PID controller parameter automatic tuning method and device

Abstract

The present invention relates to the technical field of temperature control, and discloses a method and device for automatically adjusting parameters of a PID controller. The present invention interacts with a user of a working terminal through an information inquiry port to determine an expected working mode of the working terminal, so as to adjust the temperature control modes of a main control unit and a sub-control unit of a temperature control module. The main control unit analyzes temperature characteristics based on a preset first PID temperature control algorithm, generates a first temperature control parameter, and transmits the first temperature control parameter to the sub-control unit. The sub-control unit analyzes temperature characteristics and the first control parameter based on a second PID algorithm, adjusts and generates a second temperature control parameter, drives an execution unit of the temperature control module according to the second temperature control parameter, performs temperature control processing, realizes automatic adjustment of PID controller parameters, improves temperature control accuracy and response speed, dynamically adjusts temperature control strategies according to different working modes, and the main control unit and the sub-control unit work together to ensure high efficiency and stability of the temperature control process, thereby solving the problem of low working efficiency of fixed temperature control modes in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to a method and device for automatically adjusting parameters of a PID controller. Background Art

[0002] When a work terminal such as a computer works for a long time, its own temperature will gradually rise, and when the work terminal is in different working modes, the temperature rise trend of each specific location of the work terminal and the resource demand for temperature control are different. In the traditional temperature control system, a fixed stable mode is used to perform temperature control operations on the work terminal. This operation will make the temperature control efficiency and resource utilization of the temperature control system for the work terminal not high enough. Summary of the invention

[0003] The object of the present invention is to provide a method and device for automatically adjusting parameters of a PID controller, aiming to solve the problem of low working efficiency of a fixed temperature control mode in the prior art.

[0004] The present invention is implemented in this way. In a first aspect, the present invention provides a method for automatically tuning parameters of a PID controller, comprising: Interacting with a user of a work terminal through an information inquiry port to obtain a work mode that the work terminal is expected to perform; According to the working mode, the temperature control mode of the temperature control module pre-configured on the working terminal is modulated, so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode; Collecting temperature data of the working terminal to obtain temperature characteristics of the working terminal; Analyzing the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmitting the first temperature control parameter to each of the sub-control units in signal communication with the main control unit; Performing a real-time analysis on the first temperature control parameter and the temperature characteristic according to a second PID temperature control algorithm preset in each of the sub-control units to obtain a second temperature control parameter; The execution unit in the temperature control module is driven to perform temperature control according to each of the second temperature control parameters, so that the execution unit performs temperature control processing on the working terminal.

[0005] In a second aspect, the present invention provides a PID controller parameter automatic tuning device, which is used to implement a PID controller parameter automatic tuning method according to any one of the first aspects, comprising: A mode analysis module, used to interact with a user of a work terminal through an information inquiry port to obtain a work mode that the work terminal is expected to execute; A mode switching module, used to modulate the temperature control mode of the temperature control module pre-configured on the working terminal according to the working mode, so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode; A temperature acquisition module, used to collect temperature data of the working terminal to obtain the temperature characteristics of the working terminal; a first temperature control module, configured to analyze the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmit the first temperature control parameter to each of the sub-control units in signal communication with the main control unit; a second temperature control module, configured to perform real-time analysis on the first temperature control parameter and the temperature characteristic according to a second PID temperature control algorithm preset in each of the sub-control units, so as to obtain a second temperature control parameter; The temperature control execution module is used to drive the execution unit in the temperature control module to perform temperature control according to each of the second temperature control parameters, so that the execution unit performs temperature control processing on the working terminal.

[0006] The present invention provides a PID controller parameter automatic tuning method, which has the following beneficial effects: The present invention interacts with the user of the working terminal through an information inquiry port to determine the expected working mode of the working terminal to adjust the temperature control modes of the main control unit and the sub-control unit of the temperature control module. The main control unit analyzes the temperature characteristics based on a preset first PID temperature control algorithm, generates a first temperature control parameter, and transmits it to the sub-control unit. The sub-control unit analyzes the temperature characteristics and the first control parameter based on a second PID algorithm, adjusts and generates the second temperature control parameter, drives the execution unit of the temperature control module according to the second temperature control parameter, performs temperature control processing, realizes automatic adjustment of the PID controller parameters, improves the temperature control accuracy and response speed, dynamically adjusts the temperature control strategy according to different working modes, enhances the adaptability of the system, and the main control and sub-control units work together to ensure the high efficiency and stability of the temperature control process, thereby solving the problem of low working efficiency of the fixed temperature control mode in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the steps of a method for automatic tuning parameters of a PID controller provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a PID controller parameter automatic tuning device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0008] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0009] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0010] Reference Figure 1 , Figure 2 As shown, a preferred embodiment of the present invention is provided.

[0011] In a first aspect, the present invention provides a method for automatically tuning parameters of a PID controller, comprising: S1: interacting with a user of a work terminal through an information inquiry port to obtain a work mode that the work terminal is expected to execute; S2: Modulating the temperature control mode of the temperature control module pre-configured on the working terminal according to the working mode, so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode; S3: collecting temperature data of the working terminal to obtain temperature characteristics of the working terminal; S4: Analyzing the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmitting the first temperature control parameter to each of the sub-control units in signal communication with the main control unit; S5: performing real-time analysis on the first temperature control parameter and the temperature characteristic according to a second PID temperature control algorithm preset in each of the sub-control units to obtain a second temperature control parameter; S6: driving the execution unit in the temperature control module to perform temperature control according to each of the second temperature control parameters, so that the execution unit performs temperature control processing on the working terminal.

[0012] Specifically, in step S1 of the embodiment provided by the present invention, preset target information is interacted with the user of the work terminal through the information inquiry port to obtain the work mode expected to be executed by the work terminal.

[0013] More specifically, the work terminal may be a personal computer terminal. When the work terminal is working, heat is generated, thereby causing the temperature of the work terminal to rise. The work terminal has multiple working modes, and the temperature change trends experienced by the work terminal in different working modes are different, so the corresponding working modes are also different.

[0014] More specifically, the information inquiry port allows users to interact with information through the interactive interface of the work terminal, so as to provide users with preset target information for using the work terminal in advance, thereby obtaining the work mode expected to be executed by the work terminal, including the requirements for scheduling various work resources, so as to analyze the temperature change pattern of the work terminal in actual work, and then provide for the subsequent temperature control module to respond.

[0015] Specifically, in step S2 of the embodiment provided by the present invention, the temperature control mode of the temperature control module pre-configured on the working terminal is modulated according to the working mode, so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode.

[0016] More specifically, the temperature control module includes a main control unit and various sub-control units. The main control unit and the sub-control units form a cascade temperature control structure. The main control unit performs overall temperature control analysis of the working terminal, and then each sub-control unit receives the output result of the overall temperature control analysis to perform real-time temperature control operations at each specific location of the working terminal. That is, the main control unit anchors a rough range, and then each sub-control unit performs detailed adjustments based on the real-time conditions of the specific location.

[0017] More specifically, since the temperature distribution and temperature changes of the working terminal are different in different working modes, the temperature control conditions and means of the corresponding working terminal will also be different, including the temperature control intensity in different time periods, the control distribution of temperature nodes at each specific location, etc. In order to implement the most suitable temperature control operation for the working mode to be performed by the working terminal, it is necessary to modulate the temperature control mode of the main control unit and each sub-control unit according to the working mode to switch to the corresponding temperature control mode.

[0018] Specifically, in step S3 of the embodiment provided by the present invention, the working terminal continuously collects temperature data through sensors. After the temperature data is collected, the characteristics of these data are analyzed in real time, including the current temperature, temperature fluctuation, change rate, temperature distribution, etc. Through continuous temperature data collection, the temperature status of the working terminal is monitored in real time to provide accurate input for the subsequent PID algorithm.

[0019] Specifically, in step S4 of the embodiment provided by the present invention, the main control unit analyzes the temperature characteristics collected from the temperature sensor according to a preset first PID temperature control algorithm (including proportional, integral, and differential control). The difference between the temperature characteristics and the target temperature (temperature error) is sent to the PID control algorithm as an input signal to generate a first temperature control parameter. By analyzing the temperature characteristics through the PID algorithm, the system can dynamically adjust the temperature control parameters to ensure that the temperature deviation is corrected in time. The PID algorithm can effectively reduce temperature fluctuations and ensure that the working terminal is maintained near the set target temperature.

[0020] More specifically, the main control unit transmits the first temperature control parameters (such as control gain, adjustment amount, etc.) calculated according to the PID algorithm to each sub-control unit, and each sub-control unit further performs local adjustments based on the first temperature control parameters transmitted by the main control unit. The cooperation between the main control unit and the sub-control unit can share the control tasks and improve the parallel processing capability and stability of the system. By transmitting the control parameters to the sub-control units, the local response of the temperature control system can be guaranteed to be faster and more accurate.

[0021] Specifically, in step S5 of the embodiment provided by the present invention, each sub-control unit uses its own second PID temperature control algorithm to further analyze the temperature characteristics and the first temperature control parameters from the main control unit. The sub-control unit performs local calculations on the received first temperature control parameters, and adjusts and generates second temperature control parameters based on local temperature data and the first temperature control parameters. The sub-control unit can fine-tune the temperature control response based on local temperature data and global control parameters, thereby avoiding excessive burden on the main control unit and improving the response speed. The sub-control unit can perform optimization adjustments in the local area based on real-time data to further improve the stability and accuracy of the system.

[0022] Specifically, in step S6 of the embodiment provided by the present invention, each sub-control unit drives the execution unit in the temperature control module to perform temperature control operations according to the second temperature control parameter calculated by it. These execution units may include heaters, coolers, fans, etc.

[0023] It should be noted that the execution unit corresponds to several specific positions on the working terminal, and the temperature control operation performed by the execution unit at each specific position depends on the sub-control unit corresponding to the specific position, that is, the second temperature control parameter output by each sub-control unit drives the execution unit to correspond to a specific position on the working terminal to perform temperature control operations on the working terminal.

[0024] It can be understood that the system, by combining the PID algorithms of the main control unit and the sub-control unit, can adjust the temperature control parameters in real time according to the actual temperature characteristics and the working mode set by the user, to ensure that the temperature is stable within the preset range. The collaboration between the main control unit and the sub-control unit can effectively share the temperature control tasks, optimize the response speed and control accuracy, and improve the overall efficiency of the system.

[0025] The present invention provides a PID controller parameter automatic tuning method, which has the following beneficial effects: The present invention interacts with the user of the working terminal through an information inquiry port to determine the expected working mode of the working terminal to adjust the temperature control modes of the main control unit and the sub-control unit of the temperature control module. The main control unit analyzes the temperature characteristics based on a preset first PID temperature control algorithm, generates a first temperature control parameter, and transmits it to the sub-control unit. The sub-control unit analyzes the temperature characteristics and the first control parameter based on a second PID algorithm, adjusts and generates the second temperature control parameter, drives the execution unit of the temperature control module according to the second temperature control parameter, performs temperature control processing, realizes automatic adjustment of the PID controller parameters, improves the temperature control accuracy and response speed, dynamically adjusts the temperature control strategy according to different working modes, enhances the adaptability of the system, and the main control and sub-control units work together to ensure the high efficiency and stability of the temperature control process, thereby solving the problem of low working efficiency of the fixed temperature control mode in the prior art.

[0026] Preferably, the step of interacting with the user of the work terminal through the information inquiry port to obtain the work mode expected to be executed by the work terminal includes: S11: monitoring the awakening state of the work terminal, and when the monitoring result shows that the work terminal is in the awakening state, starting a preset information inquiry port to allow the user to interact with preset target information; S12: receiving an interactive operation instruction of the user through the information inquiry port, and performing target information conversion on the interactive operation instruction to obtain preset target information of the user for the work terminal; S13: Match the preset target information according to the preset interaction instruction library to obtain the working mode that the working terminal is expected to execute.

[0027] Specifically, the system first determines whether it is in the "wake-up" state by monitoring the status of the working terminal. The wake-up state means that the system recovers from the standby mode or the sleep mode to the operational mode, which usually means that the device is ready to receive input and perform operations. Through wake-up monitoring, the system can save energy when the working terminal is in the standby state, and start the operation only when needed, optimizing battery usage and device energy consumption. Once the working terminal enters the wake-up state, the system can respond in time and enter the mode ready to receive user input.

[0028] More specifically, when the work terminal is awakened and enters an interactive state, the system automatically starts a preset information inquiry port, which can be a graphical user interface (GUI) or a command line interface (CLI), which prompts the user to input or select target information. The information inquiry port contains multiple interactive elements, such as buttons, text boxes, drop-down menus, etc., which are used to collect user instructions for work modes, task requirements, temperature requirements, etc.

[0029] More specifically, the system receives interactive operation instructions input by the user through the information inquiry port. These instructions are usually selections or settings related to the working mode, such as temperature control targets, work tasks, etc. After receiving the instructions, the system needs to convert these operation instructions into specific target information. The target information includes numbers, set values ​​or configuration options, which usually involve specific working parameters, such as temperature settings, mode selections, etc.

[0030] More specifically, the system uses a preset interactive instruction library (or rule library) to match the target information input by the user. The library contains different working modes and their corresponding target information templates. Information matching refers to searching for the corresponding configuration in the preset library based on the target information input by the user (such as target temperature, operating mode) and confirming the execution requirements of the working mode.

[0031] More specifically, after information matching, the system finally determines the expected execution working mode of the working terminal, which includes temperature control settings, equipment operation mode, task priority, etc., depending on the user's input. After confirming the working mode, the system can automatically adjust the behavior of the working terminal, including starting the corresponding working mode, adjusting the temperature control strategy, etc.

[0032] Preferably, the steps of modulating the temperature control mode of the temperature control module pre-configured on the working terminal according to the working mode so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode include: S21: parsing the phase characteristics of the working mode to obtain temperature characteristics of the working terminal corresponding to several working stages of the working mode; wherein the temperature characteristics are used to describe the temperature change trend of the working terminal in the working stage; S22: analyzing theoretical temperature control nodes of the temperature control module according to the temperature characteristics of each working stage of the working mode corresponding to the working terminal, so as to obtain theoretical temperature control nodes of the temperature control module corresponding to each working stage; S23: Analyzing the node execution mode of the theoretical temperature control nodes corresponding to each working stage of the temperature control module according to the performance parameters of the main control unit and each sub-control unit in the temperature control module, so as to obtain the overall execution mode of the main control unit corresponding to each working stage and the specific execution mode of each sub-control unit corresponding to each working stage; S24: pre-inputting temperature control algorithm parameters to the main control unit according to the overall execution mode of the main control unit corresponding to each working stage, so that the first PID temperature control algorithm set in the main control unit matches the working mode, thereby switching the main control unit to the corresponding temperature control mode; S25: Pre-input the temperature control algorithm parameters to each sub-control unit according to the specific execution method of each sub-control unit for each working stage, so that the second PID temperature control algorithm set in each sub-control unit matches the working mode, thereby switching each sub-control unit to the corresponding temperature control mode.

[0033] Specifically, the system needs to parse the working mode, especially the temperature characteristics of each stage in the working mode. The working mode includes multiple stages, and each stage has different temperature requirements or temperature change trends. For example, in different stages such as heating, cooling or stabilization, the temperature change presents linear, exponential, or other forms of change. By analyzing each stage of the working mode, the temperature characteristics of each stage can be determined, that is, the temperature change trend of this stage over time. These characteristics will provide a basis for subsequent temperature control settings.

[0034] It can be understood that by analyzing the temperature characteristics of different working stages, the system can provide precise temperature control requirements for each stage and ensure the temperature control accuracy in different stages. The key to this step is to dynamically adjust the temperature control strategy according to different working stages, rather than applying a single temperature control strategy, to ensure the flexibility of the working terminal in complex tasks.

[0035] More specifically, based on the analyzed temperature characteristics of each working stage, the system will analyze the theoretical temperature control nodes. These nodes usually indicate information such as the target temperature that needs to be achieved in each stage and its rate of change, helping the system understand how to adjust the temperature in each stage. When analyzing the temperature control nodes, the system needs to consider the specific requirements of temperature changes, such as whether rapid heating, smooth control or gradual adjustment is required.

[0036] It can be understood that through the analysis of temperature control nodes, the system can provide a scientific theoretical basis for the temperature control module and guide how to set and adjust the temperature. Theoretical analysis can ensure that the temperature control module can be accurately adjusted according to needs in different stages to avoid excessive temperature fluctuations or insufficient control.

[0037] More specifically, the system analyzes the temperature control nodes at each stage based on the performance parameters of the main control unit and the sub-control unit in the temperature control module (such as response time, power output range, etc.). These performance parameters will affect the response speed, adjustment accuracy and overall control capability of the temperature control module. Through this analysis, the system can determine how the theoretical temperature control nodes at each stage should be allocated between the main control unit and the sub-control unit to ensure that each unit can perform at its best and complete the target temperature control task.

[0038] It is understandable that by optimizing the control strategy based on the performance parameters of the units, the load and responsibility of the main control unit and the sub-control unit are reasonably divided, thereby improving the overall efficiency and accuracy of the system. By analyzing the characteristics of different units, the main control unit and the sub-control unit can work together to achieve precise temperature control without being overloaded or slow in response.

[0039] More specifically, after analyzing the execution methods of the main control unit and the sub-control unit, the system needs to pre-input the temperature control algorithm parameters to the main control unit. Common temperature control algorithms include the PID (proportional-integral-differential) algorithm, which is used to adjust the heating or cooling output according to the temperature deviation. The main control unit needs to preset the temperature control algorithm parameters according to the needs of different working stages, such as the proportional coefficient, integral coefficient, differential coefficient, etc. in PID control, to ensure that the temperature control algorithm matches the working mode.

[0040] It can be understood that by pre-inputting appropriate PID algorithm parameters, the main control unit can achieve precise temperature control at each stage, thereby ensuring the stability and response speed of the temperature control system. Adapting the algorithm parameters to different working stages enables the main control unit to intelligently adjust the temperature and ensure that the system can cope with complex temperature control requirements.

[0041] More specifically, similar to the operation of the main control unit, the sub-control unit also needs to input corresponding temperature control algorithm parameters according to the requirements of each working stage. The sub-control unit needs to adjust the control strategy according to different working stages, such as making corresponding adjustments according to changes in load. The PID control algorithm parameter pre-input of each sub-control unit can be completed by system analysis of the execution method of each stage, ensuring that the sub-control unit accurately performs specific temperature control tasks in each stage.

[0042] It can be understood that by adjusting the temperature control algorithm parameters of the sub-control unit, it can be ensured that the sub-control unit accurately controls specific temperature control tasks during the specific execution process, improve the overall accuracy of the system, and through reasonable parameter input, the main control unit and the sub-control unit can divide the work and cooperate to jointly complete multi-stage, multi-parameter temperature control tasks.

[0043] More specifically, after all temperature control algorithm parameters and control strategies are preset, the main control unit and each sub-control unit will switch to the corresponding temperature control mode according to the set temperature control mode. The switching process includes adjusting the working status of the heating and cooling equipment, and making corresponding adjustments based on the deviation between the actual temperature and the target temperature to ensure that each stage works according to the predetermined temperature characteristics.

[0044] Preferably, according to the performance parameters of the main control unit and each sub-control unit in the temperature control module, the node execution mode of the theoretical temperature control nodes of the temperature control module corresponding to each working stage is analyzed to obtain the overall execution mode of the main control unit corresponding to each working stage and the specific execution mode of each sub-control unit corresponding to each working stage, including: S231: Acquire performance parameters of the main control unit and each of the sub-control units; wherein the performance parameters are used to describe the correlation between the temperature control execution form of the main control unit and each of the sub-control units controlling the execution unit to perform temperature control work based on the temperature characteristics, and the working effect of the execution unit receiving the drive of the temperature control execution form to perform temperature control work; S232: Analyzing the driving form required to achieve the temperature control effect for the theoretical temperature control nodes of the temperature control module corresponding to each working stage, so as to obtain the driving requirement characteristics of the execution unit to achieve the theoretical temperature control nodes of each working stage; S233: analyzing the correlation of the driving demand characteristics of each working phase according to the performance parameters of the main control unit and each of the sub-control units to obtain the overall execution mode of the main control unit and the specific execution mode of each of the sub-control units.

[0045] Specifically, the performance parameters of the main control unit and the sub-control unit are obtained. These parameters describe their control execution forms in the temperature control process, as well as the relationship between the temperature control effect after the temperature control execution unit receives the drive of the control execution form. That is, the main control unit and the sub-control unit have multiple control execution forms. Different control execution forms will give different driving instructions to the execution unit when facing the same temperature characteristics, so that the execution unit can perform different temperature control operations to control the temperature of the working terminal. It is not difficult to see that the temperature control effects, resource scheduling rates, etc. brought about by different control execution forms are different.

[0046] More specifically, the driving form of the theoretical temperature control nodes required by the temperature control module in different working stages is analyzed to clarify how to achieve the temperature control effects in these stages through driving methods, that is, the theoretical temperature control nodes are substituted into the temperature control effects, and then the driving form that the execution unit needs to receive to achieve this temperature control effect is derived. The temperature control effect after the execution unit receives the driving is correlated with the driving form received by the execution unit, and the control execution form of the control unit is correlated with the driving form received by the execution unit. The driving requirement characteristics represent the driving form that the execution unit needs to receive in order to achieve the temperature control effect. Based on this driving form, the control execution form of the execution unit corresponding to the main control unit and the sub-control unit can be derived.

[0047] More specifically, based on the performance parameters of the main control unit and the sub-control unit, the driving demand characteristics of each working stage are analyzed and matched with the actual execution method of each unit, thereby obtaining the overall execution method of the main control unit in each working stage and the specific execution method of the sub-control unit in each working stage. In this analysis stage, by combining the performance parameters with the driving demand characteristics, it is possible to determine how the main control unit and the sub-control unit coordinate their work to form an optimal execution plan. The main control unit is responsible for overall regulation, while the sub-control unit is responsible for more detailed adjustments.

[0048] Preferably, the step of analyzing the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmitting the first temperature control parameter to each of the sub-control units in signal communication with the main control unit includes: S41: parsing the timing characteristics of the temperature feature to obtain the temperature timing characteristics of the temperature feature, and matching and identifying the temperature timing characteristics according to the data portion corresponding to the current temperature control mode in the database to obtain the working stage fed back by the temperature feature, marking it as the current working stage; S42: Determine the stage target of the current working stage according to the first PID temperature control algorithm to obtain the preset temperature control characteristics corresponding to the current working stage, and perform multiple parallel analyses of real-time error, historical accumulated error and error change rate on the preset temperature control characteristics and the temperature characteristics according to the first PID temperature control algorithm, and optimize the control parameters of the preset temperature control characteristics according to the results of the multiple parallel analyses to obtain the first temperature control parameters.

[0049] Specifically, the system needs to analyze the timing characteristics of the temperature characteristic data, which mainly includes: Temperature timing characteristics: By analyzing the time series of temperature changes, the system can identify the laws of temperature changes, such as the temperature rise / fall rate, fluctuation cycle, oscillation mode, etc. Time axis analysis: Identify the changes in temperature characteristics at different time points, and determine whether there are periodic or trend changes, so as to facilitate subsequent matching with the target temperature control characteristics.

[0050] It can be understood that by analyzing the temperature characteristic time series, the system can clearly understand the pattern of temperature change and provide basic data support for subsequent temperature control tasks. The system can adjust the temperature control strategy in time according to the timing characteristics of temperature changes to ensure that the temperature control system can respond to the trend of temperature fluctuations.

[0051] More specifically, the system accesses the data in the database and matches and identifies the temperature characteristics corresponding to the current temperature control mode. The database stores data such as temperature change patterns, error characteristics, and system response characteristics under different temperature control modes. According to the current temperature timing characteristics, the system matches these characteristics with the preset temperature control mode in the database to confirm the current temperature control stage. This stage is marked as the "current working stage". By matching the temperature timing characteristics with the preset temperature control mode, the system can identify and determine which working stage the temperature control system is currently in, such as heating up, cooling down, maintaining stability, etc.

[0052] It can be understood that through timing characteristic matching, the system can accurately identify the current temperature control stage, avoid manual intervention or misjudgment, and improve the level of automation and intelligence. The temperature control system can adaptively adjust to the correct temperature control mode according to the real-time temperature timing characteristics to improve the temperature control accuracy.

[0053] More specifically, after determining the "current working stage", the system uses the first PID temperature control algorithm to set the target temperature for this stage. According to the principle of the PID algorithm, the system sets a "target temperature" or "target temperature control characteristic". This target temperature is usually a temperature value that matches the ideal temperature control target of the current working stage. This step ensures that the target temperature of the current working stage meets the task requirements of the temperature control module, such as the target temperature for heating, the set temperature for stable maintenance, etc.

[0054] It can be understood that by setting clear targets for each working stage through the PID algorithm, it can be ensured that the temperature control process can be adjusted in real time according to the target temperature to avoid excessive deviations in the temperature control process. Accurate target setting can reduce system errors and fluctuations and ensure efficient and accurate operation of the temperature control system.

[0055] More specifically, multiple parallel analyses are real-time processing methods for temperature errors based on the PID control algorithm, mainly including: Real-time error analysis: the difference between the current temperature and the target temperature. This error represents the immediate deviation of the temperature control system, and the PID algorithm will make adjustments based on this error; Historical accumulated error analysis: The accumulated error can reflect whether there is a continuous deviation in the temperature control process, and can affect the adjustment of the integral (I) part to ensure that the system gradually corrects the deviation; Error change rate analysis: The speed of temperature change reflects the dynamic changes of the system. If the temperature changes too fast or too slow, the PID algorithm will adjust the speed of the system output to avoid oscillation or lag. Through multiple parallel analyses of these three errors, the PID controller will continuously optimize the control strategy and adjust various parameters (such as proportional Kp, integral Ki, differential Kd) so that the system can track the target temperature more efficiently and control stably.

[0056] It can be understood that by analyzing the real-time error, historical error and change rate in parallel, the PID algorithm can adjust the control parameters more accurately and reduce temperature deviation and fluctuation. The analysis of historical error and error change rate can effectively eliminate overshoot, oscillation and other phenomena in the temperature control process and ensure the stability of the system. The PID controller can automatically adjust the proportional, integral and differential coefficients to cope with the characteristics of temperature changes in different working stages and improve the adaptability of the system.

[0057] More specifically, after the error analysis, the system optimizes and adjusts the temperature control parameters according to the PID algorithm. By analyzing the error results in parallel, the system optimizes the control parameters of the preset temperature control characteristics, mainly including: optimizing the proportion (Kp): adjusting the proportional term according to the amplitude of the real-time error to ensure that the system has a moderate response speed, optimizing the integral (Ki): adjusting the integral term according to the accumulation of historical errors to ensure that the system can effectively eliminate long-term deviations, optimizing the differential (Kd): adjusting the differential term according to the error change rate to slow down the system's overreaction and reduce oscillation. The system continuously optimizes these control parameters and finally obtains the first temperature control parameters suitable for the current working stage, and transmits these optimized control parameters to each sub-control unit to achieve more accurate temperature control.

[0058] It can be understood that through the optimization of PID parameters, the system can maintain temperature stability in a dynamic environment, respond more quickly and with higher accuracy. After the optimized control parameters are transmitted to each sub-control unit, it can ensure the coordinated work between multiple temperature control units and avoid local control errors affecting the overall temperature control effect. The optimization process is not limited to a single temperature control stage, but is based on a comprehensive analysis of real-time errors and historical data, and can cope with various complex temperature change patterns.

[0059] Preferably, multiple parallel analyses of real-time error, historical accumulated error and error change rate are performed on the preset temperature control feature and the temperature feature, and the step of optimizing the control parameters of the preset temperature control feature according to the results of the multiple parallel analyses is implemented by a PID control model, and the PID control model is a pre-trained machine learning model, and the training steps of the PID control model include: S401: Construct a machine learning model; wherein the machine learning model is a convolutional neural network model or a long short-term memory model; S402: Collect training data; wherein the training data includes several groups of temperature control experiment records collected by tests, and the temperature control experiment records include temperature characteristics of a working terminal in a specified working stage, temperature control characteristics of a temperature control module that performs temperature control processing on the working terminal, and temperature control effects after temperature control processing by the temperature control module; S403: Divide the training data into sets to obtain a training set, a validation set, and a test set; S404: Performing model training on the machine learning model according to the training set to update model parameters of the machine learning model, so that the updated model parameters can feedback the mapping relationship between the temperature characteristics, the temperature control characteristics, and the temperature control effect in the temperature control experiment record; S405: Evaluate the intermediate performance of the machine learning model after one round of training with the training set according to the validation set to supervise the training effect of the training set, and optimize the hyperparameters of the machine learning model; S406: Evaluate the model performance of the machine learning model after training with the training set according to the test set, and determine whether the machine learning model needs to be retrained according to the evaluation result, until the training of the machine learning model is completed.

[0060] Specifically, a machine learning model for PID control optimization is constructed. There are two main candidate architectures for this model: Convolutional Neural Network (CNN): Suitable for processing data with spatial locality, such as certain local change characteristics in time series data, Long Short-Term Memory Network (LSTM): Suitable for processing sequence data with time dependence, capable of capturing long-term dependencies of data, which is particularly important in temperature control data. One of the network models (CNN or LSTM) is selected, and the corresponding network architecture is constructed according to its applicability. This model will be used to learn the mapping relationship between temperature characteristics, temperature control characteristics, and temperature control effects to optimize the parameters of the PID controller. By selecting an appropriate neural network architecture (CNN or LSTM), the advantages of the machine learning model can be maximized to adapt to the characteristics of different temperature control data. The machine learning model can automatically adjust to capture the complex laws and dynamic changes in the temperature control system to ensure the accuracy of the optimization effect.

[0061] More specifically, a large number of temperature control experiment records are collected. These records include: temperature characteristics: real-time temperature data of the working end of the temperature control system in a specified working stage, temperature control characteristics: target temperature set by the temperature control module, temperature control strategy and its parameters, temperature control effect: temperature response result after adjustment by the temperature control module, reflecting the actual effect of the temperature control system. Through experiments or simulations, as much temperature control data as possible in different working stages is collected to ensure the generalization ability of the model and the comprehensiveness of the training data. Comprehensive collection of temperature control experiment records provides sufficient training data for the machine learning model, which can help the model capture the correlation between various features in the temperature control system. By including a variety of data in different working stages, the generalization ability of the model can be improved to make it suitable for different working environments.

[0062] More specifically, the collected training data is divided into three sets in proportion: training set: used to train the machine learning model to help the model learn the relationship between temperature control features and effects, validation set: used to evaluate the model during the training process to adjust hyperparameters and avoid overfitting, test set: used to evaluate the performance of the final model to ensure that the model can produce reasonable predictions on unseen data. By reasonably dividing the data set, the training process of the machine learning model can be ensured not to be affected by data leakage. At the same time, the model hyperparameters are adjusted through the validation set to ensure the robustness of the model. By using the validation set to monitor the training process, the model overfitting can be effectively avoided and the generalization ability of the model can be improved.

[0063] More specifically, the machine learning model is trained using a training set. The model will adjust its parameters based on the mapping relationship between temperature features, temperature control features, and temperature control effects in the training data. During the training process, the model will continuously update parameters through a back-propagation algorithm (such as gradient descent) to make the prediction results more accurate. The goal of the training is to enable the model to identify and optimize the control parameters of the PID controller so that the model can learn the best control strategy from the temperature control features and effects.

[0064] It is understandable that through machine learning, the model can automatically learn the complex relationship between temperature control characteristics and effects, reducing the burden of manual adjustment of PID parameters. The optimized model can improve the temperature control effect of the PID controller by adjusting the control parameters, thereby improving the stability and response accuracy of the system.

[0065] More specifically, after each round of training, the validation set is used to evaluate the performance of the machine learning model. The validation set is used to: monitor the training effect: evaluate whether the model effect on the training set is overfitting, check the performance of the model on unknown data, and optimize hyperparameters: tune the model's hyperparameters (such as learning rate, number of layers, etc.) according to the results of the validation set to ensure the performance of the model in a real environment. Common hyperparameter optimization techniques include Grid Search or Bayesian Optimization, which can help find the optimal training configuration.

[0066] It is understandable that through the evaluation of the validation set, problems in the model training process can be discovered and corrected in a timely manner, and the generalization ability of the model can be improved. Hyperparameter optimization enables the machine learning model to achieve optimal performance in different environments and enhance the accuracy and stability of the temperature control system.

[0067] More specifically, the test set is used to perform a final performance evaluation on the machine learning model that has been tuned with the validation set. The test set is data that the model has never seen during the training process. Therefore, it can truly reflect the actual performance of the model. It mainly evaluates the accuracy and robustness of the model in predicting PID control parameter optimization, such as the accuracy of predicting control parameters, the stability of temperature control effects, etc. If the test results do not meet expectations, the model needs to be retrained or the data set needs to be adjusted.

[0068] It is understandable that the evaluation of the test set helps verify the effectiveness of the model in actual temperature control tasks and ensures that the model can generate reasonable control strategies in real scenarios. The final verification of the test set can ensure that the machine learning model has strong adaptability and stability and is not easily affected by noise or unknown data.

[0069] More specifically, after evaluation through the test set, if the performance of the model reaches the expected goal, the model training is completed. Ultimately, the trained machine learning model will be used to optimize the parameters of the PID controller by continuously performing parallel analysis of real-time errors, historical accumulated errors, and error change rates, thereby improving the accuracy and response speed of the temperature control system. The trained machine learning model can intelligently adjust the PID control parameters, improve the adaptability and temperature control accuracy of the temperature control system in different working stages, and automatically optimize the control parameters through machine learning, thereby reducing manual intervention and improving the automation and reliability of the system.

[0070] Preferably, the step of performing real-time analysis on the first temperature control parameter and the temperature characteristic according to the second PID temperature control algorithm preset in each of the sub-control units to obtain the second temperature control parameter includes: S51: the sub-control unit performs algorithm parameter configuration on the received first temperature control parameter to determine that the working terminal is in a current working stage, and marks it as the current working stage; S52: according to the specific position of the working terminal corresponding to the auxiliary control unit, the temperature feature is analyzed in the corresponding area to obtain the temperature feature to be controlled of the auxiliary control unit; S53: Delimiting the range of the stage target of the current working stage according to the second PID temperature control algorithm to obtain a reference temperature control target, and performing multiple parallel analyses of real-time error, historical accumulated error, and error change rate on the reference temperature control target and the temperature characteristic to be controlled according to the second PID temperature control algorithm, and adjusting the control target of the reference temperature control target according to the results of the multiple parallel analyses to obtain a real-time temperature control target corresponding to the temperature characteristic to be controlled; S54: According to the temperature control execution process relationship of the specific position of the working terminal corresponding to each of the sub-control units, the real-time temperature control target of each of the sub-control units is corrected for correlation to obtain the second temperature control parameter of each of the sub-control units.

[0071] Specifically, each sub-control unit receives the first temperature control parameters from the main control system. These parameters are usually based on the system's preliminary settings or historical data, such as the target temperature range, control mode, etc. The sub-control unit configures its own PID control algorithm according to the received control parameters, which includes setting the initial parameters of the PID controller according to information such as the current working stage, temperature control requirements, and system status. By analyzing the current working environment and system status, the sub-control unit determines the working stage of the working terminal and marks it as "current working stage" to set the basis for subsequent control logic. By correctly identifying the current working stage, the sub-control unit can provide precise working conditions for subsequent temperature control strategies. According to different working states or environments, the sub-control unit can flexibly adjust the PID algorithm configuration to make the temperature control more in line with actual needs.

[0072] More collectively, the sub-control unit determines the specific area involved in the location according to the location of the corresponding work terminal, which is usually based on the geographical or functional distribution of multiple sub-control units in the system. The sub-control unit analyzes the temperature characteristic data in the area, which usually involves data collected by temperature sensors. These data will reflect the temperature distribution and changing trends in different areas. Based on the location and temperature characteristic data, the sub-control unit obtains the "temperature characteristic to be controlled", which represents the temperature parameter or regional temperature that currently needs to be regulated.

[0073] More specifically, the sub-control unit analyzes the temperature characteristics according to the specific location, can effectively reflect and adjust the temperature changes in the local area, and improve the local temperature control accuracy of the system. The system can capture the temperature changes in different areas in real time and make corresponding adjustments.

[0074] More specifically, the sub-control unit sets a phased target range through the second PID temperature control algorithm according to the current working stage. This range defines the acceptable interval of temperature fluctuations in the current stage and determines the baseline temperature control target of the stage. The sub-control unit performs real-time error calculation on the difference between the baseline temperature control target and the temperature characteristic to be controlled. The errors usually include: real-time error: the difference between the current temperature and the target temperature, historical accumulated error: the long-term deviation between the temperature and the target, error change rate: the difference between the rate of temperature change and the rate of change of the target. Through these error indicators, the sub-control unit analyzes the error trend in the temperature control process and adjusts the baseline temperature control target.

[0075] More specifically, based on the results of the parallel analysis, the sub-control unit adjusts the control target of the baseline temperature control target to optimize the real-time temperature control target of the temperature characteristic to be controlled. This adjustment process involves the adjustment of PID control parameters (such as proportional, integral and differential coefficients).

[0076] It can be understood that through multiple parallel analyses of real-time errors, historical errors and error change rates, the sub-control unit can efficiently identify and adjust the temperature control target to ensure system stability and minimize deviations. Real-time adjustment of the temperature control target enables the sub-control unit to effectively respond to temperature changes in a dynamic environment, ensuring the accuracy and stability of the temperature control target at different stages.

[0077] More specifically, based on the specific locations of the working terminals corresponding to the sub-control units, the system will consider the temperature control execution process relationships between the sub-control units. This means that in a temperature control system, different sub-control units have mutual influence and dependence on temperature control targets. For example, temperature control changes in one area will affect the temperatures in other areas. Especially in large systems, the synergy of temperature control is particularly critical. The sub-control units make correlation corrections to the real-time temperature control targets of each sub-control unit based on these execution process relationships to ensure that the temperature control targets of all sub-control units can be coordinated and consistent, avoiding problems such as local control conflicts or over-compensation.

[0078] It can be understood that by correcting the correlation of each sub-control unit, the system can avoid local control conflicts, achieve temperature control coordination of the entire system, and optimize the overall temperature control effect. The correlation correction can enable each sub-control unit to adjust according to global needs and improve the stability and robustness of the temperature control system.

[0079] More specifically, after completing the above-mentioned adjustments and correlation corrections, the sub-control unit finally calculates the second temperature control parameter, which is generated based on the control target and real-time adjustment result of the second PID temperature control algorithm. This parameter will be used in the subsequent temperature control execution stage to accurately control the temperature to operate stably within the preset range. By calculating and generating the second temperature control parameter in real time, the sub-control unit can achieve refined and personalized control in the temperature control system, so that the temperature control target of each sub-control unit is more in line with actual needs. Through the optimization of global and local temperature control targets, the system can respond more accurately to temperature changes in different areas and stages, thereby improving the operating efficiency and effectiveness of the entire temperature control system.

[0080] Preferably, according to the temperature control execution process relationship of the specific position of the working terminal corresponding to each of the sub-control units, the step of performing correlation correction on the real-time temperature control target of each of the sub-control units to obtain the second temperature control parameter of each of the sub-control units includes: S541: Acquire the specific position of the working terminal corresponding to each of the sub-control units, and analyze the process sequence of the specific position of the working terminal corresponding to each of the sub-control units according to the cooling execution process of each specific position of the working terminal corresponding to the execution unit, and assign a process tag to each of the sub-control units according to the analysis result of the process sequence; S542: Simulate the influence of temperature control effects on the real-time temperature control targets of each sub-control unit according to the process mark of each sub-control unit, and correct the real-time temperature control target of each sub-control unit according to the simulation result to obtain the second temperature control parameter of each sub-control unit.

[0081] Specifically, each sub-control unit corresponds to a specific location on the work terminal. The sub-control unit first obtains the specific location of the work terminal where it is located, which is usually the identification of the physical location or functional location (such as a specific section of the temperature control equipment). These locations are the basis for temperature control task allocation, status monitoring and execution control. Accurately obtaining the location of the work terminal where the sub-control unit is located ensures the targeted and efficient subsequent temperature control analysis, and can collect corresponding temperature data according to the specific needs of each location to ensure that the temperature control needs of the entire system at different locations are accurately identified.

[0082] More specifically, the sub-control unit analyzes the cooling execution process at the corresponding work terminal according to the specific position of the work terminal. The cooling execution process refers to the specific steps and sequence of temperature adjustment between each sub-control unit and the work terminal during the temperature control process. The sub-control unit analyzes the causal relationship of each process according to the sequence of the cooling process (for example, cooling down first, maintaining, then heating, etc.), and determines whether the temperature control tasks at different positions have temporal connection or interaction. Based on these relationships, the sub-control unit assigns a process tag to each work terminal to identify the cooling steps at different positions and their positions and functions in the entire system. By analyzing the cooling execution process sequence, the sub-control unit can clearly understand the sequence and mutual relationship of each control step, providing a basis for subsequent temperature control adjustment and optimization. The process sequence analysis ensures the orderly execution of temperature control tasks, avoids control conflicts or errors, and improves the system's operating efficiency.

[0083] More specifically, after assigning a process tag to each sub-control unit, the sub-control unit begins to simulate the mutual influence between its temperature control targets. This simulation process is based on the process sequence and temperature control targets of different sub-control units. The sub-control unit will affect the temperature control targets of other sub-control units according to the temperature control tasks of its working terminal (for example, the increase in temperature in a certain area may affect the temperature control strategy of the adjacent area). This mutual influence may be reflected in temperature changes, error transmission, feedback mechanisms, etc. The purpose of the simulation is to evaluate whether there are conflicts, lags or feedback problems in the temperature control effect of the entire system by considering the temperature control influence relationship between each sub-control unit.

[0084] It can be understood that simulation can fully consider the interaction between each sub-control unit, thereby optimizing the temperature control effect on a global scale and avoiding local temperature control conflicts or excessive adjustments. Through simulation, the system can quickly respond to the temperature control influence relationship between each sub-control unit and predict possible temperature fluctuations or errors in advance, so as to take preventive measures.

[0085] More specifically, after completing the simulation analysis, the sub-control unit adjusts its real-time temperature control target according to the simulation results. This correction process is based on the mutual influence between the sub-control units and the overall coordination of the temperature control target. The correction process usually includes: Reducing conflicts: If the temperature control targets of some sub-control units conflict with other sub-control units, adjust the temperature control targets of these sub-control units to reduce interference, balancing errors: If the simulation results show that the temperature errors in certain areas are large, the sub-control unit can adjust the temperature control targets according to the error analysis results to ensure that the temperature is within the predetermined range, and optimizing coordination: According to the coordinated relationship between the sub-control units, adjust the temperature control target of each sub-control unit to make the temperature control of the entire system more coordinated and consistent.

[0086] It can be understood that by correcting the temperature control target, the sub-control unit can track and adjust the temperature more accurately, thereby improving the accuracy and stability of the overall temperature control. By correcting the temperature control targets between the sub-control units, conflicts in temperature control targets within the system are avoided, and unnecessary error accumulation and temperature fluctuations are reduced. Correcting and coordinating temperature control targets between multiple sub-control units can ensure that the temperature control tasks of the system at different locations and stages are coordinated and consistent, thereby improving the efficiency and stability of the entire system.

[0087] More specifically, after simulation analysis and correction of the real-time temperature control target, the sub-control unit will calculate new control parameters based on the corrected temperature control target, namely the second temperature control parameters. These second temperature control parameters include corrected PID control parameters, error feedback coefficient, temperature fluctuation tolerance range, etc., which can achieve more refined and accurate temperature control in the actual control process.

[0088] It can be understood that through the optimized second temperature control parameters, the sub-control unit can control the temperature more accurately so that it fluctuates within the target range. The second temperature control parameters can be adjusted in real time according to the specific needs of the system and changes in the working environment, thereby enhancing the system's ability to adapt to different working stages and external disturbances.

[0089] Reference Figure 2 As shown, in a second aspect, the present invention provides a PID controller parameter automatic tuning device, which is used to implement a PID controller parameter automatic tuning method described in any one of the first aspects, comprising: A mode analysis module, used to interact with a user of a work terminal through an information inquiry port to obtain a work mode that the work terminal is expected to execute; A mode switching module, used to modulate the temperature control mode of the temperature control module pre-configured on the working terminal according to the working mode, so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode; A temperature acquisition module, used to collect temperature data of the working terminal to obtain temperature characteristics of the working terminal; a first temperature control module, configured to analyze the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmit the first temperature control parameter to each of the sub-control units in signal communication with the main control unit; a second temperature control module, configured to perform a real-time analysis of the first temperature control parameter and the temperature characteristic according to a second PID temperature control algorithm preset in each of the sub-control units, so as to obtain a second temperature control parameter; The temperature control execution module is used to drive the execution unit in the temperature control module to perform temperature control according to each of the second temperature control parameters, so that the execution unit performs temperature control processing on the working terminal.

[0090] In this embodiment, for the specific implementation of each module in the above-mentioned device embodiment, please refer to the above-mentioned method embodiment, which will not be described in detail here.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for automatic tuning of PID controller parameters, characterized in that: include: Interacting with a user of a work terminal through an information inquiry port to obtain a work mode that the work terminal is expected to perform; According to the working mode, the temperature control mode of the temperature control module pre-configured on the working terminal is modulated, so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode; Collecting temperature data of the working terminal to obtain temperature characteristics of the working terminal; Analyzing the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmitting the first temperature control parameter to each of the sub-control units in signal communication with the main control unit; Performing a real-time analysis on the first temperature control parameter and the temperature characteristic according to a second PID temperature control algorithm preset in each of the sub-control units to obtain a second temperature control parameter; The execution unit in the temperature control module is driven to perform temperature control according to each of the second temperature control parameters, so that the execution unit performs temperature control processing on the working terminal.

2. The PID controller parameter automatic tuning method according to claim 1, characterized in that: The step of interacting with a user of a work terminal through an information inquiry port to obtain a work mode that is expected to be executed by the work terminal includes: Monitoring the awakening state of the work terminal, and when the monitoring result shows that the work terminal is in the awakening state, starting a preset information inquiry port for the user to interact with preset target information; Receiving the user's interactive operation instruction through the information inquiry port, and converting the interactive operation instruction into target information to obtain the user's preset target information for the work terminal; The preset target information is matched according to the preset interaction instruction library to obtain the working mode that the working terminal is expected to execute.

3. The PID controller parameter automatic tuning method according to claim 1, characterized in that: The steps of modulating the temperature control mode of the temperature control module pre-configured on the working terminal according to the working mode so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode include: Analyzing the phase characteristics of the working mode to obtain temperature characteristics of the working terminal corresponding to several working phases of the working mode; wherein the temperature characteristics are used to describe the temperature change trend of the working terminal in the working phase; According to the temperature characteristics of each working stage of the working mode corresponding to the working terminal, the theoretical temperature control nodes of the temperature control module are analyzed to obtain the theoretical temperature control nodes of the temperature control module corresponding to each working stage; According to the performance parameters of the main control unit and each sub-control unit in the temperature control module, the node execution mode of the theoretical temperature control nodes of the temperature control module corresponding to each working stage is analyzed to obtain the overall execution mode of the main control unit corresponding to each working stage and the specific execution mode of each sub-control unit corresponding to each working stage; Pre-inputting temperature control algorithm parameters to the main control unit according to the overall execution mode of the main control unit corresponding to each working stage, so that the first PID temperature control algorithm set in the main control unit matches the working mode, thereby switching the main control unit to the corresponding temperature control mode; The temperature control algorithm parameters are pre-input into each sub-control unit according to the specific execution mode of each working stage of each sub-control unit, so that the second PID temperature control algorithm set in each sub-control unit matches the working mode, thereby making each sub-control unit switch to the corresponding temperature control mode.

4. The PID controller parameter automatic tuning method according to claim 3, characterized in that: According to the performance parameters of the main control unit and each sub-control unit in the temperature control module, the node execution mode of the theoretical temperature control nodes of the temperature control module corresponding to each working stage is analyzed to obtain the overall execution mode of the main control unit corresponding to each working stage and the specific execution mode of each sub-control unit corresponding to each working stage, including: Acquire performance parameters of the main control unit and each of the sub-control units; wherein the performance parameters are used to describe the correlation between the temperature control execution form of the main control unit and each of the sub-control units controlling the execution unit to perform temperature control work based on the temperature characteristics, and the working effect of the execution unit receiving the drive of the temperature control execution form to perform temperature control work; Analyzing the driving form required to achieve the temperature control effect for the theoretical temperature control nodes of the temperature control module corresponding to each working stage, so as to obtain the driving requirement characteristics of the theoretical temperature control nodes of each working stage implemented by the execution unit; According to the performance parameters of the main control unit and each of the sub-control units, the driving demand characteristics of each working stage are analyzed for correlation, so as to obtain the overall execution mode of the main control unit and the specific execution mode of each of the sub-control units.

5. The PID controller parameter automatic tuning method according to claim 1, characterized in that: The steps of analyzing the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmitting the first temperature control parameter to each of the sub-control units in signal communication with the main control unit include: Analyze the timing characteristics of the temperature characteristics to obtain the temperature timing characteristics of the temperature characteristics, and match and identify the temperature timing characteristics according to the data portion corresponding to the current temperature control mode in the database to obtain the working stage fed back by the temperature characteristics, which is marked as the current working stage; According to the first PID temperature control algorithm, a stage target is determined for the current working stage to obtain a preset temperature control feature corresponding to the current working stage, and according to the first PID temperature control algorithm, multiple parallel analyses of real-time error, historical accumulated error and error change rate are performed on the preset temperature control feature and the temperature feature, and control parameters of the preset temperature control feature are optimized based on the results of the multiple parallel analyses to obtain a first temperature control parameter.

6. The method for automatic tuning of PID controller parameters as claimed in claim 5, characterized in that: The preset temperature control feature and the temperature feature are subjected to multiple parallel analyses of real-time error, historical accumulated error, and error change rate, and the step of optimizing the control parameters of the preset temperature control feature according to the results of the multiple parallel analyses is implemented by a PID control model, wherein the PID control model is a pre-trained machine learning model, and the training steps of the PID control model include: Constructing a machine learning model; wherein the machine learning model is a convolutional neural network model or a long short-term memory model; Collecting training data; wherein the training data includes several groups of temperature control experiment records collected by tests, the temperature control experiment records including temperature characteristics of a working terminal in a specified working stage, temperature control characteristics of a temperature control module that performs temperature control processing on the working terminal, and temperature control effects after temperature control processing by the temperature control module; Dividing the training data into sets to obtain a training set, a validation set, and a test set; Performing model training on the machine learning model according to the training set to update model parameters of the machine learning model so that the updated model parameters can feedback the mapping relationship between the temperature characteristics, the temperature control characteristics, and the temperature control effect in the temperature control experiment record; Evaluate the intermediate performance of the machine learning model after one round of training with the training set according to the validation set to supervise the training effect of the training set, and optimize the hyperparameters of the machine learning model; The model performance of the machine learning model after training with the training set is evaluated according to the test set, and whether the machine learning model needs to be retrained is determined according to the evaluation result until the training of the machine learning model is completed.

7. The PID controller parameter automatic tuning method according to claim 1, characterized in that: The step of performing real-time analysis on the first temperature control parameter and the temperature characteristic according to the second PID temperature control algorithm preset in each of the sub-control units to obtain the second temperature control parameter comprises: The sub-control unit performs algorithm parameter configuration on the received first temperature control parameter to determine that the working terminal is in a current working stage, and marks it as the current working stage; According to the specific position of the working terminal corresponding to the auxiliary control unit, the temperature feature is analyzed in the corresponding area to obtain the temperature feature to be controlled of the auxiliary control unit; Delimiting the range of the stage target of the current working stage according to the second PID temperature control algorithm to obtain a reference temperature control target, and performing multiple parallel analyses of real-time error, historical accumulated error, and error change rate on the reference temperature control target and the temperature characteristic to be controlled according to the second PID temperature control algorithm, and adjusting the control target of the reference temperature control target according to the results of the multiple parallel analyses to obtain a real-time temperature control target corresponding to the temperature characteristic to be controlled; According to the temperature control execution process relationship of the specific position of the working terminal corresponding to each of the sub-control units, the real-time temperature control target of each of the sub-control units is corrected for correlation to obtain the second temperature control parameter of each of the sub-control units.

8. The method for automatic tuning of PID controller parameters as claimed in claim 7, characterized in that: According to the temperature control execution process relationship of the specific position of the working terminal corresponding to each of the sub-control units, the real-time temperature control target of each of the sub-control units is corrected for correlation to obtain the second temperature control parameter of each of the sub-control units, including: Obtaining the specific position of the working terminal corresponding to each of the sub-control units, and analyzing the process sequence of the specific position of the working terminal corresponding to each of the sub-control units according to the cooling execution process of each specific position of the working terminal corresponding to the execution unit, and assigning a process tag to each of the sub-control units according to the analysis result of the process sequence; According to the process mark of each sub-control unit, the influence of the temperature control effect on the real-time temperature control target of each sub-control unit is simulated, and the real-time temperature control target of each sub-control unit is corrected according to the simulation result to obtain the second temperature control parameter of each sub-control unit.

9. A PID controller parameter automatic tuning device, characterized in that: A method for automatically tuning parameters of a PID controller according to any one of claims 1 to 8, comprising: A mode analysis module, used to interact with a user of a work terminal through an information inquiry port to obtain a work mode that the work terminal is expected to execute; A mode switching module, used to modulate the temperature control mode of the temperature control module pre-configured on the working terminal according to the working mode, so that the main control unit and each sub-control unit in the temperature control module are respectively switched to the corresponding temperature control mode; A temperature acquisition module, used to collect temperature data of the working terminal to obtain the temperature characteristics of the working terminal; a first temperature control module, configured to analyze the temperature characteristics according to a first PID temperature control algorithm preset in the main control unit to obtain a first temperature control parameter, and transmit the first temperature control parameter to each of the sub-control units in signal communication with the main control unit; a second temperature control module, configured to perform a real-time analysis of the first temperature control parameter and the temperature characteristic according to a second PID temperature control algorithm preset in each of the sub-control units, so as to obtain a second temperature control parameter; The temperature control execution module is used to drive the execution unit in the temperature control module to perform temperature control according to each of the second temperature control parameters, so that the execution unit performs temperature control processing on the working terminal.

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