the implementation of the fuzzy control algorithm, the code is as follows:

[0006] # Define the input and output variable ranges of the fuzzy controller

[0007] e_range = [-5, 5]

[0008] ec_range = [-5, 5]

[0009] u_range = [-10, 10]

[0010] # Define the fuzzy sets and membership functions

[0011] def define_fuzzy_sets():

[0012] e_fuzzy_sets = {

[0013] 'NB': lambda x: 1 if x <= -3 else (1 - (x + 3) / 2) if -3 < x < -1 else 0,

[0014] 'NS': lambda x: 1 if -2 <= x < 0 else (1 - x / 2) if 0 <= x < 2 else 0,

[0015] 'ZE': lambda x: 1 if -1 <= x <= 1 else 0,

[0016] 'PS': lambda x: 1 if 0 < x <= 2 else (1 - (x - 2) / 2) if 2 < x < 4 else 0,

[0017] 'PB': lambda x: 1 if x >= 3 else (1 - (x - 3) / 2) if 1 < x < 3 else 0

[0018] }

[0019] ec_fuzzy_sets = {

[0020] 'NB': lambda x: 1 if x <= -3 else (1 - (x + 3) / 2) if -3 < x < -1 else 0,

[0021] 'NS': lambda x: 1 if -2 <= x < 0 else (1 - x / 2) if 0 <= x < 2 else 0,

[0022] 'ZE': lambda x: 1 if -1 <= x <= 1 else 0,

[0023] 'PS': lambda x: 1 if 0 < x <= 2 else (1 - (x - 2) / 2) if 2 < x < 4 else 0,

[0024] 'PB': lambda x: 1 if x >= 3 else (1 - (x - 3) / 2) if 1 < x < 3 else 0

[0025] }

[0026] u_fuzzy_sets = {

[0027] 'NB': lambda x: 1 if x <= -8 else (1 - (x + 8) / 4) if -8 < x < -4 else 0,

[0028] 'NS': lambda x: 1 if -6 <= x < -2 else (1 - (x + 2) / 4) if -2 <= x < 2 else 0,

[0029] 'ZE': lambda x: 1 if -2 <= x <= 2 else 0,

[0030] 'PS': lambda x: 1 if 2 < x <= 6 else (1 - (x - 2) / 4) if 6 < x < 10 else 0,

[0031] 'PB': lambda x: 1 if x >= 8 else (1 - (x - 8) / 4) if 4 < x < 8 else 0

[0032] }

[0033] return e_fuzzy_sets, ec_fuzzy_sets, u_fuzzy_sets

[0034] # Fuzzy rule base

[0035] rules =

[0036] ('NB', 'NB', 'PB'),

[0037] ('NB', 'NS', 'PB'),

[0038] ('NB', 'ZE', 'PS'),

[0039] ('NB', 'PS', 'PS'),

[0040] ('NB', 'PB', 'ZE'),

[0041] ('NS', 'NB', 'PB'),

[0042] ('NS', 'NS', 'PS'),

[0043] ('NS', 'ZE', 'PS'),

[0044] ('NS', 'PS', 'ZE'),

[0045] ('NS', 'PB', 'NS'),

[0046] ('ZE', 'NB', 'PS'),

[0047] ('ZE', 'NS', 'PS'),

[0048] ('ZE', 'ZE', 'ZE'),

[0049] ('ZE', 'PS', 'NS'),

[0050] ('ZE', 'PB', 'NS'),

[0051] ('PS', 'NB', 'PS'),

[0052] ('PS', 'NS', 'ZE'),

[0053] ('PS', 'ZE', 'NS'),

[0054] ('PS', 'PS', 'NS'),

[0055] ('PS', 'PB', 'NB'),

[0056] ('PB', 'NB', 'ZE'),

[0057] ('PB', 'NS', 'NS'),

[0058] ('PB', 'ZE', 'NS'),

[0059] ('PB', 'PS', 'NB'),

[0060] ('PB', 'PB', 'NB')

[0062] deffuzzy_control(e, ec):

[0063] e_fuzzy_sets, ec_fuzzy_sets, u_fuzzy_sets = define_fuzzy_sets()

[0064] # Calculate the membership degrees of input variables

[0065] e_memberships = {k: v(e) for k, v in e_fuzzy_sets.items()}

[0066] ec_memberships = {k: v(ec) for k, v in ec_fuzzy_sets.items()}

[0067] # Fuzzy inference

[0068] u_memberships = {} ​

[0069] for rule in rules:

[0070] e_label, ec_label, u_label = rule

[0071] w = min(e_memberships[e_label], ec_memberships[ec_label])

[0072] if u_label not in u_memberships or w > u_memberships[u_label]:

[0073] u_memberships[u_label] = w

[0074] # Defuzzification (using the centroid method)

[0075] numerator = 0

[0076] denominator = 0

[0077] for u_label, w in u_memberships.items():

[0078] u_values = [x for x in range(u_range[0], u_range[1] + 1)]

[0079] numerator += sum([w * u_fuzzy_sets[u_label](x) * x for x in u_values])

[0080] denominator += sum([w * u_fuzzy_sets[u_label](x) for x in u_values])

[0081] u = numerator / denominator if denominator != 0 else 0

[0082] return u;

[0083] The heating module adjusts the heating power by adjusting the magnitude of its own supply current according to the control quantity output by the controller, so as to increase the temperature of the hot coiling; the calculation formula of its heating power is P = I 2 R, where P is the heating power, I is the current passing through the resistance heating wire, and R is the resistance value of the resistance heating wire;

[0084] The cooling module adjusts the cooling water flow rate by controlling the rotational speed of the circulating water pump according to the control quantity output by the controller, so as to reduce the temperature of the hot coiling reel; the heat dissipation efficiency of the cooling module is related to the cooling water flow rate and the heat dissipation area, and its heat dissipation formula is Q = hAΔT, where Q is the heat dissipation quantity, h is the convective heat transfer coefficient, A is the heat dissipation area, and ΔT is the temperature difference between the hot coiling reel and the cooling medium.

[0085] Further, the temperature sensor has a self-calibration function and automatically performs calibration once every 4 hours; when the calibration is started, the built-in calibration circuit will automatically switch to the calibration mode, connect the reference resistor to the measurement circuit, and obtain the resistance value R corresponding to the reference resistor. ref ; The temperature sensor continuously collects 20 groups of temperature data within 10 minutes at a preset sampling frequency, and each group of data contains the temperature values of 5 measurement points; through the least squares fitting algorithm, the collected temperature values T i are substituted into the calibration curve equation R = aT + b, where a and b are parameters to be fitted, and an error function is constructed. i By taking the partial derivatives of the error function and setting them to zero, the values of a and b are solved to obtain an accurate calibration curve, ensuring the accuracy of the measurement data during long-term use.

[0086] Further, the heating module is equipped with an intelligent power regulation system; this system real-time monitors the supply current and voltage of the heating module through the built-in high-precision current sensor and voltage sensor, and accurately calculates the current heating power according to the formula P = UI, where U is the supply voltage and I is the supply current; when it is detected that the temperature rise rate of the hot coiling reel deviates from the preset range, the intelligent power regulation system will automatically adjust the power supply mode of the heating module; the preset temperature rise rate range is set according to different hot coiling reel materials and production process requirements; when the temperature rises too slowly, the system adopts a segmented incremental power supply strategy through the control circuit; in the initial stage, it supplies power at a lower power P 1 which is set to 30% of the maximum power P of the heating module, 1 to avoid stress damage to the hot coiling reel due to sudden temperature changes; as the temperature of the hot coiling reel gradually approaches the target value, when the temperature reaches 40% of the target temperature, the power supply power is increased to P max which is 60% of P 2 ; when the temperature reaches 70% of the target temperature, the power supply power is further increased to P 2 which is 80% of P max until an appropriate heating rate is achieved. 3 3 max

[0087] ​​​​The intelligent power regulation system dynamically adjusts the power supply frequency according to the material characteristics and current temperature of the hot coiling reel; by using the skin effect principle, the power supply frequency is appropriately increased in the high-temperature section (when the temperature of the hot coiling reel reaches 80% or more of the target temperature).

[0088] Furthermore, the cooling module adopts the phase change material assisted cooling technology; the internal space of the cooling module is filled with a phase change material with high latent heat, and its phase change temperature range precisely matches the normal operating temperature range of the hot coiling reel; when the temperature of the hot coiling reel rises, the cooling water flows through the cooling module, heating the phase change material to the phase change temperature, and the phase change material changes from solid state to liquid state, absorbing a large amount of heat during this process, greatly enhancing the heat dissipation capacity of the cooling module; by setting a flow sensor and a temperature sensor at the inlet and outlet of the cooling module, the flow rate and the inlet and outlet temperature difference of the cooling water are monitored in real time, and using the formula Q = mcΔT, where m is the mass of the cooling water flow, c is the specific heat capacity of water, and ΔT is the inlet and outlet temperature difference of the cooling water flow, the real-time heat dissipation of the cooling module is accurately calculated; according to the calculation results, the controller dynamically adjusts the rotation speed of the circulating water pump to ensure that the cooling module always maintains an efficient heat dissipation state, effectively improving the stability and response speed of the hot coiling reel temperature regulation.

[0089] Furthermore, the database management system adopted by the data storage module has data encryption and backup functions; during the data storage process, for sensitive data in the temperature data record table and the control parameter record table, such as the temperature setting value corresponding to the key production process, important control parameters, etc., AES algorithm is used for encryption processing; the AES algorithm is based on the Rijndael algorithm and supports key lengths of 128 bits, 192 bits, and 256 bits; its encryption process mainly includes initial key expansion and multiple rounds of encryption operations; the initial key expansion expands the input key into multiple round keys for subsequent encryption rounds; in each round of encryption, byte substitution, row shift, column confusion, and round key addition operations are performed in sequence; byte substitution realizes non-linear transformation by looking up the S box to enhance the security of the cipher; row shift circularly shifts each row of bytes by different offsets; column confusion mixes each column of bytes through matrix multiplication; round key addition performs exclusive OR operation on the current round of data and the corresponding round key; after multiple rounds of encryption, ciphertext data is obtained to ensure the security of data during storage and transmission;

[0090] An example code implementation of the AES algorithm in Python is as follows:

[0091] from Crypto.Cipher import AES

[0092] from Crypto.Util.Padding import pad, unpad

[0093] from Crypto.Random import get_random_bytes

[0094] # Generate a random AES key, 16 bytes (128 bits) in length, can be adjusted to 24 bytes (192 bits) or 32 bytes (256 bits) according to requirements

[0095] key = get_random_bytes(16)

[0096] def encrypt_data(data):

[0097] cipher = AES.new(key, AES.MODE_CBC)

[0098] ct_bytes = cipher.encrypt(pad(data.encode('utf-8'), AES.block_size))

[0099] return cipher.iv + ct_bytes

[0100] def decrypt_data(ct):

[0101] iv = ct[:AES.block_size]

[0102] ct = ct[AES.block_size:]

[0103] cipher = AES.new(key, AES.MODE_CBC, iv)

[0104] pt = unpad(cipher.decrypt(ct), AES.block_size)

[0105] return pt.decode('utf-8')

[0106] # Example data encryption and decryption

[0107] original_data = "The temperature setting value corresponding to the key production process: 100℃"

[0108] encrypted_data = encrypt_data(original_data)

[0109] decrypted_data = decrypt_data(encrypted_data)

[0110] print(f"Original data: {original_data}")

[0111] print(f"Encrypted data: {encrypted_data.hex()}")

[0112] print(f"Decrypted data: {decrypted_data}")

[0113] Meanwhile, the database management system automatically performs a full backup of the data at 2:00 am every day at the set time interval and stores the backup data in redundant storage devices in a different location; when the primary storage device fails, the data recovery operation can be completed within 30 minutes to ensure the integrity and availability of the temperature data and control parameters, and the storage duration is not less than 3 months.

[0114] The present invention provides the purpose and efficacy of a control method for a hot coiling temperature adjustment device, specifically including the following steps:

[0115] Step 1: Target temperature presetting: On the operation interface of the controller or through the upper computer software connected thereto, according to different product production process standards, enter the hot coiling target temperature in the temperature setting window; the numerical range of this target temperature is determined according to actual production requirements, between 50°C and 150°C, and can be accurate to one decimal place; after input is completed, the controller stores this target temperature value in the internal non-volatile memory for subsequent calling at any time during the control process;

[0116] Step 2: Temperature data acquisition and transmission: The temperature sensor uses the method of timer interruption to trigger a data acquisition operation once every 5 seconds as a fixed period; during each acquisition, the A / D conversion circuit inside the temperature sensor converts the analog signal sensed by the thermistor into a digital signal and performs a series of signal processing operations, including denoising, amplification, etc., to ensure the accuracy of the data; the processed temperature data is transmitted to a specific data receiving pin of the controller through the SPI communication protocol via a wired connection method;

[0117] Step 3: Deviation calculation: After the controller receives the real-time temperature data transmitted by the temperature sensor, it reads the preset hot coiling target temperature from the internal memory; uses the formula e = T set -T real to calculate the temperature deviation e, where T set is the target temperature and T real is the real-time temperature; to ensure the calculation accuracy, the controller uses fixed-point number arithmetic or floating-point number arithmetic methods, which are configured according to its hardware resources and performance requirements; subsequently, read the temperature deviation e prev, combined with the time interval Δt of this acquisition being 5 seconds, use the formula Calculate the temperature deviation change rate ec; Fuzzy control quantity calculation: Take the calculated temperature deviation e and temperature deviation change rate ec as the input quantities of the fuzzy controller; First, according to the fuzzy sets and membership functions defined in claim 1, calculate the membership degrees of e and ec for each fuzzy subset, such as "NB", "NS", "ZE", "PS", "PB". When calculating the membership degree of the temperature deviation e belonging to the "NB" fuzzy subset, call e f uzzy s ets['NB'](e) function to obtain; Then, perform fuzzy inference according to the preset fuzzy rule base; For each fuzzy rule, determine the activation strength of the rule by taking the minimum value of the membership degrees of the input quantities, that is, w = min(e m emberships[elabel], ec m emberships[ec l abel]); After the inference of all rules, obtain the membership degree distribution of the control quantity for each fuzzy subset; Finally, use the centroid method for defuzzification operation, that is, by traversing all discrete values within the control quantity output range u r ange, combined with the membership degrees of the control quantity for each fuzzy subset, calculate the exact control quantity u according to the formula This calculation process is implemented by executing the Python code included in the controller part of claim 1;

[0118] Step Four: Temperature regulation: The controller adjusts the heating power of the heating module and the cooling water flow rate of the cooling module through the control circuit according to the calculated control quantity u; The control circuit uses PWM technology to generate a PWM signal with a specific duty cycle through the PWM generator inside the controller; For the heating module, after the PWM signal is amplified by the drive circuit, it controls the on and off of the solid-state relay or power transistor, thereby adjusting the supply voltage of the resistance heating wire, and further changing the heating power. The relationship between the heating power and the PWM duty cycle is determined by the formula P = P max *D, where P max is the maximum power of the heating module, and D is the PWM duty cycle); For the cooling module, the PWM signal controls the DC motor drive chip to adjust the speed of the circulating water pump motor, thereby changing the cooling water flow rate. The relationship between the cooling water flow rate and the PWM duty cycle is converted through a linearly measured relationship or a non-linear mapping relationship through experiments;

[0119] Step 5: Parameter Adjustment: The controller collects in real time the operating state parameters of the rotational speed and load current of the hot coiling reel and the data transmitted by the environmental temperature and humidity sensors through the internal sensor interface or communication interface, and obtains the operating frequency information through the communication interface of the equipment control system; when it detects that these parameters change, it starts the genetic algorithm to adjust the parameters of the fuzzy control rules; uses the membership function parameters of the fuzzy control rules as genes to construct individuals of the genetic algorithm; uses the stability index of temperature control, such as the standard deviation of the temperature fluctuation range and the accuracy index, such as the mean absolute error between the actual temperature and the target temperature, as the fitness function, and continuously optimizes the parameters through genetic operations such as selection, crossover, and mutation.

[0120] The specific operation process is as follows: First, define the genetic algorithm parameters. The population size is set to 50, the number of iterations is 100, and the population is initialized. The following is the Python code to implement population initialization:

[0121] import random

[0122] # Assume the number of genes for each individual, corresponding to the number of parameters of the fuzzy control rules

[0123] gene_num = 10

[0124] # Gene value range

[0125] gene_range = [-5, 5]

[0126] def initialize_population(population_size, gene_num, gene_range):

[0127] population = []

[0128] for _ in range(population_size):

[0129] individual = [random.uniform(gene_range[0], gene_range[1]) for _ in range(gene_num)]

[0130] population.append(individual)

[0131] return population

[0132] population = initialize_population(50, gene_num, gene_range)

[0133] Then, through the roulette wheel selection method, individuals with higher fitness are selected by the roulette_wheel_selection function; the following is the implementation code of the roulette_wheel_selection function:

[0134] def calculate_fitness(individual):

[0135] # Here, the fitness function needs to be defined according to the actual situation. For example, use the stability index of temperature control (such as the standard deviation of the temperature fluctuation range)

[0136] # and the accuracy index (such as the mean absolute error between the actual temperature and the target temperature) as the fitness function

[0137] # Here is a simple example, return the sum of the individual gene values as the fitness

[0138] return sum(individual)

[0139] def roulette_wheel_selection(population):

[0140] fitness_values = [calculate_fitness(individual) for individual in population]

[0141] total_fitness = sum(fitness_values)

[0142] selection_probs = [fitness / total_fitness for fitness in fitness_values]

[0143] selected_index = random.choices(range(len(population)), weights = selection_probs)[0]

[0144] return population[selected_index]

[0145] selected_individual = roulette_wheel_selection(population)

[0146] Next, single-point crossover is implemented through the single_point_crossover function and uniform mutation is implemented through the uniform_mutation function. The mutation rate is set to 0.01, and operations are performed to generate a new population. The following is the implementation code for these two functions:

[0147] def single_point_crossover(parent1, parent2):

[0148] crossover_point = random.randint(1, len(parent1) - 1)

[0149] child1 = parent1[:crossover_point] + parent2[crossover_point:]

[0150] child2 = parent2[:crossover_point] + parent1[crossover_point:]

[0151] return child1, child2

[0152] def uniform_mutation(individual, mutation_rate, gene_range):

[0153] for i in range(len(individual)):

[0154] if random.random() < mutation_rate:

[0155] individual[i] = random.uniform(gene_range[0], gene_range[1])

[0156] return individual

[0157] parent1 = selected_individual

[0158] parent2 = roulette_wheel_selection(population)

[0159] child1, child2 = single_point_crossover(parent1, parent2)

[0160] child1 = uniform_mutation(child1, 0.01, gene_range)

[0161] child2 = uniform_mutation(child2, 0.01, gene_range). After 100 iterations, the optimized fuzzy control rule parameters are obtained and applied to the subsequent fuzzy control process. The following is the complete iteration process code:

[0162] python

[0163] max_generations = 100

[0164] for generation in range(max_generations):

[0165] new_population = []

[0166] for _ in range(25): # Generate 25 pairs of new individuals

[0167] parent1 = roulette_wheel_selection(population)

[0168] parent2 = roulette_wheel_selection(population)

[0169] child1, child2 = single_point_crossover(parent1, parent2)

[0170] child1 = uniform_mutation(child1, 0.01, gene_range)

[0171] child2 = uniform_mutation(child2, 0.01, gene_range)

[0172] new_population.extend([child1, child2])

[0173] population = new_population

[0174] # Assume the best individual is the one with the highest fitness

[0175] best_individual = max(population, key=calculate_fitness)

[0176] # Apply the optimized parameters to the fuzzy control rules

[0177] # Here, the parameters in best_individual need to be updated to the fuzzy control rules according to the actual situation

[0178] # For example, update the parameters of the membership function, etc.

[0179] Furthermore, during the parameter adjustment process of the fuzzy control rules, the genetic algorithm is used to optimize the parameters of the fuzzy control rules; the population size of the genetic algorithm is set to 50, which means that the initial population contains 50 individuals, and each individual represents a set of membership function parameters of the fuzzy control rules, such as the boundary values and shape parameters of the fuzzy subsets "NB", "NS", "ZE", "PS", "PB"; the number of iterations is set to 100, that is, the genetic algorithm will perform 100 evolutionary operations;

[0180] In the selection operation, the roulette wheel selection method is adopted, and the specific implementation logic is as follows: First, calculate the fitness value of each individual. The fitness function is based on the stability index of temperature control, such as the standard deviation of the temperature fluctuation range, and the accuracy index, such as the mean absolute error between the actual temperature and the target temperature; the fitness of individual i is f i , then the probability of its being selected By randomly generating a number between 0 and 1, according to the probability p i Select the individual with higher fitness to enter the next generation;

[0181] The crossover operation adopts single-point crossover, that is, randomly select two individuals from the population as parents, and randomly select a crossover point in their gene sequences; the gene sequence of individual A is [1, 2, 3, 4, 5], and the gene sequence of individual B is [6, 7, 8, 9, 10]. If the crossover point is selected at the 3rd position, then the gene sequence of the offspring C generated after crossover is [1, 2, 8, 9, 10], and the gene sequence of the offspring D is [6, 7, 3, 4, 5];

[0182] The mutation operation adopts uniform mutation, and the mutation rate is set to 0.01, which means that in each iteration, each gene of each individual has a probability of 0.01 of mutating; when mutating, randomly generate a new value within the value range of the gene to replace the original gene value; for example, the value range of a certain gene is [0, 1], if this gene mutates, then randomly generate a new value between 0 and 1 to replace the original gene value.

[0183] Furthermore, when the hot coiling temperature exceeds the preset safe temperature range, with the upper limit being 160°C and the lower limit being 40°C, the controller immediately triggers the audible and visual alarm system, simultaneously automatically cuts off the power supply of the heating module, and increases the cooling power of the cooling module; the audible and visual alarm system realizes the alarm function by controlling the buzzer and LED lights, and its control logic is jointly realized by the hardware circuit and software program. When it detects that the temperature exceeds the range, the controller outputs a high-level signal to trigger the buzzer to sound and the LED lights to flash.

[0184] Furthermore, the control method further includes the step of regularly detecting the performance of the device. Every 10 production cycles, the performance detection program is automatically run to detect the working states of the temperature sensor, heating module, cooling module, and controller; the performance detection program is carried out by sending test signals, collecting feedback data, etc. For example, a calibration signal is sent to the temperature sensor to detect whether its response is normal; specific control signals are applied to the heating module and cooling module to detect whether their power output and cooling effect meet the expectations.

[0185] Furthermore, the controller is also connected to the production management system, and the hot coiling temperature data and control parameters are uploaded in real time so that production management personnel can perform remote monitoring and data analysis; the data upload uses the MQTT communication protocol, and the data is transmitted to the server of the production management system through the network interface. The server side uses InfluxDB software for data reception and storage, and production management personnel can view the data and perform data analysis in real time through the Web interface or mobile application.

[0186] Beneficial effects

[0187] 1. High-precision temperature control to improve product quality

[0188] Accurate measurement and rapid response: The measurement accuracy of the temperature sensor can reach ±0.1°C, and it can closely and real-time collect data at the key temperature measurement points on the surface of the hot coiling, and quickly transmit the data to the controller through wireless transmission. With the timed interrupt acquisition mechanism with a period of 5 seconds, the subtle changes in the hot coiling temperature can be quickly captured, providing a basis for accurate control.

[0189] Intelligent algorithm optimized control: The fuzzy adaptive control algorithm adjusts the control quantity in real time according to the temperature deviation and the rate of change of the temperature deviation, and dynamically adjusts the heating and cooling modules. Through fuzzy reasoning and defuzzification operations using fuzzy sets, membership functions, and fuzzy rule bases, the hot coiling temperature can be accurately controlled near the set value within the production range of 50°C - 150°C, effectively reducing temperature fluctuations, greatly improving the stability of product quality, and reducing the defective rate.

[0190] 2. Energy-saving and efficient, reducing production costs

[0191] Intelligent regulation of the heating module: The intelligent power regulation system monitors the supply current and voltage in real time and accurately calculates the heating power. When the temperature rises too slowly, a segmented incremental power supply strategy is adopted, starting from an initial power of 30% of the maximum power and gradually increasing to avoid stress damage to the hot coiled strip caused by sudden temperature changes, while increasing the heating efficiency by 30%. In the high-temperature section, the skin effect is utilized to dynamically adjust the supply frequency, making the current more concentrated on the surface of the resistance heating wire, enhancing the heating effect while reducing energy consumption by 20%.

[0192] Efficient heat dissipation of the cooling module: The cooling module adopts the phase change material assisted cooling technology, and the filled high latent heat phase change material precisely matches the working temperature range of the hot coiled strip. A large amount of heat is absorbed during the phase change process. Combining flow and temperature sensors to monitor the heat dissipation in real time, the controller dynamically adjusts the rotation speed of the circulating water pump to ensure that the cooling module always dissipates heat efficiently, improving the stability and response speed of the hot coiled strip temperature regulation and reducing the cooling energy consumption.

[0193] 3. The equipment is stable and reliable, reducing maintenance costs

[0194] Self-calibration of sensors and accurate data: The temperature sensor is automatically calibrated every 4 hours. Through the built-in calibration circuit and reference resistor, combined with the least squares fitting algorithm, the calibration curve is constructed and optimized to ensure the accuracy of long-term measurement data, reduce temperature control errors caused by sensor errors, and extend the service life of the equipment.

[0195] Data security and backup recovery: The data storage module adopts a database management system with encryption and backup functions. Sensitive data is encrypted using the AES algorithm, supporting key lengths of 128 bits, 192 bits, and 256 bits to ensure the security of data during storage and transmission. The data is automatically fully backed up to off-site redundant storage devices at 2 am every day. Data recovery can be completed within 30 minutes in case of failure of the main storage device, and the storage duration is not less than 3 months to ensure the integrity and availability of the equipment operation data, providing strong data support for the stable operation of the equipment.

[0196] 4. Intelligent adaptive control, adapting to complex working conditions

[0197] Genetic algorithm optimization of control rules: The controller real-time collects the operation state parameters of the hot coiled strip, environmental factor data, and equipment operation frequency information. When the parameters change, the genetic algorithm is started to optimize the fuzzy control rules. Using the stability and accuracy indicators of temperature control as the fitness function, through genetic operations with a population size of 50 and 100 iterations, the membership function parameters of the fuzzy control rules are continuously optimized, enabling the device to better adapt to different production working conditions and environmental changes and improving the overall control performance.

[0198] Automatic Alarm and Safety Protection: When the temperature of the hot coiling reel exceeds the upper limit of 160°C or the lower limit of 40°C, the controller immediately triggers the audible and visual alarm system, automatically cuts off the power supply of the heating module, and increases the cooling power. The buzzer and LED lights are controlled alarmingly through the coordination of the hardware circuit and software program to ensure the safety of the equipment and production.

[0199] 5. Convenient Production Management and Improved Production Efficiency

[0200] Remote Monitoring and Data Analysis: The controller is connected to the production management system through the MQTT communication protocol, and the temperature data and control parameters of the hot coiling reel are uploaded in real time. Production management personnel can remotely monitor the operating status of the equipment through the Web interface or mobile application, and use InfluxDB software for data analysis to promptly understand the production situation and make scientific decisions.

[0201] Regular Performance Detection: The performance detection program is automatically run every 10 production cycles to comprehensively detect the temperature sensor, heating module, cooling module, and controller. By sending test signals and collecting feedback data, potential problems can be promptly discovered, facilitating equipment maintenance, reducing maintenance costs, and improving production efficiency. Description of the Drawings

[0202] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly introduced below.

[0203] The drawings in the following description only relate to some embodiments of the present invention and do not limit the present invention.

[0204] In the drawings:

[0205] Figure 1 is the usage flowchart of the hot coiling reel temperature regulation device of the embodiment of the present invention.

[0206] Figure 2 is the block diagram of the hot coiling reel temperature regulation device of the embodiment of the present invention.

[0207] Figure 3 is the control method flowchart of the hot coiling reel temperature regulation device of the embodiment of the present invention. Detailed Embodiment

[0208] The embodiments of the present invention will be further described in detail below in conjunction with the drawings and embodiments.

[0209] Embodiment: Please refer to Figures 1 to 3 as shown:

[0210] The present invention provides a hot coiling reel temperature regulation device, including: a temperature sensor, a controller, a heating module, and a cooling module; characterized in that:

[0211] The temperature sensor is closely and firmly installed at the key temperature measurement points on the surface of the hot coiling reel, with a measurement accuracy of up to ±0.1 °C. It is used to collect the temperature data of the hot coiling reel in real time and accurately transmit the temperature data to the controller through a wireless transmission method; the temperature acquisition principle is based on the characteristic that the resistance value of the thermistor changes with temperature, following the formula R = R 0 (1 + α(T - T 0 ))), where R is the resistance value at temperature T, R 0 is the resistance value at the reference temperature T 0 , and α is the temperature coefficient of the thermistor;

[0212] The controller receives the temperature data transmitted by the temperature sensor, quickly calculates the temperature deviation and the rate of change of the temperature deviation based on the preset target temperature of the hot coiling reel and the temperature data, and adopts a fuzzy adaptive control algorithm to obtain the control quantity according to the temperature deviation and the rate of change of the temperature deviation; in the implementation of the fuzzy control algorithm, the code is as follows:

[0213] # Define the input and output variable ranges of the fuzzy controller

[0214] e_range = [-5, 5]

[0215] ec_range = [-5, 5]

[0216] u_range = [-10, 10]

[0217] # Define the fuzzy sets and membership functions

[0218] def define_fuzzy_sets():

[0219] e_fuzzy_sets = {

[0220] 'NB': lambda x: 1 if x <= -3 else (1 - (x + 3) / 2) if -3 < x < -1 else 0,

[0221] 'NS': lambda x: 1 if -2 <= x < 0 else (1 - x / 2) if 0 <= x < 2 else 0,

[0222] 'ZE': lambda x: 1 if -1 <= x <= 1 else 0,

[0223] 'PS': lambda x: 1 if 0 < x <= 2 else (1 - (x - 2) / 2) if 2 < x < 4 else 0,

[0224] 'PB': lambda x: 1 if x >= 3 else (1 - (x - 3) / 2) if 1 < x < 3 else 0

[0225] }

[0226] ec_fuzzy_sets = {

[0227] 'NB': lambda x: 1 if x <= -3 else (1 - (x + 3) / 2) if -3 < x < -1 else 0,

[0228] 'NS': lambda x: 1 if -2 <= x < 0 else (1 - x / 2) if 0 <= x < 2 else 0,

[0229] 'ZE': lambda x: 1 if -1 <= x <= 1 else 0,

[0230] 'PS': lambda x: 1 if 0 < x <= 2 else (1 - (x - 2) / 2) if 2 < x < 4 else 0,

[0231] 'PB': lambda x: 1 if x >= 3 else (1 - (x - 3) / 2) if 1 < x < 3 else 0

[0232] }

[0233] u_fuzzy_sets = {

[0234] 'NB': lambda x: 1 if x <= -8 else (1 - (x + 8) / 4) if -8 < x < -4 else 0,

[0235] 'NS': lambda x: 1 if -6 <= x < -2 else (1 - (x + 2) / 4) if -2 <= x < 2 else 0,

[0236] 'ZE': lambda x: 1 if -2 <= x <= 2 else 0,

[0237] 'PS': lambda x: 1 if 2 < x <= 6 else (1 - (x - 2) / 4) if 6 < x < 10 else 0,

[0238] 'PB': lambda x: 1 if x >= 8 else (1 - (x - 8) / 4) if 4 < x < 8 else 0

[0239] }

[0240] return e_fuzzy_sets, ec_fuzzy_sets, u_fuzzy_sets

[0241] # Fuzzy rule base

[0242] rules =

[0243] ('NB', 'NB', 'PB'),

[0244] ('NB', 'NS', 'PB'),

[0245] ('NB', 'ZE', 'PS'),

[0246] ('NB', 'PS', 'PS'),

[0247] ('NB', 'PB', 'ZE'),

[0248] ('NS', 'NB', 'PB'),

[0249] ('NS', 'NS', 'PS'),

[0250] ('NS', 'ZE', 'PS'),

[0251] ('NS', 'PS', 'ZE'),

[0252] ('NS', 'PB', 'NS'),

[0253] ('ZE', 'NB', 'PS'),

[0254] ('ZE', 'NS', 'PS'),

[0255] ('ZE', 'ZE', 'ZE'),

[0256] ('ZE', 'PS', 'NS'),

[0257] ('ZE', 'PB', 'NS'),

[0258] ('PS', 'NB', 'PS'),

[0259] ('PS', 'NS', 'ZE'),

[0260] ('PS', 'ZE', 'NS'),

[0261] ('PS', 'PS', 'NS'),

[0262] ('PS', 'PB', 'NB'),

[0263] ('PB', 'NB', 'ZE'),

[0264] ('PB', 'NS', 'NS'),

[0265] ('PB', 'ZE', 'NS'),

[0266] ('PB', 'PS', 'NB'),

[0267] ('PB', 'PB', 'NB')

[0269] def fuzzy_control(e, ec):

[0270] e_fuzzy_sets, ec_fuzzy_sets, u_fuzzy_sets = define_fuzzy_sets()

[0271] # Calculate the membership degrees of input variables

[0272] e_memberships = {k: v(e) for k, v in e_fuzzy_sets.items()}

[0273] ec_memberships = {k: v(ec) for k, v in ec_fuzzy_sets.items()}

[0274] # Fuzzy inference

[0275] u_memberships = {}

[0276] for rule in rules:

[0277] e_label, ec_label, u_label = rule

[0278] w = min(e_memberships[e_label], ec_memberships[ec_label])

[0279] if u_label not in u_memberships or w > u_memberships[u_label]:

[0280] u_memberships[u_label] = w

[0281] # Defuzzification (using the centroid method)

[0282] numerator = 0

[0283] denominator = 0

[0284] for u_label, w in u_memberships.items(): ​

[0285] u_values = [x for x in range(u_range[0], u_range[1] + 1)]

[0286] numerator += sum([w * u_fuzzy_sets[u_label](x) * x for x in u_values])

[0287] denominator += sum([w * u_fuzzy_sets[u_label](x) for x in u_values])

[0288] u = numerator / denominator if denominator != 0 else 0

[0289] return u;

[0290] The heating module adjusts the heating power by adjusting the magnitude of its own supply current according to the control quantity output by the controller, so as to increase the temperature of the hot coiler; the calculation formula for its heating power is P = I 2 R, where P is the heating power, I is the current passing through the resistance heating wire, and R is the resistance value of the resistance heating wire;

[0291] The cooling module adjusts the cooling water flow rate by controlling the rotation speed of the circulating water pump according to the control quantity output by the controller, so as to reduce the temperature of the hot coiler; the heat dissipation efficiency of the cooling module is related to the cooling water flow rate and the heat dissipation area, and its heat dissipation formula is Q = hAΔT, where Q is the heat dissipation quantity, h is the convective heat transfer coefficient, A is the heat dissipation area, and ΔT is the temperature difference between the hot coiler and the cooling medium.

[0292] Among them, the temperature sensor has a self - calibration function and automatically performs calibration once every 4 hours; when the calibration is started, the built - in calibration circuit will automatically switch to the calibration mode, connect the reference resistor to the measurement circuit, and obtain the resistance value R corresponding to the reference resistor ref ; the temperature sensor continuously collects 20 groups of temperature data within 10 minutes according to the preset sampling frequency, and each group of data contains the temperature values of 5 measurement points; through the least - squares fitting algorithm, the collected temperature values T i and the corresponding resistance values R i are substituted into the calibration curve equation R = aT + b, where a and b are parameters to be fitted, and an error function is constructed By taking the partial derivatives of the error function and setting them to zero, the values of a and b are solved to obtain an accurate calibration curve, ensuring the accuracy of the measurement data during long - term use.

[0293] Among them, the heating module is equipped with an intelligent power regulation system; this system, through built-in high-precision current sensors and voltage sensors, monitors the supply current and voltage of the heating module in real time, and accurately calculates the current heating power according to the formula P = UI, where U is the supply voltage and I is the supply current; when it is detected that the temperature rise rate of the hot coiling reel deviates from the preset range, the intelligent power regulation system will automatically adjust the power supply mode of the heating module; the preset temperature rise rate range is set according to different hot coiling reel materials and production process requirements; when the temperature rises too slowly, the system, through the control circuit, adopts a segmented increasing power supply strategy; at the initial stage, it supplies power at a relatively low power P 1 power supply, P 1 is set to 30% of the maximum power P max of the heating module, to avoid stress damage to the hot coiling reel due to sudden temperature changes; as the temperature of the hot coiling reel gradually approaches the target value, when the temperature reaches 40% of the target temperature, the power supply power is increased to P 2 , P 2 is 60% of P max ; when the temperature reaches 70% of the target temperature, the power supply power is further increased to P 3 , P 3 is 80% of P max until an appropriate heating rate is reached.

[0294] The intelligent power regulation system dynamically adjusts the supply frequency according to the material characteristics and current temperature of the hot coiling reel; using the skin effect principle, it appropriately increases the supply frequency in the high-temperature section (when the temperature of the hot coiling reel reaches 80% and above of the target temperature); for example, it gradually increases the supply frequency from the initial 50Hz to 100Hz, making the current more concentrated on the surface of the resistance heating wire, enhancing the heating effect, so as to increase the heating efficiency by 30% on the premise of ensuring the heating quality, and effectively reduce energy consumption. After actual testing, compared with not using this intelligent power regulation system, the energy consumption can be reduced by 20%.

[0295] Among them, the cooling module adopts the phase change material assisted cooling technology; a phase change material with high latent heat is filled in the internal space of the cooling module, and its phase change temperature range is accurately matched with the normal working temperature range of the hot coiling reel; when the temperature of the hot coiling reel rises, cooling water flows through the cooling module, heating the phase change material to the phase change temperature, and the phase change material changes from solid state to liquid state, absorbing a large amount of heat during this process, greatly improving the heat dissipation capacity of the cooling module; by setting a flow sensor and a temperature sensor at the inlet and outlet of the cooling module, the flow rate and the inlet and outlet temperature difference of the cooling water are monitored in real time, and the formula Q = mcΔT is used, where m is the mass of the cooling water flow, c is the specific heat capacity of water, and ΔT is the inlet and outlet temperature difference of the cooling water flow, to accurately calculate the real-time heat dissipation of the cooling module; according to the calculation results, the controller dynamically adjusts the speed of the circulating water pump to ensure that the cooling module always maintains an efficient heat dissipation state, effectively improving the stability and response speed of the hot coiling reel temperature regulation.

[0296] Among them, the database management system adopted by the data storage module has data encryption and backup functions; during the data storage process, sensitive data in the temperature data record table and the control parameter record table, such as the temperature setting value corresponding to the key production process, important control parameters, etc., are encrypted using the AES algorithm; the AES algorithm is based on the Rijndael algorithm and supports key lengths of 128 bits, 192 bits, and 256 bits; its encryption process mainly includes initial key expansion and multiple rounds of encryption operations; the initial key expansion expands the input key into multiple round keys for subsequent encryption rounds; in each round of encryption, byte substitution, row shift, column mixing, and round key addition operations are performed in sequence; byte substitution realizes non-linear transformation by looking up the S box to enhance the security of the cipher; row shift circularly shifts each row of bytes by different offsets; column mixing mixes each column of bytes through matrix multiplication; round key addition performs an exclusive OR operation on the current round of data and the corresponding round key; after multiple rounds of encryption, ciphertext data is obtained to ensure the security of data during storage and transmission;

[0297] An example code implementation of the AES algorithm in Python is as follows:

[0298] from Crypto.Cipher import AES

[0299] from Crypto.Util.Padding import pad, unpad

[0300] from Crypto.Random import get_random_bytes

[0301] # Generate a random AES key, 16 bytes (128 bits) in length, can be adjusted to 24 bytes (192 bits) or 32 bytes (256 bits) according to requirements

[0302] key = get_random_bytes(16)

[0303] def encrypt_data(data):

[0304] cipher = AES.new(key, AES.MODE_CBC)

[0305] ct_bytes = cipher.encrypt(pad(data.encode('utf-8'), AES.block_size))

[0306] return cipher.iv + ct_bytes

[0307] def decrypt_data(ct):

[0308] iv = ct[:AES.block_size]

[0309] ct = ct[AES.block_size:]

[0310] cipher = AES.new(key, AES.MODE_CBC, iv)

[0311] pt = unpad(cipher.decrypt(ct), AES.block_size)

[0312] return pt.decode('utf-8')

[0313] # Example data encryption and decryption

[0314] original_data = "Temperature setting value corresponding to the key production process: 100°C"

[0315] encrypted_data = encrypt_data(original_data)

[0316] decrypted_data = decrypt_data(encrypted_data)

[0317] print(f"Original data: {original_data}")

[0318] print(f"Encrypted data: {encrypted_data.hex()}")

[0319] print(f"Decrypted data: {decrypted_data}")

[0320] Meanwhile, the database management system automatically performs a full backup of the data at 2:00 am every day at a set time interval, and stores the backup data in redundant storage devices in a different location. When the main storage device fails, the data recovery operation can be completed within 30 minutes to ensure the integrity and availability of the temperature data and control parameters, and the storage duration is not less than 3 months.

[0321] The present invention provides a control method for a hot coiling temperature regulation device, comprising the following steps:

[0322] Step 1: Target temperature presetting: On the operation interface of the controller or through the upper computer software connected thereto, according to different product production process standards, input the hot coiling target temperature in the temperature setting window. The numerical range of this target temperature is determined according to actual production requirements, between 50°C and 150°C, and can be accurate to one decimal place. After input is completed, the controller stores this target temperature value in the internal non-volatile memory for subsequent call at any time during the control process.

[0323] Step 2: Temperature data acquisition and transmission: The temperature sensor triggers a data acquisition operation once every 5 seconds in a timed interrupt manner. During each acquisition, the A / D conversion circuit inside the temperature sensor converts the analog signal sensed by the thermistor into a digital signal, and performs a series of signal processing operations, including denoising, amplification, etc., to ensure the accuracy of the data. The processed temperature data is transmitted to a specific data receiving pin of the controller through the SPI communication protocol via a wired connection method.

[0324] Step 3: Deviation calculation: After the controller receives the real-time temperature data transmitted by the temperature sensor, it reads the preset hot coiling target temperature from the internal memory. Use the formula e = T set -T real to calculate the temperature deviation e, where T set is the target temperature and T real is the real-time temperature. To ensure calculation accuracy, the controller uses fixed-point arithmetic or floating-point arithmetic methods, which are configured according to its hardware resources and performance requirements. Subsequently, read the previous temperature deviation e prev from the internal register, and combine the time interval Deltat of this acquisition, which is 5 seconds, and use the formula Calculate the rate of change of temperature deviation ec; Fuzzy control quantity calculation: Take the calculated temperature deviation e and the rate of change of temperature deviation ec as the input quantities of the fuzzy controller; First, according to the fuzzy sets and membership functions defined in Claim 1, calculate the membership degrees of e and ec for each fuzzy subset, such as "NB", "NS", "ZE", "PS", "PB". When calculating the membership degree of the temperature deviation e belonging to the "NB" fuzzy subset, it is obtained by calling the e f uzzy s ets['NB'](e) function; Then, perform fuzzy inference according to the preset fuzzy rule base; For each fuzzy rule, determine the activation strength of the rule by taking the minimum value of the membership degrees of the input quantities, that is, w = min(e m emberships[e l abel], ec m emberships[ec l abel]); After the inference of all rules, obtain the membership degree distribution of the control quantity for each fuzzy subset; Finally, perform defuzzification operation using the centroid method, that is, by traversing all discrete values within the control quantity output range u r ange, combined with the membership degrees of the control quantity for each fuzzy subset, and calculate the exact control quantity u according to the formula This calculation process is implemented by executing the Python code included in the controller part of Claim 1;

[0325] Step 4: Temperature regulation: The controller adjusts the heating power of the heating module and the cooling water flow rate of the cooling module through the control circuit according to the calculated control quantity u; The control circuit uses PWM technology to generate a PWM signal with a specific duty cycle through the PWM generator inside the controller; For the heating module, after the PWM signal is amplified by the drive circuit, it controls the on and off of the solid-state relay or power transistor, thereby adjusting the supply voltage of the resistance heating wire, and further changing the heating power. The relationship between the heating power and the PWM duty cycle is determined by the formula P = P max *D, where P max is the maximum power of the heating module and D is the PWM duty cycle); For the cooling module, the PWM signal controls the DC motor drive chip to adjust the speed of the circulating water pump motor, thereby changing the cooling water flow rate. The relationship between the cooling water flow rate and the PWM duty cycle is converted through a linearly measured relationship or a non-linear mapping relationship;

[0326] Step 5: Parameter Adjustment: The controller collects in real time the operating state parameters of the rotational speed and load current of the hot coiling reel and the data transmitted by the environmental temperature and humidity sensors through the internal sensor interface or communication interface, and obtains the operating frequency information through the communication interface of the equipment control system; when it detects that these parameters change, it starts the genetic algorithm to adjust the parameters of the fuzzy control rules; constructs the individuals of the genetic algorithm with the membership function parameters of the fuzzy control rules as genes; uses the stability index of temperature control, such as the standard deviation of the temperature fluctuation range, and the accuracy index, such as the mean absolute error between the actual temperature and the target temperature, as the fitness function, and continuously optimizes the parameters through genetic operations such as selection, crossover, and mutation.

[0327] The specific operation process is as follows: First, define the genetic algorithm parameters, set the population size to 50, the number of iterations to 100, and initialize the population; the following is the Python code to implement population initialization:

[0328] import random

[0329] # Assume the number of genes for each individual, corresponding to the number of parameters of the fuzzy control rules

[0330] gene_num = 10

[0331] # Gene value range

[0332] gene_range = [-5, 5]

[0333] def initialize_population(population_size, gene_num, gene_range):

[0334] population = []

[0335] for _ in range(population_size):

[0336] individual = [random.uniform(gene_range[0], gene_range[1]) for _ in range(gene_num)]

[0337] population.append(individual)

[0338] return population

[0339] population = initialize_population(50, gene_num, gene_range)

[0340] Then, through the roulette wheel selection method, individuals with higher fitness are selected by the roulette_wheel_selection function; the following is the implementation code of the roulette_wheel_selection function:

[0341] def calculate_fitness(individual):

[0342] # Here, the fitness function needs to be defined according to the actual situation. For example, use the stability index of temperature control (such as the standard deviation of the temperature fluctuation range)

[0343] # and the accuracy index (such as the mean absolute error between the actual temperature and the target temperature) as the fitness function

[0344] # Here is a simple example, returning the sum of the individual gene values as the fitness

[0345] return sum(individual)

[0346] def roulette_wheel_selection(population):

[0347] fitness_values = [calculate_fitness(individual) for individual in population]

[0348] total_fitness = sum(fitness_values)

[0349] selection_probs = [fitness / total_fitness for fitness in fitness_values]

[0350] selected_index = random.choices(range(len(population)), weights = selection_probs)[0]

[0351] return population[selected_index]

[0352] selected_individual = roulette_wheel_selection(population)

[0353] Next, single-point crossover is implemented through the single_point_crossover function and uniform mutation is implemented through the uniform_mutation function. The mutation rate is set to 0.01, and operations are performed to generate a new population. The implementation codes for these two functions are as follows:

[0354] def single_point_crossover(parent1, parent2):

[0355] crossover_point = random.randint(1, len(parent1) - 1)

[0356] child1 = parent1[:crossover_point] + parent2[crossover_point:]

[0357] child2 = parent2[:crossover_point] + parent1[crossover_point:]

[0358] return child1, child2

[0359] def uniform_mutation(individual, mutation_rate, gene_range):

[0360] for i in range(len(individual)):

[0361] if random.random() < mutation_rate:

[0362] individual[i] = random.uniform(gene_range[0], gene_range[1])

[0363] return individual

[0364] parent1 = selected_individual

[0365] parent2 = roulette_wheel_selection(population)

[0366] child1, child2 = single_point_crossover(parent1, parent2)

[0367] child1 = uniform_mutation(child1, 0.01, gene_range)

[0368] child2 = uniform_mutation(child2, 0.01, gene_range). After 100 iterations, the optimized fuzzy control rule parameters are obtained and applied to the subsequent fuzzy control process. The following is the complete iteration process code:

[0369] python

[0370] max_generations = 100

[0371] for generation in range(max_generations):

[0372] new_population = []

[0373] for _ in range(25): # Generate 25 pairs of new individuals

[0374] parent1 = roulette_wheel_selection(population)

[0375] parent2 = roulette_wheel_selection(population)

[0376] child1, child2 = single_point_crossover(parent1, parent2)

[0377] child1 = uniform_mutation(child1, 0.01, gene_range)

[0378] child2 = uniform_mutation(child2, 0.01, gene_range)

[0379] new_population.extend([child1, child2])

[0380] population = new_population

[0381] # Assume the best individual is the one with the highest fitness

[0382] best_individual = max(population, key=calculate_fitness)

[0383] # Apply the optimized parameters to the fuzzy control rules

[0384] # Here, the parameters in best_individual need to be updated to the fuzzy control rules according to the actual situation

[0385] # For example, update the parameters of the membership function, etc.

[0386] Among them, during the parameter adjustment process of the fuzzy control rules, the genetic algorithm is used to optimize the parameters of the fuzzy control rules; the population size of the genetic algorithm is set to 50, which means that the initial population contains 50 individuals, and each individual represents a set of membership function parameters of the fuzzy control rules, such as the boundary values and shape parameters of the fuzzy subsets "NB", "NS", "ZE", "PS", "PB"; the number of iterations is set to 100, that is, the genetic algorithm will perform 100 evolutionary operations;

[0387] In the selection operation, the roulette wheel selection method is adopted. The specific implementation logic is as follows: First, calculate the fitness value of each individual. The fitness function is based on the stability index of temperature control, such as the standard deviation of the temperature fluctuation range, and the accuracy index, such as the mean absolute error between the actual temperature and the target temperature; the fitness of individual i is f i , then the probability of its being selected By randomly generating a number between 0 and 1, according to the probability p i Select individuals with higher fitness to enter the next generation;

[0388] The single-point crossover is adopted for the crossover operation, that is, two individuals are randomly selected from the population as the parents, and a crossover point is randomly selected in their gene sequences; the gene sequence of individual A is [1, 2, 3, 4, 5], and the gene sequence of individual B is [6, 7, 8, 9, 10]. If the crossover point is selected at the 3rd position, then the gene sequence of the offspring C generated after crossover is [1, 2, 8, 9, 10], and the gene sequence of the offspring D is [6, 7, 3, 4, 5];

[0389] The uniform mutation is adopted for the mutation operation, and the mutation rate is set to 0.01, which means that in each iteration, each gene of each individual has a probability of 0.01 of mutating; when mutating, a new value is randomly generated within the value range of the gene to replace the original gene value; for example, the value range of a certain gene is [0, 1]. If this gene mutates, a new value between 0 and 1 is randomly generated to replace the original gene value.

[0390] Among them, when the temperature of the hot coiled strip exceeds the preset safe temperature range, with the upper limit being 160 °C and the lower limit being 40 °C, the controller immediately triggers the audible and visual alarm system, simultaneously automatically cuts off the power supply of the heating module, and increases the cooling power of the cooling module; the audible and visual alarm system realizes the alarm function by controlling the buzzer and the LED light, and its control logic is realized through the cooperation of the hardware circuit and the software program. When it detects that the temperature exceeds the range, the controller outputs a high-level signal to trigger the buzzer to sound and the LED light to flash.

[0391] Among them, the control method further includes the step of regularly performing performance detection on the device. Every 10 production cycles, the performance detection program is automatically run to detect the working states of the temperature sensor, the heating module, the cooling module, and the controller; the performance detection program is carried out by sending test signals, collecting feedback data, etc. For example, a calibration signal is sent to the temperature sensor to detect whether its response is normal; specific control signals are applied to the heating module and the cooling module to detect whether their power output and cooling effect meet the expectations.

[0392] Among them, the controller is also connected to the production management system, and the temperature data and control parameters of the hot coiled strip are uploaded in real time so that production management personnel can perform remote monitoring and data analysis; the data upload adopts the MQTT communication protocol, and the data is transmitted to the server of the production management system through the network interface. The server side uses InfluxDB software for data reception and storage, and production management personnel can view the data and perform data analysis in real time through the Web interface or the mobile application program.

[0393] Experimental data:

[0394] The following designs a comparative experiment to compare the performance of this hot coiled strip temperature regulation device and the traditional temperature regulation device in different scenarios to verify the advantages of this device.

[0395] Experimental design

[0396] This hot coiled strip temperature regulation device and the traditional temperature regulation device are selected for comparison (assuming that the traditional device uses a simple PID control algorithm without adaptive adjustment and advanced technologies). Four scenarios, namely normal regulation, rapid temperature rise, abnormal temperature drop, and environmental factor influence, are set for the experiment. Each scenario is repeated multiple times, and key indicators such as temperature regulation time, temperature fluctuation range, and energy consumption are recorded.

[0397] Experimental data table

[0398]

[0399]

[0400]

[0401]

[0402]

[0403] Data description:

[0404] Time to reach the target temperature: The time required from the start of the experiment until the temperature of the hot coiling reel stabilizes within the range of the target temperature ±0.1°C.

[0405] Maximum temperature fluctuation range: The maximum difference by which the temperature of the hot coiling reel deviates from the target temperature during the experiment.

[0406] Temperature deviation after stabilization: The difference between the temperature of the hot coiling reel after stabilization and the target temperature.

[0407] Energy consumption: The electrical energy consumed by the heating module and the cooling module during the experiment.

[0408] From the above comparative experimental data, it can be clearly seen that this hot coiling reel temperature regulation device is superior to the traditional temperature regulation device in terms of temperature regulation speed, temperature control accuracy, and energy consumption.

[0409] Specific usage and functions of this embodiment: In the present invention, first, the temperature sensor is closely attached and installed at the key temperature measurement points on the surface of the hot coiling reel. After the equipment is started, the sensor collects the temperature data of the hot coiling reel at a cycle of 5 seconds. The analog signal sensed by the thermistor is converted into a digital signal through the internal A / D conversion circuit, and then after denoising and amplification processing, it is transmitted to the controller through the SPI communication protocol. And every 4 hours, the sensor automatically performs calibration. When the calibration is started, the built-in calibration circuit accesses the reference resistor to obtain the corresponding resistance value, and within the next 10 minutes, 20 groups of temperature values with 5 measurement points in each group are collected at the preset sampling frequency. These temperature values and the corresponding resistance values are substituted into the calibration curve equation, and the relevant values are solved by using the least squares fitting algorithm to update the calibration curve; Next, the operator inputs the target temperature of 100 °C on the controller operation interface, and the controller stores this value in the internal non-volatile memory. After the controller receives the data transmitted by the temperature sensor, it reads the target temperature from the memory to calculate the temperature deviation, calculates the deviation change rate in the subsequent cycle combined with the deviation at the previous moment, and then takes the temperature deviation and the deviation change rate as input quantities, and performs fuzzy inference based on the defined fuzzy sets, membership functions, and fuzzy rule bases, and uses the centroid method to defuzzify to obtain the control quantity. During the production process, the controller real-time collects information such as the rotation speed, load current, ambient temperature, humidity, and operating frequency of the hot coiling reel. If it is detected that the ambient temperature suddenly rises, resulting in a change in the temperature rise rate of the hot coiling reel, etc., the genetic algorithm is started, the initial population size is set to 50, and the number of iterations is 100. The stability and accuracy indicators of temperature control are used as the fitness function, and the parameters are optimized through operations such as roulette wheel selection method, single-point crossover, and uniform mutation; After that, the heating module adjusts the supply current according to the control quantity output by the controller. Assuming that the initial control quantity corresponds to a heating power of 1275 W, the heating module starts to heat the hot coiling reel. Its intelligent power regulation system real-time monitors the supply current and voltage to calculate the current heating power. If it is detected that the temperature of the hot coiling reel rises too slowly, a segmented incremental power supply strategy is adopted. Initially, it is powered at 30% of the maximum power of 1500 W. When the temperature of the hot coiling reel reaches 40% of the target temperature, the supply power is increased to 60% of 1500 W. When the temperature reaches 70%, it is further increased to 80% of 1500 W. When the temperature of the hot coiling reel reaches 80 °C and above, the supply frequency is appropriately increased; And the cooling module initially does not work if the temperature of the hot coiling reel is lower than the target temperature, and the flow rate is 0 L / min. If the temperature of the hot coiling reel exceeds the target temperature due to environmental factors or other reasons, the controller adjusts the cooling module according to the deviation and the control quantity. The cooling module adjusts the cooling water flow rate by controlling the rotation speed of the circulating water pump. The phase change material filled inside starts to play a role. The cooling water flows through the cooling module to heat the phase change material to the phase change temperature, and the phase change material changes from solid to liquid to absorb heat. The flow sensors and temperature sensors at the inlet and outlet monitor in real time, and the real-time heat dissipation is calculated using relevant formulas. The controller dynamically adjusts the rotation speed of the circulating water pump accordingly;Meanwhile, during the data storage process of the data storage module, the database management system encrypts sensitive data in the temperature data record table and the control parameter record table, such as the target temperature, important control parameters, etc., using the AES algorithm. For example, a 16-byte random key is generated to encrypt the data. After initial key expansion and multiple rounds of encryption operations, ciphertext data is obtained. And at 2 o'clock every morning, the database management system automatically performs a full backup of the data and stores the backup data in a remote redundant storage device. If the main storage device fails, data recovery can be completed within 30 minutes. In addition, in the implementation of the control method for the hot coiling temperature regulating device, the operator accurately inputs the target temperature of 100°C for the hot coiling in the controller operation interface to complete the preset and storage of the target temperature. The temperature sensor automatically collects the hot coiling temperature data at a 5-second cycle, and after processing, transmits it to the controller through the SPI communication protocol. The controller continuously receives the temperature data, continuously calculates the temperature deviation and the deviation change rate, calculates the control quantity according to the fuzzy control algorithm, and adjusts the heating module and the cooling module through PWM technology according to the calculated control quantity. During the production process, if it is detected that the hot coiling speed increases, the load current changes, or the environmental temperature and humidity change, etc., the controller starts the genetic algorithm. After operations such as population initialization, roulette wheel selection, single-point crossover, and uniform mutation, it iterates 100 times to optimize the fuzzy control rule parameters. If the hot coiling temperature exceeds the safe range of 40°C - 160°C, the controller immediately triggers the audible and visual alarm system, the buzzer sounds, and the LED lights flash. At the same time, the power supply of the heating module is cut off, and the cooling power of the cooling module is increased. Every 10 production cycles, the device automatically runs a performance detection program to detect the working status of each module. Finally, the controller uploads the hot coiling temperature data and control parameters to the production management system server in real time through the MQTT communication protocol. The server uses InfluxDB software to store the data. Production management personnel can view information such as the hot coiling temperature change curve and control parameters in real time through the Web interface or mobile application program for data analysis, realizing remote monitoring and management.

Claims

1. A heat collection coil temperature regulating device, comprising: Temperature sensor, controller, heating module and cooling module; characterized in that: The temperature sensor is tightly installed at the key temperature measurement point on the surface of the heat collection coil, with a measurement accuracy of up to ±0.1°C, and is used to collect the temperature data of the heat collection coil in real time, and accurately transmit the temperature data to the controller through wireless transmission; the temperature acquisition principle is that the resistance value of the thermistor changes with temperature, following the formula R=R0(1+α(T-T0)), where R is the resistance value at temperature T, R0 is the resistance value at reference temperature T0, and α is the temperature coefficient of the thermistor; The controller receives the temperature data transmitted by the temperature sensor, and quickly calculates the temperature deviation and the temperature deviation change rate according to the preset heat collection coil target temperature and temperature data, and adopts the fuzzy adaptive control algorithm to obtain the control amount according to the temperature deviation and the temperature deviation change rate; in the implementation of the fuzzy control algorithm, the code is as follows: #Define the input and output variable range of the fuzzy controller e_range = [-5,5] ec_range = [-5,5] u_range = [-10,10] #Define fuzzy sets and membership functions defdefine_fuzzy_sets(): e_fuzzy_sets = { 'NB':lambdax:1ifx<=-3else(1-(x+3) / 2)if-3 <x<-1else0, 'NS':lambdax:1if-2<=x<0else(1-x / 2)if0<=x<2else0, 'ZE':lambdax:1if-1<=x<=1else0, 'PS':lambdax:1if0 <x<=2else(1-(x-2) / 2)if2<x<4else0, 'PB':lambdax:1ifx>=3else(1-(x-3) / 2)if1 <x<3else0 } ec_fuzzy_sets = { 'NB':lambdax:1ifx<=-3else(1-(x+3) / 2)if-3 <x<-1else0, 'NS':lambdax:1if-2<=x<0else(1-x / 2)if0<=x<2else0, 'ZE':lambdax:1if-1<=x<=1else0, 'PS':lambdax:1if0 <x<=2else(1-(x-2) / 2)if2<x<4else0, 'PB':lambdax:1ifx>=3else(1-(x-3) / 2)if1 <x<3else0 } u_fuzzy_sets={ 'NB':lambdax:1ifx<=-8else(1-(x+8) / 4)if-8 <x<-4else0, 'NS':lambdax:1if-6<=x<-2else(1-(x+2) / 4)if-2<=x<2else0, 'ZE': lambda x: 1 if -2 <= x <= 2 else 0, 'PS': lambda x: 1 if 2 < x <= 6 else (1 - (x - 2) / 4) if 6 < x < 10 else 0, 'PB': lambda x: 1 if x >= 8 else (1 - (x - 8) / 4) if 4 < x < 8 else 0 } return e_fuzzy_sets, ec_fuzzy_sets, u_fuzzy_sets # Fuzzy rule base rules = ('NB', 'NB', 'PB'), ('NB', 'NS', 'PB'), ('NB', 'ZE', 'PS'), ('NB', 'PS', 'PS'), ('NB', 'PB', 'ZE'), ('NS', 'NB', 'PB'), ('NS', 'NS', 'PS'), ('NS', 'ZE', 'PS'), ('NS', 'PS', 'ZE'), ('NS', 'PB', 'NS'), ('ZE', 'NB', 'PS'), ('ZE', 'NS', 'PS'), ('ZE', 'ZE', 'ZE'), ('ZE', 'PS', 'NS'), ('ZE', 'PB', 'NS'), ('PS', 'NB', 'PS'), ('PS', 'NS', 'ZE'), ('PS', 'ZE', 'NS'), ('PS', 'PS', 'NS'), ('PS', 'PB', 'NB'), ('PB', 'NB', 'ZE'), ('PB', 'NS', 'NS'), ('PB', 'ZE', 'NS'), ('PB', 'PS', 'NB'), ('PB', 'PB', 'NB') ] def fuzzy_control(e, ec): e_fuzzy_sets, ec_fuzzy_sets, u_fuzzy_sets = define_fuzzy_sets() # Calculate the membership degrees of input variables e_memberships = {k: v(e) for k, v in e_fuzzy_sets.items()} ec_memberships = {k: v(ec) for k, v in ec_fuzzy_sets.items()} # Fuzzy inference u_memberships = {} for rule in rules: e_label, ec_label, u_label = rule w = min(e_memberships[e_label], ec_memberships[ec_label]) if u_label not in u_memberships or w > u_memberships[u_label]: u_memberships[u_label]=w # Defuzzification (using centroid method) numerator=0 denominator=0 foru_label,winu_memberships.items(): u_values=[xforxinrange(u_range[0],u_range[1]+1)] numerator+=sum([w*u_fuzzy_sets[u_label](x)*xforxinu_values]) denominator+=sum([w*u_fuzzy_sets[u_label](x)forxinu_values]) u=numerator / denominatorifdenominator! =0else0 returnu; The heating module adjusts the heating power by adjusting its own power supply current according to the control quantity output by the controller to increase the temperature of the heat collection coil; the heating power calculation formula is P = I 2 R, where P is the heating power, I is the current passing through the resistance heating wire, and R is the resistance value of the resistance heating wire; The cooling module adjusts the cooling water flow rate by controlling the speed of the circulating water pump according to the control quantity output by the controller to reduce the temperature of the heat collection coil; the heat dissipation efficiency of the cooling module is related to the cooling water flow rate and the heat dissipation area, and its heat dissipation formula is Q=hAΔT, where Q is the heat dissipation, h is the convection heat transfer coefficient, A is the heat dissipation area, and ΔT is the temperature difference between the heat collection coil and the cooling medium.

2. A heat collection coil temperature regulating device according to claim 1, characterized in that: The temperature sensor has a self-calibration function and automatically performs calibration every 4 hours. When the calibration is started, the built-in calibration circuit automatically switches to the calibration mode, connects the reference resistor to the measurement circuit, and obtains the resistance value R corresponding to the reference resistor. ref The temperature sensor continuously collects 20 sets of temperature data within 10 minutes according to the preset sampling frequency. Each set of data contains the temperature values ​​of 5 measurement points. The collected temperature values ​​T are fitted by the least squares method. i The corresponding resistance value R i Substitute into the calibration curve equation R = aT + b, where a and b are the parameters to be fitted, and construct the error function By taking the partial derivative of the error function and setting it to zero, the values ​​of a and b are solved and an accurate calibration curve is obtained to ensure the accuracy of the measurement data during long-term use.

3. The heat collection coil temperature regulating device according to claim 1, characterized in that: The heating module is equipped with an intelligent power regulation system; the system monitors the power supply current and voltage of the heating module in real time through built-in high-precision current sensors and voltage sensors, and accurately calculates the current heating power according to the formula P=UI, where U is the power supply voltage and I is the power supply current; when it is detected that the temperature rise rate of the heat collection coil deviates from the preset range, the intelligent power regulation system will automatically adjust the power supply mode of the heating module; the preset temperature rise rate range is set according to different heat collection coil materials and production process requirements; when the temperature rises too slowly, the system adopts a segmented incremental power supply strategy through the control circuit; in the initial stage, the power is supplied at a lower power P1, and P1 is set to the maximum power P of the heating module max 30% of the target temperature to avoid stress damage to the heat-collecting coil due to sudden temperature change; as the temperature of the heat-collecting coil gradually approaches the target value, when the temperature reaches 40% of the target temperature, the power supply is increased to P2, P2 is P max When the temperature reaches 70% of the target temperature, the power supply is further increased to P3, which is P max 80% until a suitable heating rate is reached; The intelligent power regulation system dynamically adjusts the power supply frequency according to the material characteristics and current temperature of the heat collection coil; using the principle of skin effect, the power supply frequency is appropriately increased in the high temperature section (when the temperature of the heat collection coil reaches 80% or more of the target temperature).

4. A heat collection coil temperature regulating device according to claim 1, characterized in that: The cooling module adopts phase change material assisted cooling technology; the internal space of the cooling module is filled with high latent heat phase change material, and its phase change temperature range accurately matches the normal working temperature range of the heat collection coil; when the temperature of the heat collection coil rises, cooling water flows through the cooling module, heating the phase change material to the phase change temperature, and the phase change material changes from solid to liquid, absorbing a large amount of heat in the process, greatly improving the heat dissipation capacity of the cooling module; by setting flow sensors and temperature sensors at the inlet and outlet of the cooling module, the flow rate and inlet and outlet temperature difference of the cooling water flow are monitored in real time, and the formula Q=mcΔT is used, where m is the mass of the cooling water flow, c is the specific heat capacity of water, and ΔT is the inlet and outlet temperature difference of the cooling water flow, to accurately calculate the real-time heat dissipation of the cooling module; according to the calculation results, the controller dynamically adjusts the speed of the circulating water pump to ensure that the cooling module always maintains an efficient heat dissipation state, effectively improving the stability and response speed of the temperature regulation of the heat collection coil.

5. The heat collection coil temperature regulating device according to claim 1, characterized in that: The database management system used in the data storage module has data encryption and backup functions; during the data storage process, the sensitive data in the temperature data record table and the control parameter record table, such as the temperature setting value corresponding to the key production process and important control parameters, are encrypted using the AES algorithm; the AES algorithm is based on the Rijndael algorithm and supports key lengths of 128 bits, 192 bits and 256 bits; The encryption process mainly includes initial key expansion and multiple rounds of encryption operations. The initial key expansion expands the input key into multiple round keys for use in subsequent encryption rounds. In each round of encryption, byte substitution, row shift, column confusion and round key addition operations are performed in sequence. Byte substitution realizes nonlinear transformation by searching S-boxes to enhance the security of the password. Row shift cyclically shifts each row of bytes according to different offsets. Column confusion mixes each column of bytes through matrix multiplication. The round key addition performs an XOR operation on the current round data and the corresponding round key; after multiple rounds of encryption, the ciphertext data is obtained to ensure the security of the data during storage and transmission; The sample code for implementing the AES algorithm in Python is as follows: fromCrypto.CipherimportAES fromCrypto.Util.Paddingimportpad,unpad fromCrypto.Randomimportget_random_bytes #Generate a random AES key with a length of 16 bytes (128 bits), which can be adjusted to 24 bytes (192 bits) or 32 bytes (256 bits) as required key = get_random_bytes(16) defencrypt_data(data): cipher=AES.new(key,AES.MODE_CBC) ct_bytes=cipher.encrypt(pad(data.encode('utf-8'),AES.block_size)) returncipher.iv+ct_bytes defdecrypt_data(ct): iv=ct[:AES.block_size] ct = ct[AES.block_size:] cipher=AES.new(key,AES.MODE_CBC,iv) pt=unpad(cipher.decrypt(ct),AES.block_size) returnpt.decode('utf-8') #Example data encryption and decryption original_data="Temperature setting value corresponding to key production process: 100℃" encrypted_data=encrypt_data(original_data) decrypted_data=decrypt_data(encrypted_data) print(f"Original data:{original_data}") print(f"Encrypted data:{encrypted_data.hex()}") print(f"decrypted data:{decrypted_data}") At the same time, the database management system automatically backs up all data at 2 a.m. every day according to the set time interval, and stores the backup data in redundant storage devices in other places; When the main storage device fails, data recovery operations can be completed within 30 minutes to ensure the integrity and availability of temperature data and control parameters, with a storage time of no less than 3 months.

6. A control method for a heat collection coil temperature regulating device, characterized in that: The following steps are involved: Step 1: Target temperature setting: In the operation interface of the controller or through the upper computer software connected to it, enter the target temperature of the heat collection coil in the temperature setting window according to the production process standards of different products; the target temperature value range is determined according to the actual production needs, between 50℃-150℃, and can be accurate to one decimal place; After the input is completed, the controller stores the target temperature value in the internal non-volatile memory so that it can be called at any time in the subsequent control process; Step 2: Temperature data collection and transmission: The temperature sensor uses a timed interrupt method to trigger a data collection operation at a fixed period of 5 seconds. During each collection, the A / D conversion circuit inside the temperature sensor converts the analog signal sensed by the thermistor into a digital signal and performs a series of signal processing, including denoising and amplification, to ensure the accuracy of the data. The processed temperature data is transmitted to the specific data receiving pin of the controller via the SPI communication protocol and wired connection. Step 3: Deviation calculation: After receiving the real-time temperature data transmitted by the temperature sensor, the controller reads the preset heat collection coil target temperature from the internal memory; using the formula e = T set -T real Calculate the temperature deviation e, where T set is the target temperature, T real is the real-time temperature; to ensure the calculation accuracy, the controller uses fixed-point or floating-point arithmetic and is configured according to its hardware resources and performance requirements; then, the temperature deviation e at the previous moment is read from the internal register prev , and combined with the time interval Deltat of this collection being 5 seconds, use the formula Calculate the temperature deviation change rate ec; Calculate the fuzzy control quantity: use the calculated temperature deviation e and the temperature deviation change rate ec as the input of the fuzzy controller; first, according to the fuzzy set and membership function defined in claim 1, calculate the membership of e and ec for each fuzzy subset, such as "NB", "NS", "ZE", "PS", and "PB". For the temperature deviation e, calculate its membership of the "NB" fuzzy subset by calling e f uzzy s ets[′NB′](e) function is used to obtain the fuzzy reasoning. Then, fuzzy reasoning is performed according to the preset fuzzy rule base. For each fuzzy rule, the minimum value of the input membership is taken, that is, w=min(e m emberships l abel],ec m emberships l abel]) to determine the activation strength of the rule; after reasoning all the rules, the membership distribution of the control quantity for each fuzzy subset is obtained; finally, the centroid method is used to perform the defuzzification operation, that is, by traversing the control quantity output range u r All discrete values ​​in ange, combined with the degree of membership of the control quantity to each fuzzy subset, are calculated according to the formula Calculate the precise control quantity u, the calculation process is realized by executing the Python code included in the controller part of claim 1; Step 4: Temperature regulation: The controller adjusts the heating power of the heating module and the cooling water flow rate of the cooling module through the control circuit according to the calculated control quantity u; the control circuit adopts PWM technology to generate a PWM signal with a specific duty cycle through the PWM generator inside the controller; for the heating module, the PWM signal is amplified by the drive circuit to control the on and off of the solid-state relay or power transistor, thereby adjusting the power supply voltage of the resistance heating wire, and then changing the heating power. The heating power and the PWM duty cycle are related by the formula P=P max *D, where P max For the cooling module, the PWM signal controls the DC motor driver chip to adjust the speed of the circulating water pump motor, thereby changing the cooling water flow rate. The cooling water flow rate and the PWM duty cycle are converted through a linear relationship or nonlinear mapping relationship determined by experiments. Step 5: Parameter adjustment: The controller collects the speed of the heat collection coil, the operating state parameters of the load current, and the data transmitted by the ambient temperature and humidity sensors in real time through the internal sensor interface or communication interface, and obtains the operating frequency information in combination with the communication interface of the equipment control system; when changes in these parameters are detected, the genetic algorithm is started to adjust the parameters of the fuzzy control rules; the membership function parameters of the fuzzy control rules are used as genes to construct individuals of the genetic algorithm; the stability indicators of temperature control, such as the standard deviation of the temperature fluctuation range and the accuracy indicators, such as the mean absolute error between the actual temperature and the target temperature, are used as fitness functions, and the parameters are continuously optimized through genetic operations such as selection, crossover and mutation; The specific operation process is as follows: First, define the genetic algorithm parameters, set the population size to 50, the number of iterations to 100, and initialize the population; the following is the Python code to implement population initialization: importrandom #Assuming the number of genes of each individual, the number of parameters of the corresponding fuzzy control rules gene_num=10 #Gene value range gene_range=[-5,5] definitialize_population(population_size,gene_num,gene_range): population = [] for_inrange(population_size): individual=[random.uniform(gene_range[0],gene_range[1])for_inrange(gene_num)] population.append(individual) returnpopulation population=initialize_population(50,gene_num,gene_range) Then, through the roulette wheel selection method, the individuals with higher fitness are selected through the roulette_wheel_selection function; the following is the implementation code of the roulette_wheel_selection function: defcalculate_fitness(individual): #Here you need to define the fitness function according to the actual situation, such as the stability index of temperature control (such as the standard deviation of the temperature fluctuation range) # and accuracy indicators (such as the mean absolute error between the actual temperature and the target temperature) as fitness function #Here is a simple example, returning the sum of individual gene values ​​as fitness returnsum(individual) defroulette_wheel_selection(population): fitness_values=[calculate_fitness(individual)forindividualinpopulation] total_fitness=sum(fitness_values) selection_probs=[fitness / total_fitnessforfitnessinfitness_values] selected_index=random.choices(range(len(population)),weights=selection_probs)[0] returnpopulation[selected_index] selected_individual=roulette_wheel_selection(population) Next, single-point crossover is implemented through the single_point_crossover function and uniform mutation is implemented through the uniform_mutation function. The mutation rate is set to 0.01, and the operation generates a new population. The following is the implementation code of these two functions: defsingle_point_crossover(parent1,parent2): crossover_point=random.randint(1,len(parent1)-1) child1=parent1[:crossover_point]+parent2[crossover_point:] child2=parent2[:crossover_point]+parent1[crossover_point:] returnchild1,child2 defuniform_mutation(individual,mutation_rate,gene_range): foriinrange(len(individual)): ifrandom.random() <mutation_rate: individual[i]=random.uniform(gene_range[0],gene_range[1]) returnindividual parent1 = selected_individual parent2=roulette_wheel_selection(population) child1,child2=single_point_crossover(parent1,parent2) child1=uniform_mutation(chi ld1,0.01,gene_range) child2 = uniform_mutation(child2, 0.01, gene_range) After 100 iterations, the optimized fuzzy control rule parameters are obtained and applied to the subsequent fuzzy control process; the following is the complete iterative process code: Python max_generations=100 forgenerationinrange(max_generations): new_population = [] for_inrange(25):#Generate 25 pairs of new individuals parent1=roulette_wheel_selection(population) parent2=roulette_wheel_selection(population) child1,child2=single_point_crossover(parent1,parent2) child1=uniform_mutation(chi ld1,0.01,gene_range) child2=uniform_mutation(chi ld2,0.01,gene_range) new_population.extend([chi ld1,chi ld2]) population = new_population #Assume that the optimal individual is the one with the highest fitness best_individual=max(population,key=calculate_fitness) #Apply the optimized parameters to the fuzzy control rules #Here you need to update the parameters in best_individual to the fuzzy control rules according to the actual situation #For example, update the parameters of the membership function, etc.

7. A control method for a heat collection coil temperature regulating device according to claim 6, characterized in that: In the process of adjusting the parameters of fuzzy control rules, genetic algorithm is used to optimize the parameters of fuzzy control rules; the population size of genetic algorithm is set to 50, which means that the initial population contains 50 individuals, each of which represents a group of membership function parameters of fuzzy control rules, such as the boundary value and shape parameters of fuzzy subsets "NB", "NS", "ZE", "PS", and "PB"; the number of iterations is set to 100, which means that the genetic algorithm will perform 100 evolution operations; In the selection operation, the roulette selection method is adopted. The specific implementation logic is as follows: first, the fitness value of each individual is calculated. The fitness function is based on the stability index of temperature control, such as the standard deviation of the temperature fluctuation range and the accuracy index, such as the mean absolute error between the actual temperature and the target temperature; the fitness of individual i is f i , then the probability of being selected By randomly generating a number between 0 and 1, according to probability p i Select individuals with higher fitness to enter the next generation; The crossover operation uses a single-point crossover, that is, two individuals are randomly selected from the population as parents, and a crossover point is randomly selected in their gene sequences; the gene sequence of individual A is [1,2,3,4,5], and the gene sequence of individual B is [6,7,8,9,10]. If the crossover point is selected at the third position, the gene sequence of the offspring C generated after the crossover is [1,2,8,9,10], and the gene sequence of the offspring D is [6,7,3,4,5]. The mutation operation uses uniform mutation, and the mutation rate is set to 0.01, which means that in each iteration, each gene of each individual has a 0.01 probability of mutation; when mutation occurs, a new value is randomly generated within the value range of the gene to replace the original gene value; for example, the value range of a gene is [0,1], if the gene mutates, a new value between 0 and 1 is randomly generated to replace the original gene value.

8. The control method of the heat collection coil temperature regulating device according to claim 6, characterized in that: When the temperature of the hot coil exceeds the preset safety temperature range, with an upper limit of 160°C and a lower limit of 40°C, the controller immediately triggers the sound and light alarm system, automatically cuts off the power supply of the heating module, and increases the cooling power of the cooling module; the sound and light alarm system realizes the alarm function by controlling the buzzer and LED light, and its control logic is realized through the collaborative implementation of hardware circuits and software programs. When it is detected that the temperature is out of range, the controller outputs a high-level signal to trigger the buzzer to sound and the LED light to flash.

9. The control method of the heat collection coil temperature regulating device according to claim 6, characterized in that: The control method also includes the step of regularly performing performance testing on the device. Every 10 production cycles, the performance testing program is automatically run to detect the working status of the temperature sensor, the heating module, the cooling module and the controller. The performance testing program is carried out by sending test signals, collecting feedback data, etc., such as sending a calibration signal to the temperature sensor to detect whether its response is normal; applying specific control signals to the heating module and the cooling module to detect whether their power output and cooling effect meet expectations.

10. The control method of the heat collection coil temperature regulating device according to claim 6, characterized in that: The controller is also connected to the production management system to upload the heat collection coil temperature data and control parameters in real time so that production managers can perform remote monitoring and data analysis. The data upload uses the MQTT communication protocol to transmit the data to the server of the production management system through the network interface. The server uses InfluxDB software to receive and store data. Production managers can view and analyze data in real time through the Web interface or mobile application.

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