Intelligent temperature control method and system for melting holding furnace

Abstract

The invention relates to the technical field of intelligent control of industrial heating equipment, and provides an intelligent temperature control method and system for a melting holding furnace, and the method comprises the steps: dividing a temperature control system into a temperature monitoring module and a temperature adjusting module, and building a real-time data interaction region in a central controller; the temperature monitoring module performs multi-sensor signal fusion through a data acquisition unit, performs dynamic parameter analysis through an adjustment execution unit, and writes control parameters into an interaction area; the temperature adjusting module monitors parameters of the interaction area, matches a preset algorithm to execute heating / cooling adjustment and feeds back a state; the temperature monitoring module updates the strategy according to the feedback. The response speed and the control precision of temperature control of the melting holding furnace can be improved, and the reliability and the maintainability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of industrial heating equipment, and more specifically, to an intelligent temperature control method and system for a melting and holding furnace. Background Art

[0002] In industrial production, melting and holding furnaces are widely used in metal smelting, glass manufacturing and other fields. The accuracy and stability of their temperature control have a vital impact on product quality and production efficiency. The traditional melting and holding furnace temperature control method mainly relies on a single sensor to collect temperature data and uses a simple PID control algorithm to adjust heating or cooling. However, this control method has many limitations. On the one hand, the data collected by a single sensor is easily affected by environmental interference, resulting in large measurement errors and an inability to accurately reflect the actual temperature conditions in the furnace; on the other hand, the traditional PID control algorithm has difficulty in achieving fast and accurate adjustment when faced with complex temperature change curves and multi-temperature zone control requirements. It is prone to overshoot, oscillation and other phenomena, affecting product quality and production efficiency. In addition, traditional temperature control systems usually lack effective protection mechanisms for actuators. During long-term operation, the actuators are prone to failure due to overload, overheating and other reasons, increasing equipment maintenance costs and downtime.

[0003] With the continuous development of industrial automation and intelligent technology, people have higher and higher performance requirements for the temperature control system of the melting and holding furnace, hoping to achieve more accurate, stable, efficient and safe temperature control. In the process of realizing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: when the existing melting and holding furnace temperature control system is controlled in multiple temperature zones, the temperature coupling effect between the temperature zones is not effectively compensated, resulting in reduced temperature control accuracy; the processing capacity of abnormal data is insufficient, and it is impossible to effectively filter noise and eliminate erroneous data, affecting the accuracy of control decisions; the actuator drive signal generation of the temperature control module lacks flexibility and safety, and cannot be dynamically adjusted according to different control requirements and equipment status, and protective measures cannot be taken in time when the actuator fails. Summary of the Invention

[0004] The present invention provides an intelligent temperature control method and system for a melting and holding furnace.

[0005] In a first aspect of the present invention, there is provided an intelligent temperature control method for a melting and holding furnace, comprising: The temperature control system of the melting and holding furnace is divided into a temperature monitoring module and a temperature regulating module; Establish a real-time data interaction area for the temperature monitoring module and the temperature adjustment module in the central controller; The temperature monitoring module divides the temperature control task into a data acquisition unit and an adjustment execution unit. It performs multi-sensor signal fusion processing on the data acquisition unit and dynamic parameter analysis on the adjustment execution unit, and writes the analyzed control parameters into the real-time data interaction area. The temperature adjustment module actively monitors the control parameters in the real-time data interaction area, performs heating power adjustment according to the control parameters and matches the preset temperature control algorithm, and feeds back the operating status to the real-time data interaction area; The temperature monitoring module reads the operating status from the real-time data interaction area and updates the control strategy.

[0006] Furthermore, the configuration of the temperature monitoring module and the temperature regulating module is determined based on the physical partition structure and actuator distribution of the melting and holding furnace; The number of temperature monitoring modules corresponds one-to-one to the independent temperature zones of the melting and holding furnace, and the remaining resources of the central controller are allocated to temperature regulation modules; Each temperature monitoring module is bound to a temperature zone, and heating power adjustment is allocated to the corresponding temperature regulation module according to the real-time temperature fluctuation of the temperature zone.

[0007] Furthermore, the temperature adjustment module establishes a subordinate relationship with the designated temperature monitoring module, receives the adjustment tasks assigned by the temperature monitoring module and feeds back the execution status; the temperature adjustment module only runs the underlying logic directly related to the temperature closed-loop control, which includes the execution of the PID algorithm and the generation of actuator drive signals.

[0008] Furthermore, the temperature control algorithm performs the following steps: According to the algorithm type identifier in the control parameter, the preset fuzzy PID algorithm or predictive control algorithm is called; Based on the output result of the fuzzy PID algorithm or the predictive control algorithm, a PWM drive signal is generated in combination with the actuator address code; The generated PWM drive signal is sent to the actuator, and the actual output parameters are written back into the dedicated data channel of the real-time data interaction area.

[0009] Furthermore, the control instruction data block includes: Current temperature zone number and priority identifier; Allowable temperature fluctuation threshold range; Temperature regulation algorithm type identifier; Associate the address code and drive parameters of the actuator.

[0010] Furthermore, the temperature regulation module has a built-in instruction parsing engine, which scans the dedicated data channel of the real-time data interaction area at a fixed period to extract the control instruction data block; calls the preset fuzzy PID algorithm or predictive control algorithm according to the algorithm type identifier, generates a PWM drive signal in combination with the actuator address code, and writes the actual output parameters back to the dedicated data channel.

[0011] In a second aspect of the present invention, an intelligent temperature control system for a melting and holding furnace is provided, comprising: Central controller, real-time data interaction area, temperature control algorithm library; The central controller includes a temperature monitoring module cluster and a temperature regulation module cluster; The real-time data interaction area uses dual-port RAM to achieve millisecond-level data synchronization for control command transmission between the temperature monitoring module cluster and the temperature regulation module cluster; The temperature control algorithm library stores fuzzy PID algorithm, predictive control algorithm and actuator protection logic; The temperature monitoring module cluster runs a temperature closed-loop management program to achieve multi-sensor data fusion, dynamic control strategy generation, and control instruction distribution; The temperature regulation module cluster runs the real-time control program, parses the control instructions and drives the heating rod to complete the temperature regulation.

[0012] Furthermore, each temperature monitoring module in the temperature monitoring module cluster independently executes: Thermocouple data of the bound temperature zones is collected through Modbus polling, and Kalman filtering is used to eliminate signal noise caused by furnace vibration. Predict the thermal inertia parameters of the current temperature zone based on historical temperature data and dynamically adjust the integral time constant of the fuzzy PID algorithm; Generate a control instruction data block containing the target temperature slope and maximum overshoot limit, and send it to the associated temperature control module through the real-time data interaction area.

[0013] Furthermore, the control instruction data block also includes: Temperature coupling compensation coefficient of adjacent temperature zones; Emergency overheat protection trigger threshold; The maximum continuous working time limit of the actuator.

[0014] Furthermore, each temperature adjustment module in the temperature adjustment module cluster includes: Drive signal safety verification unit to verify that the PWM duty cycle does not exceed the rated power of the heating rod; State feedback unit, converting the actual heating current value into a standardized data format; The abnormal fuse unit cuts off the drive signal and triggers the alarm mark in the real-time data interaction area when it detects abnormal impedance of the actuator.

[0015] According to the above-mentioned embodiments of the present invention, there are at least the following beneficial effects: the intelligent temperature control method and system of the present invention can achieve precise temperature control of the melting and holding furnace. By dividing the temperature control system into a temperature monitoring module and a temperature adjustment module, and establishing a real-time data interaction area, rapid data transmission and sharing can be achieved. The temperature monitoring module adopts multi-sensor signal fusion processing, which can effectively improve the accuracy and reliability of temperature data and reduce measurement errors caused by single sensor failure or interference. At the same time, according to the deviation value between the target temperature curve and the real-time temperature data, the adjustment requirements are decomposed into discrete control steps, and detailed control instruction data blocks are generated, so that the temperature adjustment module can perform the heating power adjustment task more accurately. In addition, the temperature adjustment module has a built-in instruction parsing engine, which can call the preset fuzzy PID algorithm or predictive control algorithm according to different algorithm type identifiers, further improving the flexibility and adaptability of temperature control, and can better cope with complex temperature change curves and multi-temperature zone control requirements.

[0016] The present invention can also improve the safety and stability of the temperature control system of the melting and holding furnace. The temperature monitoring module is configured based on the physical partition structure and actuator distribution of the melting and holding furnace. Each temperature monitoring module is bound to a temperature zone and can assign tasks to the corresponding temperature adjustment module according to the real-time temperature fluctuation of the temperature zone, so as to achieve independent control and management of each temperature zone and avoid mutual interference between temperature zones. Each temperature adjustment module in the temperature adjustment module cluster includes a drive signal safety verification unit, a state feedback unit and an abnormal fuse unit, which can perform safety verification on the drive signal of the actuator to prevent the heating rod from being overloaded and damaged due to an excessively high PWM duty cycle; convert the actual heating current value into a standardized data format for state feedback, so as to facilitate real-time monitoring of the equipment operation status; when the actuator impedance abnormality is detected, the drive signal can be cut off in time and the alarm mark can be triggered, effectively protecting the actuator from damage, extending the service life of the equipment, and reducing equipment maintenance costs and downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which: Figure 1 A schematic flow chart of an intelligent temperature control method for a melting and holding furnace provided in one embodiment of the present invention; Figure 2 A schematic structural diagram of an intelligent temperature control system for a melting and holding furnace provided in one embodiment of the present invention; Figure 3 The figure schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0019] Those skilled in the art will appreciate that embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0020] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0021] Reference below Figure 1 , Figure 1 Schematic diagram of the flow of the intelligent temperature control method of the melting and holding furnace provided by one embodiment of the present invention. Figure 1 As shown, an intelligent temperature control method for a melting and holding furnace includes: S1. Divide the temperature control system of the melting and holding furnace into a temperature monitoring module and a temperature regulating module; S2. Establishing a real-time data interaction area for the temperature monitoring module and the temperature adjustment module in the central controller; S3. The temperature monitoring module divides the temperature control task into a data acquisition unit and an adjustment execution unit, performs multi-sensor signal fusion processing on the data acquisition unit, performs dynamic parameter analysis on the adjustment execution unit, and writes the analyzed control parameters into the real-time data interaction area; S4. The temperature adjustment module actively monitors the control parameters in the real-time data interaction area, performs heating power adjustment according to the control parameters and the preset temperature control algorithm, and feeds back the operation status to the real-time data interaction area; S5. The temperature monitoring module reads the operating status from the real-time data interaction area and updates the control strategy.

[0022] It should be noted that the present invention divides the temperature control system of the melting and holding furnace into a temperature monitoring module and a temperature regulating module. This division is to achieve functional specialization and coordination. The temperature monitoring module is mainly responsible for the collection and processing of temperature data. It obtains the temperature information of each temperature zone in the furnace through a variety of sensors, and performs data fusion and analysis to generate accurate control parameters. The temperature regulating module performs specific heating operations and adjusts the temperature state in the furnace according to these control parameters. The real-time data interaction area in the central controller is a bridge for data transmission between the two modules, ensuring the timeliness and accuracy of the control instructions. Among them, multi-sensor signal fusion processing refers to the integration of data from multiple sensors, eliminating noise and outliers through specific algorithms, and improving the reliability of the data; dynamic parameter analysis is to dynamically adjust the control parameters according to real-time data to adapt to different temperature control requirements.

[0023] Specifically, the temperature monitoring module divides the temperature control task into a data acquisition unit and an adjustment execution unit. The data acquisition unit synchronously collects temperature data of each temperature zone of the melting and holding furnace through a thermocouple array and an infrared temperature measurement unit. The thermocouple array is a commonly used temperature measurement device that can convert temperature changes into electrical signals, while the infrared temperature measurement unit measures temperature by detecting infrared radiation on the surface of an object. The combination of the two can provide more comprehensive temperature information. The adjustment execution unit is responsible for performing specific heating operations based on the analyzed control parameters. Dynamic parameter analysis refers to the dynamic adjustment of control parameters, such as heating power, based on the temperature data collected in real time and the preset control targets. The setting of these parameters needs to be determined according to the specific application scenarios and process requirements of the melting and holding furnace. For example, in the metal smelting process, the adjustment of the heating power needs to take into account the melting point and melting speed of the metal.

[0024] Preferably, the temperature monitoring module uses redundancy check and sliding average filtering when performing multi-sensor signal fusion processing. Redundancy check refers to identifying and eliminating outliers by comparing data from multiple sensors to ensure data accuracy. Sliding average filtering is a commonly used signal processing method that smoothes data fluctuations and reduces noise interference by averaging continuous data points. When generating the control instruction data block, the adjustment requirements are decomposed into discrete control steps based on the deviation between the target temperature curve and the real-time temperature data. The target temperature curve is pre-set according to the process requirements of the melting and holding furnace and reflects the temperature change pattern in the furnace over time. Real-time temperature data is acquired in real time through the data acquisition unit. The deviation value is calculated by comparing the difference between the target temperature and the actual temperature. Based on this deviation value, the system will dynamically adjust the control strategy and generate a control instruction data block containing a control type identifier, an algorithm identifier, and a target parameter threshold. These instruction data blocks will be written to the real-time data interaction area for the temperature adjustment module to read and execute.

[0025] In some embodiments, the configuration of the temperature monitoring module and the temperature regulating module is determined based on the physical partition structure and actuator distribution of the melting and holding furnace; The number of temperature monitoring modules corresponds one-to-one to the independent temperature zones of the melting and holding furnace, and the remaining resources of the central controller are allocated to temperature regulation modules; Each temperature monitoring module is bound to a temperature zone, and heating power adjustment is allocated to the corresponding temperature regulation module according to the real-time temperature fluctuation of the temperature zone.

[0026] It should be noted that the configuration of the temperature monitoring module and the temperature regulation module in the present invention is determined based on the physical partition structure and actuator distribution of the melting and holding furnace. The physical partition structure refers to the different temperature zones inside the melting and holding furnace divided according to process requirements. Each zone needs to independently control the temperature to meet different processing requirements. The actuator distribution refers to the layout of the equipment used for heating, such as the heating rod in the furnace. The number of temperature monitoring modules corresponds one-to-one to the independent temperature zones of the melting and holding furnace, which means that each temperature zone has a dedicated temperature monitoring module to monitor its temperature changes and ensure the temperature control accuracy of each zone. The remaining resources of the central controller are allocated to the temperature regulation module to perform specific heating operations. This resource allocation method can improve the overall efficiency and response speed of the system.

[0027] Specifically, the configuration of the temperature monitoring module needs to be set according to the actual structure of the melting and holding furnace. For example, if the melting and holding furnace is divided into three independent temperature zones, then three temperature monitoring modules need to be configured, and each module is responsible for the temperature monitoring of one temperature zone. After each temperature monitoring module is bound to a temperature zone, it will allocate heating power adjustment to the corresponding temperature regulation module based on the real-time temperature fluctuations of the temperature zone. The binding here refers to the association of the temperature monitoring module with a specific temperature zone, ensuring that each module only focuses on its corresponding temperature zone. The heating power adjustment task refers to dynamically adjusting the power of the heating equipment based on the data provided by the temperature monitoring module to maintain the temperature of the temperature zone within the set range. The allocation of tasks needs to take into account factors such as the temperature target of the temperature zone, the current temperature, and the temperature change rate.

[0028] The temperature monitoring module preferably considers multiple parameters when assigning tasks. For example, when assigning a heating power adjustment task, the required heating power is calculated based on the difference between the current temperature in the temperature zone and the target temperature. If the target temperature is higher than the current temperature, the system increases the heating power; otherwise, it decreases the heating power. The specific heating power calculation can be achieved through a simple proportional relationship, where the heating power is proportional to the temperature difference. Parameter setting and adjustment can be achieved through a preset algorithm, which is dynamically updated based on real-time data to ensure accurate and stable temperature control.

[0029] In some embodiments, the temperature adjustment module establishes a subordinate relationship with a designated temperature monitoring module, receives adjustment tasks assigned by the temperature monitoring module and feeds back the execution status; the temperature adjustment module only runs the underlying logic directly related to temperature closed-loop control, and the underlying logic includes PID algorithm execution and actuator drive signal generation.

[0030] It should be noted that the temperature adjustment module in the present invention establishes a subordinate relationship with the designated temperature monitoring module. This subordinate relationship means that the temperature adjustment module needs to receive the adjustment task assigned by the temperature monitoring module when executing the task, and feed back the execution status to the temperature monitoring module. This design can ensure that the operation of the temperature adjustment module is closely coordinated with the real-time data provided by the temperature monitoring module, thereby achieving precise temperature control. The temperature adjustment module only runs the underlying logic directly related to the closed-loop temperature control. The underlying logic includes the execution of the PID algorithm and the generation of actuator drive signals. These functions are the core tasks of the temperature adjustment module, ensuring the real-time and stability of the temperature control.

[0031] Specifically, the subordinate relationship between the temperature adjustment module and the temperature monitoring module can be achieved through communication protocols and data interaction. For example, the temperature monitoring module will send control instructions to the temperature adjustment module through the real-time data interaction area. These instructions include specific parameters for adjusting the heating power. After receiving the instructions, the temperature adjustment module will execute the corresponding tasks and feedback the execution status to the temperature monitoring module. The PID algorithm execution in the underlying logic refers to adjusting the working state of the heating equipment through the three parameters of proportion, integration and differentiation according to the PID control principle to achieve the set temperature target. The actuator drive signal generation is based on the output results of the PID algorithm to generate the drive signal for controlling the heating rod. These signals will directly act on the actuator to make it work according to the predetermined parameters.

[0032] Preferably, the temperature regulation module will perform a series of optimization operations when performing tasks. For example, during the execution of the PID algorithm, the PID parameters will be dynamically adjusted according to the real-time temperature data to adapt to different temperature changes. Specifically, if the temperature changes rapidly, the weight of the differential parameter will be increased to reduce overshoot; if the temperature changes slowly, the weight of the integral parameter will be increased to eliminate steady-state errors. In terms of actuator drive signal generation, a suitable drive signal will be generated based on parameters such as the maximum speed of the rated power of the heating rod. For example, the drive signal of the heating rod will adjust the PWM duty cycle according to the required heating power to ensure that the heating rod operates within a safe range; the above-mentioned optimization operations can be achieved through a preset algorithm model, which will dynamically adjust the output results according to the input real-time data and the preset parameter range, thereby improving the accuracy and stability of temperature control.

[0033] In some embodiments, the temperature monitoring module performs the following operations: The temperature data of each temperature zone of the melting and holding furnace is collected synchronously through the thermocouple array and infrared temperature measurement unit, and abnormal data is subjected to redundancy check and sliding average filtering; According to the deviation between the target temperature curve and the real-time temperature data, the regulation demand is decomposed into discrete control steps that can be executed by the temperature regulation module, and a control instruction data block containing a control type identifier, an algorithm identifier and a target parameter threshold is generated; The control instruction data block is written into the dedicated data channel associated with the target temperature adjustment module in the real-time data interaction area.

[0034] It should be noted that the temperature monitoring module in the present invention performs operations including synchronously collecting temperature data from each temperature zone of the melting and holding furnace using a thermocouple array and an infrared temperature measurement unit, and performing redundancy checking and sliding average filtering on abnormal data. The thermocouple array is a temperature sensor array that converts temperature changes into electrical signals, while the infrared temperature measurement unit measures temperature by detecting infrared radiation from the surface of an object. The combined use of these two sensors can provide more comprehensive and accurate temperature data. Redundancy checking involves comparing data from multiple sensors to identify and eliminate outliers, ensuring data accuracy. Sliding average filtering is a common signal processing method that averages consecutive data points to smooth data fluctuations and reduce noise interference. In addition, the temperature monitoring module also decomposes the control requirements into discrete control steps executable by the temperature control module based on the deviation between the target temperature curve and the real-time temperature data. It generates a control instruction data block containing a control type identifier, an algorithm identifier, and a target parameter threshold, and writes this data block to a dedicated data channel associated with the target temperature control module in the real-time data interaction area.

[0035] Specifically, the operation of the temperature monitoring module involves several key steps and parameters. First, synchronized data acquisition by the thermocouple array and infrared temperature measurement unit involves these two sensors simultaneously measuring the temperature in each temperature zone of the melting and holding furnace to ensure data timeliness and consistency. Redundancy checking aims to identify and eliminate outliers by comparing data from multiple sensors. For example, if the data from a particular sensor deviates significantly from that of other sensors, it may be considered an outlier and eliminated. Sliding average filtering reduces temperature fluctuations caused by sensor noise or environmental interference by averaging continuously collected temperature data. The target temperature curve is pre-set based on the process requirements of the melting and holding furnace and reflects the temperature variation within the furnace over time. Real-time temperature data is acquired in real time by the data acquisition unit. Deviation values are calculated by comparing the difference between the target and actual temperatures. Based on this deviation, the system dynamically adjusts the control strategy and generates a control instruction data block containing a control type identifier, algorithm identifier, and target parameter thresholds. The control type identifier is used to indicate the type of control operation, such as heating; the algorithm identifier is used to specify the control algorithm used, such as PID algorithm or fuzzy control algorithm; the target parameter threshold is the specific parameter value that the control operation needs to achieve, such as temperature value or power value.

[0036] Preferably, the temperature monitoring module performs more detailed operations when generating the control instruction data block. For example, during redundancy checking, the system sets a threshold. When the data from a particular sensor deviates from that of other sensors by more than this threshold, the data is marked as abnormal and discarded. During the sliding average filtering process, an appropriate window size is selected based on the fluctuating characteristics of the temperature data. The window size determines the number of data points involved in the averaging calculation. A larger window size improves the filtering effect, but also results in slightly slower response speed. When generating the control instruction data block, the control instruction parameters are dynamically adjusted based on the size and trend of the deviation value. For example, if the deviation value is large and increasing, the heating power may be increased; if the deviation value is small and stable, the current control parameters may be maintained or fine-tuned. These operations can be implemented using a preset algorithm model, which dynamically adjusts the output based on the real-time input data and the preset parameter range, thereby improving the accuracy and stability of temperature control.

[0037] In some embodiments, the temperature control algorithm performs the following steps: According to the algorithm type identifier in the control parameter, the preset fuzzy PID algorithm or predictive control algorithm is called; Based on the output result of the fuzzy PID algorithm or the predictive control algorithm, a PWM drive signal is generated in combination with the actuator address code; The generated PWM drive signal is sent to the actuator, and the actual output parameters are written back into the dedicated data channel of the real-time data interaction area.

[0038] Specifically, after extracting control parameters from the real-time data interaction area, the temperature adjustment module first parses the algorithm type identifier. If the algorithm type identifier points to the fuzzy PID control mode, the preset fuzzy PID algorithm is invoked. This algorithm dynamically adjusts the weight coefficients of the proportional, integral, and differential parameters based on the real-time temperature deviation and its rate of change. If the identifier points to the predictive control mode, the predictive control algorithm is activated, and the heating power output is continuously optimized based on the predicted temperature changes over multiple control cycles.

[0039] The target heater's position is determined based on the selected control algorithm's output and the actuator's address code. The power adjustment calculated by the algorithm is converted into a corresponding PWM signal duty cycle value. A double safety check is performed before transmission: first, the duty cycle is verified to not exceed 95% of the heater's rated power, and second, the maximum power limit allowed for the current temperature zone is checked. If the safety range is exceeded, the power is automatically reduced proportionally to the compliance threshold.

[0040] The verified PWM drive signal is sent to the designated actuator via the industrial bus, while the actual operating current and voltage data of the heater rod are collected in real time. The actual operating parameters, including the actual duty cycle of the drive signal, the effective current value, the instantaneous voltage value, and the timestamp information, are written back to the dedicated data channel of the real-time data interaction area in a predefined standardized format. If the actual current deviates from the theoretical calculated value by more than 15% for a sustained period, the fuzzy PID algorithm's membership function adaptive adjustment mechanism is triggered to recalibrate the control parameters.

[0041] In some embodiments, the control instruction data block includes: Current temperature zone number and priority identifier; Allowable temperature fluctuation threshold range; Temperature regulation algorithm type identifier; Associate the address code and drive parameters of the actuator.

[0042] It should be noted that the control instruction data block is a data structure generated by the temperature monitoring module and is used to transmit temperature control requirements to the temperature regulation module in a standardized form. It contains multiple key information, such as the current temperature zone number and priority identifier, the allowable temperature fluctuation threshold range, the temperature regulation algorithm type identifier, the address code and drive parameters of the associated actuator, etc. This information ensures that the temperature regulation module can accurately understand and execute the instructions of the temperature monitoring module, thereby achieving precise temperature control. The current temperature zone number is used to identify the specific temperature zone, the priority identifier is used to determine the processing order when multiple temperature zones need to be adjusted simultaneously, the allowable temperature fluctuation threshold range defines the acceptable temperature deviation range, the temperature regulation algorithm type identifier specifies the control algorithm to be used, and the address code and drive parameters of the associated actuator are used to guide the specific heating operation.

[0043] Specifically, each parameter in the control instruction data block has a clear meaning and function. The current temperature zone number is a unique identifier used to distinguish different temperature zones in the melting and holding furnace. The priority identifier is a numerical value used to indicate the priority of the current temperature zone in the temperature control task. For example, when multiple temperature zones need to be adjusted at the same time, the temperature zone with a higher priority will be processed first. The allowable temperature fluctuation threshold range is a temperature interval. For example, if it is set to ±5°C, it means that the temperature of the current temperature zone can fluctuate 5°C above and below the target temperature. The temperature control algorithm type identifier is a code used to indicate which algorithm the temperature control module uses for control, such as the PID algorithm or the fuzzy control algorithm. The address code of the associated actuator is an address identifier used to specify a specific actuator such as a heating rod, and the drive parameters include specific control parameters, such as the speed of the heating power, etc.

[0044] Preferably, the process for generating control instruction data blocks can be further refined. For example, when generating the current temperature zone number, the system assigns a unique number to each zone based on the physical partitioning structure of the melting and holding furnace. Priority identifiers can be set based on process requirements and the importance of the temperature zone; for example, temperature zones closer to the furnace core have higher priority. The allowable temperature fluctuation threshold range can be set based on the thermal sensitivity of the material; for heat-sensitive materials, the fluctuation range can be set to a smaller value. The choice of temperature control algorithm type identifier can be determined based on the complexity of the temperature control. For simple temperature control tasks, a PID algorithm can be used; for complex temperature change curves, a fuzzy control algorithm can be used. The address code and drive parameters of the associated actuators are set based on the specific device model and control requirements. For example, the drive parameters of the heating rod can include the PWM duty cycle. These parameters can be set using a preset algorithm model. The model dynamically adjusts the output results based on the input real-time data and preset process requirements, thereby improving the accuracy and stability of temperature control.

[0045] In some embodiments, the temperature regulation module has a built-in instruction parsing engine, which scans the dedicated data channel of the real-time data interaction area at a fixed period to extract the control instruction data block; calls the preset fuzzy PID algorithm or predictive control algorithm according to the algorithm type identifier, generates a PWM drive signal or valve opening instruction in combination with the actuator address code, and writes the actual output parameters back to the dedicated data channel.

[0046] It should be noted that the temperature control module in the present invention has a built-in instruction parsing engine, which scans the dedicated data channel of the real-time data interaction area at a fixed period to extract the control instruction data block. The function of the instruction parsing engine is to parse the information in the control instruction data block and call the preset fuzzy PID algorithm or predictive control algorithm according to the algorithm type identifier. The fuzzy PID algorithm is an algorithm that combines fuzzy logic and PID control and can handle nonlinear and uncertainty problems, while the predictive control algorithm adjusts the control strategy in advance by predicting future temperature changes. Combined with the actuator address code, a PWM drive signal or a valve opening instruction is generated, and the actual output parameters are written back to the dedicated data channel to ensure that the temperature control module can dynamically adjust the control strategy according to the real-time data to achieve precise temperature control.

[0047] Specifically, the instruction parsing engine is the core component of the temperature control module. It scans the dedicated data channel of the real-time data interaction zone at a fixed period, such as once per second, to ensure timely access to the latest control instruction data block. The control instruction data block contains various information, such as the current temperature zone number, priority identifier, allowable temperature fluctuation threshold range, temperature control algorithm type identifier, address code of the associated actuator, and drive parameters. The algorithm type identifier is a key parameter that indicates which control algorithm the temperature control module uses. For example, if the identifier is 1, it indicates the fuzzy PID algorithm; if the identifier is 2, it indicates the predictive control algorithm. The PWM drive signal is a pulse-width modulated signal used to control the power output of the heating rod. These signals and instructions are generated based on the output of the control algorithm to ensure that the actuator can operate according to the predetermined parameters.

[0048] Preferably, the instruction parsing engine will perform more detailed operations when extracting and parsing control instruction data blocks. For example, when scanning dedicated data channels, the engine will check the integrity and accuracy of the data to ensure that there is no data loss or error. When calling the fuzzy PID algorithm, the PID parameters will be dynamically adjusted according to the temperature deviation and change rate of the current temperature zone to achieve better control effects. For the predictive control algorithm, future temperature changes will be predicted based on historical temperature data and current trends, and the control strategy will be adjusted in advance. When generating PWM drive signals or valve opening instructions, the safe operating range of the actuator will be considered, for example, ensuring that the PWM duty cycle does not exceed the rated power of the heating rod and the valve opening does not exceed the maximum allowable value. These operations can be achieved through a preset algorithm model, which will dynamically adjust the output results based on the input real-time data and the preset parameter range, thereby improving the accuracy and stability of temperature control.

[0049] The aforementioned embodiments of the present invention have the following beneficial effects: The present invention utilizes a modular architecture design, decoupling temperature monitoring and regulation functions. Millisecond-level coordinated control is achieved through real-time data interaction zones, significantly improving the response speed and accuracy of independent control across multiple temperature zones. The monitoring and regulation modules, configured based on physical partitions, precisely match the structural characteristics of the melting furnace. Combined with multi-sensor fusion and dynamic parameter analysis techniques, they eliminate the signal lag and execution deviation issues inherent in traditional control, reducing the temperature fluctuation range by over 30%.

[0050] Through the structured transmission mechanism of control command data blocks, complex temperature control requirements can be converted into discrete executable steps. Combined with pre-configured fuzzy PID and predictive control algorithms, it can adaptively handle differences in thermal inertia under different operating conditions. The command parsing engine and safety verification unit work together to ensure the accuracy of actuator drive parameters. At the same time, the abnormal fuse mechanism can actively prevent overload risks, allowing the system to maintain process accuracy while improving equipment safety.

[0051] like Figure 2 As shown, some embodiments provide an intelligent temperature control system for a melting and holding furnace, the system comprising: Central controller 201, real-time data interaction area 202, temperature adjustment algorithm library 203; The central controller includes a temperature monitoring module cluster and a temperature regulation module cluster; The real-time data interaction area uses dual-port RAM to achieve millisecond-level data synchronization for control command transmission between the temperature monitoring module cluster and the temperature regulation module cluster; The temperature control algorithm library stores fuzzy PID parameter self-tuning algorithm, multi-step prediction temperature control algorithm and actuator protection logic; The temperature monitoring module cluster runs a temperature closed-loop management program to achieve multi-sensor data fusion, dynamic control strategy generation, and control instruction distribution; The temperature regulation module cluster runs the real-time control program, parses the control instructions and drives the heating rod to complete the temperature regulation.

[0052] It is understandable that the modules recorded in the intelligent temperature control system of the melting and holding furnace are the same as those in the reference Figure 1 The steps in the intelligent temperature control method for a melting and holding furnace described above correspond to each other. Therefore, the operations, features, and beneficial effects described above for the intelligent temperature control method for a melting and holding furnace are also applicable to the intelligent temperature control system for a melting and holding furnace and the modules contained therein, and will not be repeated here.

[0053] In some embodiments, each temperature monitoring module in the temperature monitoring module cluster independently performs: (1) Thermocouple data of the bound temperature zone is collected through Modbus bus polling, and Kalman filtering is used to eliminate signal noise caused by furnace vibration; (2) Predict the thermal inertia parameters of the current temperature zone based on historical temperature data and dynamically adjust the integral time constant of the PID control algorithm; (3) Generate a control instruction data block containing the target temperature slope and the maximum overshoot limit, and send it to the associated temperature control module through the real-time data interaction area.

[0054] It should be noted that each temperature monitoring module in the temperature monitoring module cluster of the present invention independently performs a series of operations to ensure the temperature control accuracy of each temperature zone of the melting and holding furnace. These operations include collecting thermocouple data from the bound temperature zone through Modbus bus polling and using Kalman filtering to eliminate signal noise caused by furnace body vibration. Modbus bus is a serial communication protocol widely used for data transmission between devices in industrial environments. Thermocouples are temperature sensors that can convert temperature changes into electrical signals. Kalman filtering is an efficient self-recursive filter that can estimate the dynamic state of the system from a series of noisy measurements and is suitable for eliminating signal noise caused by external interference such as furnace body vibration. In addition, the thermal inertia parameters of the current temperature zone are predicted based on historical temperature data, and the integral time constant of the PID control algorithm is dynamically adjusted to adapt to different temperature change characteristics. Finally, a control instruction data block containing the target temperature slope and maximum overshoot limit is generated and sent to the associated temperature control module through the real-time data interaction area to ensure the accuracy and stability of temperature control.

[0055] Specifically, Modbus polling acquisition involves the temperature monitoring module periodically querying the thermocouple sensors in the bound temperature zones via the Modbus protocol to obtain temperature data. The polling period can be set according to actual needs, for example, once per second, to ensure real-time data availability. A Kalman filter processes the collected temperature data. By establishing a system state model and observation model and applying a recursive algorithm to filter the data, it effectively eliminates signal noise caused by factors such as furnace vibration. The thermal inertia parameter refers to the inertial characteristics exhibited by a temperature zone during temperature changes. By analyzing historical temperature data, the thermal inertia parameter of the current temperature zone can be predicted, enabling dynamic adjustment of the integral time constant in the PID control algorithm. The integral time constant is a key parameter in the PID control algorithm, determining the degree of influence of the integral term on the controlled variable. Dynamic adjustment of this parameter allows for better adaptation to varying temperature variations and improves control accuracy. The target temperature slope refers to the rate of change of the target temperature over time, and the maximum overshoot limit specifies the maximum overshoot allowed during temperature regulation. These two parameters together determine the dynamic performance and stability of temperature regulation.

[0056] Preferably, the temperature monitoring module can be further refined when performing the above operations. For example, when using Kalman filtering, the initial parameters of the filter, such as the process noise covariance and the observation noise covariance, need to be set based on the characteristics of the thermocouple sensor and the actual situation of the furnace vibration. The setting of these parameters can be optimized through experiments and simulations to ensure the filtering effect. When predicting thermal inertia parameters, a mathematical model based on historical temperature data can be established, and the thermal inertia parameters of the current temperature zone can be predicted through curve fitting or machine learning algorithms. When dynamically adjusting the integral time constant of the PID control algorithm, the specific value of the integral time constant can be determined by looking up a table or calculating a formula based on the predicted thermal inertia parameters, combined with the current temperature deviation and rate of change. When generating the control instruction data block, the specific values of the target temperature slope and the maximum overshoot limit can be calculated based on the target temperature curve and real-time temperature data. For example, the target temperature slope can be calculated by the difference between the target temperature and the current time, and the maximum overshoot limit can be set according to the process requirements and material properties. These operations can be achieved through a preset algorithm model, which will dynamically adjust the output results based on the input real-time data and the preset parameter range, thereby improving the accuracy and stability of temperature control.

[0057] In some embodiments, the control instruction data block further includes: Temperature coupling compensation coefficient of adjacent temperature zones; Emergency overheat protection trigger threshold; The maximum continuous working time limit of the actuator.

[0058] It should be noted that, in addition to the basic control information, the control instruction data block in the present invention also adds three parameters: the temperature coupling compensation coefficient of adjacent temperature zones, the emergency overheating protection trigger threshold, and the maximum continuous working time limit of the actuator. The purpose of these newly added parameters is to further optimize the accuracy and safety of temperature control. The temperature coupling compensation coefficient of adjacent temperature zones is used to compensate for the possible temperature influence between temperature zones, ensuring that the temperature control of each temperature zone is more independent and accurate. The emergency overheating protection trigger threshold is a safety mechanism used to take protective measures in time when the temperature rises abnormally to prevent equipment damage or safety accidents. The maximum continuous working time limit of the actuator is to protect actuators such as heating rods to avoid overheating or damage to the equipment due to long-term continuous operation.

[0059] Specifically, the temperature coupling compensation coefficient of adjacent temperature zones is a numerical value used to quantify the degree of mutual influence between the temperatures of adjacent temperature zones. For example, if the temperature change in one temperature zone will have a certain impact on the adjacent temperature zone, this coefficient can be used to adjust the control strategy to reduce this mutual interference. The emergency overheating protection trigger threshold is a temperature value. When the detected temperature exceeds this threshold, the system will immediately start the protection mechanism, such as cutting off the heating power supply. The maximum continuous working time limit of the actuator is a time value used to specify the maximum allowable time for actuators such as heating rods to work continuously. After this time, the system will automatically suspend the operation of the actuator to prevent the equipment from overheating or damage. The settings of these parameters need to be determined according to the specific design and process requirements of the melting and holding furnace. For example, the values of these parameters will be different for furnaces of different materials or different production processes.

[0060] Preferably, the setting of the temperature coupling compensation coefficient of adjacent temperature zones in the control instruction data block can be determined by experiments and simulations. For example, the coupling coefficient can be calculated by monitoring the temperature change relationship between temperature zones in actual production, or by establishing a heat conduction model. The setting of the emergency overheating protection trigger threshold needs to take into account the heat resistance of the material and the production safety requirements. It is usually set at a value slightly higher than the normal operating temperature to ensure that protective measures can be taken in time when the temperature rises abnormally. The setting of the maximum continuous working time limit of the actuator needs to be determined based on the performance parameters and service life of the actuator. For example, if the rated working time of the heating rod is 10 hours of continuous operation, then this limit value can be set to slightly less than 10 hours to ensure the safe operation of the equipment. The dynamic adjustment of these parameters can be achieved through a preset algorithm model. The model will automatically adjust the parameter values in the control instruction data block based on real-time monitoring data and preset safety thresholds, thereby improving the safety and reliability of the system.

[0061] In some embodiments, each temperature regulation module in the temperature regulation module cluster includes: Drive signal safety verification unit to verify that the PWM duty cycle does not exceed the rated power of the heating rod; State feedback unit, converting the actual heating current value into a standardized data format; The abnormal fuse unit cuts off the drive signal and triggers the alarm mark in the real-time data interaction area when it detects abnormal impedance of the actuator.

[0062] It should be noted that each temperature regulating module in the temperature regulating module cluster of the present invention includes a drive signal safety verification unit, a state feedback unit and an abnormal fuse unit. These units are provided to ensure the safety and reliability of the temperature regulating module when performing heating. The function of the drive signal safety verification unit is to verify that the PWM duty cycle does not exceed the rated power of the heating rod to prevent damage to the equipment due to overload. The state feedback unit converts the actual heating current value into a standardized data format to facilitate real-time monitoring of the operating status of the equipment. When the abnormal fuse unit detects that the actuator impedance is abnormal, it can cut off the drive signal in time and trigger an alarm mark to protect the equipment from damage.

[0063] Specifically, the drive signal safety verification unit is a key safety mechanism that ensures that the operating power of the heater does not exceed its rated value by monitoring the duty cycle of the PWM signal. For example, if the rated power of the heater is 1000 watts, the corresponding PWM duty cycle upper limit is set to a specific value, such as 80%, to ensure that the device operates within a safe range. The function of the state feedback unit is to convert operating parameters such as the actual heating current value into a standardized data format to facilitate real-time monitoring and analysis by the system. For example, the heating current value is converted into a percentage value from 0 to 100. The abnormal fuse unit determines whether there is an abnormality by monitoring the impedance change of the actuator. For example, if the impedance of the heater suddenly decreases, indicating that there is a short circuit risk, the abnormal fuse unit will immediately cut off the drive signal and trigger an alarm mark to prevent equipment damage or safety accidents.

[0064] Preferably, the drive signal safety verification unit can monitor the PWM duty cycle by setting a threshold. For example, the system will regularly check whether the duty cycle of the PWM signal exceeds a preset threshold, such as 80%. If exceeded, the system will automatically reduce the duty cycle to ensure that the operating power of the heating rod is within a safe range. The data conversion process of the state feedback unit can be implemented through a mapping table to map the actual current value and speed value to a standardized data format. For example, the heating current value can be converted into a percentage value through a linear mapping formula. The impedance monitoring of the abnormal fuse unit can be achieved through a real-time impedance detection circuit. When the detected impedance value is lower than a safety threshold, the system will trigger the fuse mechanism. The specific implementation of these units can ensure the safety and reliability of the system by combining hardware circuits and software algorithms.

[0065] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0066] like Figure 3 As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0067] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0068] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0069] The above descriptions merely illustrate some preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. An intelligent temperature control method for a melting and holding furnace, characterized in that: include: The temperature control system of the melting and holding furnace is divided into a temperature monitoring module and a temperature regulating module; Establish a real-time data interaction area for the temperature monitoring module and the temperature adjustment module in the central controller; The temperature monitoring module divides the temperature control task into a data acquisition unit and an adjustment execution unit. It performs multi-sensor signal fusion processing on the data acquisition unit and dynamic parameter analysis on the adjustment execution unit, and writes the analyzed control parameters into the real-time data interaction area. The temperature adjustment module actively monitors the control parameters in the real-time data interaction area, performs heating power adjustment according to the control parameters and matches the preset temperature control algorithm, and feeds back the operating status to the real-time data interaction area; The temperature monitoring module reads the operating status from the real-time data interaction area and updates the control strategy; The temperature monitoring module performs the following steps: The temperature data of each temperature zone of the melting and holding furnace is collected synchronously through the thermocouple array and infrared temperature measurement unit, and abnormal data is subjected to redundancy check and sliding average filtering; According to the deviation value between the target temperature curve and the real-time temperature data, the adjustment demand is decomposed into discrete control steps that can be executed by the temperature adjustment module, and a control instruction data block containing a control type identifier, an algorithm identifier and a target parameter threshold is generated; The control instruction data block is written into the dedicated data channel associated with the target temperature adjustment module in the real-time data interaction area.

2. The intelligent temperature control method according to claim 1, characterized in that: The configuration of the temperature monitoring module and the temperature regulating module is determined based on the physical partition structure and actuator distribution of the melting and holding furnace; The number of temperature monitoring modules corresponds one-to-one to the independent temperature zones of the melting and holding furnace, and the remaining resources of the central controller are allocated to the temperature regulation modules; Each temperature monitoring module is bound to a temperature zone, and heating power adjustment is allocated to the corresponding temperature regulation module according to the real-time temperature fluctuation of the temperature zone.

3. The intelligent temperature control method according to claim 1 or 2, characterized in that: The temperature adjustment module establishes a subordinate relationship with the designated temperature monitoring module, receives the adjustment tasks assigned by the temperature monitoring module and feeds back the execution status; the temperature adjustment module only runs the underlying logic directly related to the temperature closed-loop control, which includes the execution of the PID algorithm and the generation of actuator drive signals.

4. The intelligent temperature control method according to claim 1, characterized in that: The temperature control algorithm performs the following steps: According to the algorithm type identifier in the control parameter, the preset fuzzy PID algorithm or predictive control algorithm is called; Based on the output result of the fuzzy PID algorithm or the predictive control algorithm, a PWM drive signal is generated in combination with the actuator address code; The generated PWM drive signal is sent to the actuator, and the actual output parameters are written back into the dedicated data channel of the real-time data interaction area.

5. The intelligent temperature control method according to claim 4, characterized in that: The control instruction data block includes: Current temperature zone number and priority identifier; Allowable temperature fluctuation threshold range; Temperature regulation algorithm type identifier; Associate the address code and drive parameters of the actuator.

6. The intelligent temperature control method according to claim 5, characterized in that: The temperature regulation module has a built-in instruction parsing engine, which scans the dedicated data channel of the real-time data interaction area at a fixed period to extract the control instruction data block; calls the preset fuzzy PID algorithm or predictive control algorithm according to the algorithm type identifier, generates a PWM drive signal in combination with the actuator address code, and writes the actual output parameters back to the dedicated data channel.

7. An intelligent temperature control system for a melting and holding furnace, characterized in that: include: Central controller, real-time data interaction area, temperature control algorithm library; The central controller includes a temperature monitoring module cluster and a temperature regulation module cluster; The real-time data interaction area uses dual-port RAM to achieve millisecond-level data synchronization, which is used for the transmission of control instructions between the temperature monitoring module cluster and the temperature regulation module cluster; The temperature control algorithm library stores fuzzy PID algorithm, predictive control algorithm and actuator protection logic; The temperature monitoring module cluster runs a temperature closed-loop management program to achieve multi-sensor data fusion, dynamic control strategy generation, and control instruction distribution; The temperature regulation module cluster runs the real-time control program, parses the control instructions and drives the heating rod to complete the temperature regulation.

8. The intelligent temperature control system according to claim 7, characterized in that: Each temperature monitoring module in the temperature monitoring module cluster independently executes: Thermocouple data of the bound temperature zones is collected through Modbus polling, and Kalman filtering is used to eliminate signal noise caused by furnace vibration. Predict the thermal inertia parameters of the current temperature zone based on historical temperature data and dynamically adjust the integral time constant of the fuzzy PID algorithm; Generate a control instruction data block containing the target temperature slope and maximum overshoot limit, and send it to the associated temperature control module through the real-time data interaction area.

9. The intelligent temperature control system according to claim 8, characterized in that: The control instruction data block also includes: Temperature coupling compensation coefficient of adjacent temperature zones; Emergency overheat protection trigger threshold; The maximum continuous working time limit of the actuator.

10. The intelligent temperature control system according to claim 9, characterized in that: Each temperature regulating module in the temperature regulating module cluster includes: Drive signal safety verification unit to verify that the PWM duty cycle does not exceed the rated power of the heating rod; State feedback unit, converting the actual heating current value into a standardized data format; The abnormal fuse unit cuts off the drive signal and triggers the alarm mark in the real-time data interaction area when it detects abnormal impedance of the actuator.

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