Self-adaptive temperature control method and device

Abstract

The invention discloses a self-adaptive temperature control method and device. The method comprises the following steps: acquiring an actual temperature and a target temperature, and initializing a difference value between an actual temperature value and a target value and a change rate of the difference value; mapping between the actual temperature and the target temperature and fuzzy values in fuzzy control is established according to the input quantity fuzzification quantization factor and the output quantity scaling factor; an adaptive coefficient m is established, the calculation formula of m is # imgabs0 #, ts is the target temperature, and ta is the actual temperature; the output quantity scale factor is corrected, and the correction formula is # imgabs1 #; and calculating Kp, Ki and Kd parameters in real time based on the fuzzy control rule table, and carrying out PID (Proportion Integration Differentiation) operation by using the Kp, Ki and Kd parameters so as to realize temperature control. According to the invention, the PID control parameters can be adaptively adjusted, and the PID parameters are automatically set according to the fuzzy empirical rule to achieve the temperature control precision and control speed under different conditions. The stability and the efficiency of the control system are ensured, and the temperature control precision and the control speed under different conditions can be achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of temperature control, and in particular relates to an adaptive temperature control method and device. Background Art

[0002] Fuzzy mathematics provides a method for dealing with uncertainty and ambiguity, and it has applications in many fields, including artificial intelligence, control systems, decision support systems, etc. Through fuzzy logic and fuzzy reasoning, the human ability to process fuzzy information can be simulated to make decisions with incomplete information.

[0003] Fuzzy variables, fuzzy sets and membership functions are basic concepts in fuzzy mathematics, which are used to deal with uncertainty and fuzziness in temperature control. Fuzzy variables are variables whose values ​​are not precise but have a certain degree of fuzziness. In classical mathematics, the values ​​of variables are definite, while in fuzzy mathematics, the values ​​of variables can be a range or a probability distribution. For example, temperature is a fuzzy variable because it can take a range of values, such as "warm" may correspond to a temperature between 20℃ and 30℃. Fuzzy sets are a generalization of classical sets. In classical sets, elements either belong to the set or do not belong to the set, which is a binary relationship. In fuzzy sets, the degree to which an element belongs to the set can be represented by a membership degree, which is a real number between 0 and 1. Fuzzy sets allow elements to belong to the set to different degrees, which reflects the fuzziness in temperature control. The membership function is the core concept in fuzzy sets, which defines the degree to which an element belongs to the fuzzy set. For a given fuzzy set and element, the membership function returns a value between 0 and 1, which indicates the degree to which the element belongs to the fuzzy set. For example, for the fuzzy set representing "young people", a 30-year-old person may have a membership of 0.8, while a 50-year-old person may have a membership of 0.3. The membership function is usually a continuous function, which can be linear or nonlinear, depending on the definition of the fuzzy set. The design of the membership function is an important task in fuzzy mathematics because it directly affects the representation of fuzzy sets and the reasoning of fuzzy logic.

[0004] The fuzzy rule base is the part of the fuzzy logic system that stores fuzzy rules. These rules usually appear in the form of "if-then" and describe the fuzzy relationship between input variables and output variables. These rules are based on expert knowledge or experience and are used to guide the decision-making process of the fuzzy control system. For example, a simple fuzzy logic rule may be "If the temperature is high and the humidity is low, then the comfort level is low." Each rule in the fuzzy rule base contains input conditions and corresponding output actions. These rules map fuzzy inputs to fuzzy outputs. Defuzzification is an important step in the fuzzy inference engine, also known as defuzzification. This process converts the fuzzy set obtained by fuzzy reasoning into a specific and clear output value. There are many methods of defuzzification, the most commonly used ones include the maximum membership method, the centroid method (center of mass method) and the weighted average method:

[0005] (1) Maximum membership method: The control value with the largest membership is selected as the final output.

[0006] (2) Center of gravity method (centroid method): Calculate the centroid of the fuzzy output to determine the final control value. The formula is: Among them, FS is Fire Strength (output membership), OW is Output Weight (output weight), which is usually the middle value of each set.

[0007] (3) Weighted average method: The weighted average is performed based on the membership degree of each fuzzy set and the corresponding output value to obtain the final output value.

[0008] A "BP neural network algorithm based on PID regulation" disclosed in Chinese patent literature, with the announcement number: CN106682735B, discloses that a mathematical model is established for the target temperature, actual temperature, target output, and actual output, and the model is input into the BP neural network after normalization to adjust the PID parameters to achieve more accurate temperature control. By improving the steepest descent method, the control step size P is adjusted in real time to make the target output escape from the local optimal solution and tend to stabilize at the global optimal solution. However, this solution has the problem of poor temperature control accuracy after the actual conditions change due to the fixed parameters in the temperature control. Summary of the invention

[0009] In view of the above prior art, the purpose of the present invention is to provide a more stable adaptive temperature control method and device, aiming to solve the problem that the general PID temperature control method has poor temperature control accuracy after the actual conditions change due to fixed parameters.

[0010] To solve the above technical problems, in the first aspect, the present application provides an adaptive temperature control method, comprising: performing temperature control based on the three basic parameters Kp, Ki, and Kd of PID, specifically comprising the following steps:

[0011] S1, obtaining the actual temperature and the target temperature and initializing the temperature difference between the actual temperature value and the target temperature value, as well as the rate of change of the difference;

[0012] S2. A mapping between the actual temperature and the target temperature and the fuzzy value in the fuzzy control is established according to the input fuzzy processing quantization factor and the output proportional factor, wherein the calculation formula of the output proportional factor is Ku=yu / h, yu is the basic domain of the controlled object, h is the fuzzy domain output by the controller, and u represents Kp, Ki, and Kd;

[0013] S3, establishing an adaptive coefficient m, wherein the calculation formula of m is m=(ts-ta) / ts, ts is the target temperature, ta is the actual temperature; and correcting the output proportional factor, the correction formula is Ku'=m*Ku;

[0014] S4. Calculate Kp, Ki, and Kd parameters in real time based on the fuzzy control rule table, and use the Kp, Ki, and Kd parameters to perform PID operation to achieve temperature control.

[0015] Preferably, the fuzzy subset in the fuzzy control is {NB, NM, NS, 0, PS, PM, PB}.

[0016] Preferably, the fuzzy domains of the difference E between the actual value of the initialization temperature and the target value and the rate of change of the difference Ec are both [-6, 6], and the basic domains E = [-10, 10] and Ec = [-1, 1];

[0017] The fuzzy domains of the outputs Kp, Ki, and Kd are all [-7,7], and the basic domains are [0,100], [0,10], and [0,20] respectively.

[0018] Preferably, the calculation formula for the quantization factor of the input fuzzy processing is Ke=2n / (Ymax-Ymin), wherein [Ymin, Ymax] is the basic domain of the input, and 2n is the number of elements contained in the fuzzy subset domain of the input.

[0019] Preferably, when setting the fuzzy subsets, each of the fuzzy subsets is provided with a plurality of state values ​​corresponding to the fuzzy variables, and the state values ​​include the change values ​​or amplitudes of the corresponding fuzzy variables.

[0020] Preferably, the fuzzy control rule table is provided with corresponding rules between different fuzzy subsets, and any two fuzzy subsets are provided with a plurality of corresponding rules, and the state values ​​correspond to respective membership values. Specifically, when the input deviation is large, the membership function is smooth, and when the input deviation is small, the membership function changes rapidly.

[0021] Preferably, when determining the activation status of the fuzzy control rule base, the process includes obtaining an input value, wherein the input value is a precise control value; determining a membership value corresponding to the precise control value, and confirming the activation status of the rules in the fuzzy control rule base according to the membership value; wherein the fuzzy control rule table is:

[0022] When the difference is negative and large, and the difference changes to negative, the difference has a tendency to increase, and the output is taken to be positive and large to prevent the difference from becoming larger as soon as possible and eliminate the existing negative and large difference;

[0023] When the difference is negative and the difference change is positive, the control system operates in the direction of reducing the difference;

[0024] When the difference value changes to a positive or medium value, it is not suitable to change its trend and the output is taken as zero;

[0025] When the difference changes in a positive direction, the output is in the middle;

[0026] When the difference is negative, the output should change so that the difference is eliminated as quickly as possible, and the output selection should be the same as when the difference is negative;

[0027] When the difference is negative, the system approaches a steady state.

[0028] In a second aspect, the present application further provides an adaptive temperature control device for implementing the adaptive temperature control method as described in the first aspect, comprising a mica sheet, a heating platform, a temperature sensor, and a controller, wherein the mica sheet is controlled by the controller to heat the heating platform;

[0029] The temperature sensor is used to obtain the actual temperature and the target temperature and initialize the temperature difference between the actual temperature value and the target temperature value, as well as the rate of change of the difference;

[0030] The controller is electrically connected to the temperature sensor and is used to execute the steps in the following modules, specifically including:

[0031] Mapping module: used to establish the mapping between the actual temperature and the target temperature and the fuzzy value in the fuzzy control according to the input fuzzy processing quantization factor and the output proportional factor, wherein the calculation formula of the output proportional factor is Ku=yu / h, yu is the basic domain of the controlled object, h is the fuzzy domain output by the controller, and u represents Kp, Ki, and Kd;

[0032] Adaptive module: used to establish an adaptive coefficient m, where the calculation formula of m is m=(ts-ta) / ts, ts is the target temperature, and ta is the actual temperature; and to correct the output proportional factor, and the correction formula is Ku'=m*Ku;

[0033] Temperature control module: used for calculating Kp, Ki, Kd parameters in real time based on the fuzzy control rule table, and performing PID operation with the Kp, Ki, Kd parameters to achieve temperature control of the heating platform.

[0034] In a third aspect, the present application also provides an adaptive temperature control device, comprising

[0035] Memory for storing computer programs;

[0036] A processor is used to implement the steps of the adaptive temperature control method as described in the first aspect when executing the computer program.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the adaptive temperature control method as described in the first aspect are implemented.

[0038] The beneficial effects of this application are as follows:

[0039] This application can adaptively adjust PID control parameters and automatically set PID parameters according to fuzzy empirical rules to achieve temperature control accuracy and control speed under different conditions. Ensure the stability and efficiency of the control system, so that the temperature control accuracy and control speed under different conditions can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 Flowchart provided for this application;

[0042] Figure 2 Logical diagram provided for this application;

[0043] Figure 3 Input membership diagram provided for this application;

[0044] Figure 4 Output membership diagram provided for this application;

[0045] Figure 5 A table of fuzzy control rules provided for this application;

[0046] Figure 6 Correspondence provided for this application Figure 5 The fuzzy control rule table of specific numbers;

[0047] Figure 7 This is a diagram of the device structure provided for this application. DETAILED DESCRIPTION

[0048] As required, detailed embodiments of the present invention are disclosed herein, but it should be understood that the disclosed embodiments are merely exemplary of the present invention, and the present invention may be implemented in different and alternative forms. The accompanying drawings may not be drawn to scale, and certain features may be exaggerated or reduced to show the details of specific components. Therefore, the specific structural and functional details disclosed herein should not be understood as having a limiting meaning, but only as a representative basis to teach those skilled in the art to adopt the present invention differently. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0049] The following is an explanation of the prior art used in this application:

[0050] Fuzzy control is a control method based on fuzzy logic principles, which imitates the human thinking process, especially the ability to deal with uncertainty and ambiguity. The core idea of ​​fuzzy control is to transform human control strategies and experience into algorithms that can be executed by computers. The following are the basic principles and steps of fuzzy control:

[0051] 1) Fuzzification input variables: The controlled system variables (such as temperature, speed, etc.) are taken as input.

[0052] Quantization Factors: Converting exact input values ​​to fuzzy values ​​This step involves defining quantization factors that map the range of input values ​​to the universe of fuzzy sets.

[0053] 2) Rule Evaluation

[0054] Fuzzy rules: Based on expert knowledge or operational experience, a series of fuzzy control rules are defined. These rules describe the relationship between input variables, usually expressed in the form of "If-Then".

[0055] Rule base: stores all fuzzy rules, which are used in the fuzzy reasoning process.

[0056] 3) Fuzzy Inference

[0057] Reasoning mechanism: Based on the current fuzzy input, fuzzy rules are applied to perform reasoning and obtain the output fuzzy set.

[0058] Reasoning methods: Common reasoning methods include Mamdani method and Sugeno method.

[0059] 4) Defuzzification

[0060] Output variable: Convert the fuzzy set obtained by reasoning back to precise numerical values ​​to facilitate the control of actuators (such as motors, valves, etc.).

[0061] like Figure 1 and Figure 2 Specifically, this embodiment includes the following steps:

[0062] S10, obtaining the actual temperature and the target temperature and initializing the temperature difference between the actual temperature value and the target temperature value, and the rate of change of the difference;

[0063] E (difference between actual temperature and target temperature): This is the difference between the target temperature and the actual temperature. This value indicates the degree of deviation of the current temperature control. Ec (rate of change of difference): This is the rate of change of error E over time, reflecting the trend of temperature change. E and Ec are the two basic inputs of the temperature control system, which together determine the control strategy of the system.

[0064] S20, establishing a mapping between the actual temperature and the target temperature and the fuzzy value in the fuzzy control according to the input fuzzy processing quantization factor and the output proportional factor, wherein the calculation formula of the output proportional factor is Ku=yu / h, yu is the basic domain of the controlled object, h is the fuzzy domain output by the controller, and u represents Kp, Ki, and Kd;

[0065] The main purpose is to determine the fuzzy set, fuzzy variables, membership function, fuzzy rule base and defuzzification process. Specifically, step S20 is to convert the precise temperature difference (E) and the difference change rate (Ec) into fuzzy values ​​for use in the fuzzy logic controller. The quantization factor Ke is used to determine the distribution of the input value in the fuzzy domain, which affects the fineness of the fuzzification process. At the same time, the output (fuzzy value) of the fuzzy controller is converted back into an accurate control signal for use in actual actuators (such as heaters or coolers). The proportional factor Ku is used to adjust the fuzzy output values ​​to ensure that they are meaningful in actual control, that is, they can be correctly mapped to the operating range of the actuator. The fuzzy subsets in fuzzy control are {NB, NM, NS, 0, PS, PM, PB}, including NB (negative large), NM (negative medium), NS (negative small), 0, PS (positive small), PM (positive medium), PB (positive large). These subsets define the linguistic variables of fuzzy control, which will be used for the formulation of fuzzy rules and fuzzy reasoning. Specifically, the fuzzy domains of the difference E between the actual value of the initial temperature and the target value and the rate of change of the difference Ec are both [-6, 6], and the basic domains are E = [-10, 10] and Ec = [-1, 1]; the fuzzy domains of the outputs Kp, Ki, and Kd are all [-7, 7], and the basic domains are [0, 100], [0, 10], and [0, 20] respectively.

[0066] S30, establishing an adaptive coefficient m, wherein the calculation formula of m is m=(ts-ta) / ts, ts is the target temperature, and ta is the actual temperature; and correcting the output proportional factor, the correction formula is Ku'=m*Ku;

[0067] Ke and Ku: Ke and Ku are interrelated because they work together to ensure that the fuzzy values ​​of input and output can be correctly mapped to the actual temperature control. Ke is responsible for converting the precise input value to the fuzzy value, while Ku is responsible for converting the fuzzy output value back to a precise control signal.

[0068] Ku and m: The relationship between Ku and m is that Ku ensures that the output of the fuzzy controller can effectively control the actuator, while m dynamically adjusts these output values ​​according to the difference between the actual temperature and the target temperature to improve the adaptability and stability of the control.

[0069] Ke and m: Ke affects the accuracy of the fuzzy processing, while m dynamically adjusts the control strategy based on the actual deviation of the temperature. A finer fuzzy processing (determined by Ke) can provide more precise fuzzy values, which helps m adjust the control strategy more accurately.

[0070] like Figure 3-6As shown, the fuzzy control rule table is provided with corresponding rules between different fuzzy subsets, and there are several corresponding rules between any two fuzzy subsets. The state values ​​correspond to their respective membership values. Specifically, when the input deviation is large, the membership function is gentle, and when the input deviation is small, the membership function changes quickly, so the isosceles triangle membership function is selected. When determining the activation of the fuzzy control rule base, it includes obtaining the input value, and the input value is the precise control value; determining the membership value corresponding to the precise control value, and confirming the activation of the rules in the fuzzy control rule base according to the membership value; wherein the fuzzy control rule table is:

[0071] When the difference is negative and large, and the difference changes to negative, the difference has a tendency to increase, and the output is taken to be positive and large to prevent the difference from becoming larger as soon as possible and eliminate the existing negative and large difference;

[0072] When the difference is negative and the difference change is positive, the control system operates in the direction of reducing the difference;

[0073] When the difference value changes to a positive or medium value, it is not suitable to change its trend and the output is taken as zero;

[0074] When the difference changes in a positive direction, the output is in the middle;

[0075] When the difference is negative, the output should change so that the difference is eliminated as quickly as possible, and the output selection should be the same as when the difference is negative;

[0076] When the difference is negative, the system approaches a steady state. The search range for adjusting parameters according to the fuzzy output value includes determining the corresponding fuzzy variables and membership degrees in the activation state, and determining the control parameters corresponding to the fuzzy variables and the target parameters corresponding to the membership degrees as the search range.

[0077] S40, calculating Kp, Ki, Kd parameters in real time based on the fuzzy control rule table, and performing PID operation with the Kp, Ki, Kd parameters to achieve temperature control.

[0078] In step S40, the output of real-time control is calculated, and the fuzzy clarity adopts the weighted average method. According to the input of the above steps and the fuzzy control rule table, the real-time kp, ki, and kd parameters are calculated. Then, PID operation is performed based on the kp, ki, and kd parameters calculated in real time to obtain the actual output variable to the actuator to control the heating power and achieve the automatic temperature control function.

[0079] Compared with the traditional PID control method: Fixed parameters: In the traditional PID control, the three parameters of proportion (P), integration (I) and differentiation (D) are pre-set fixed values, which usually need to be determined by manual adjustment or empirical formulas. Linear control: The PID controller assumes that the system is linear or approximately linear, and it calculates the control signal based on linear control theory. Response adjustment: When facing system parameter changes or external disturbances, the response of the PID controller may not be flexible enough, and the parameters need to be readjusted to adapt to the new situation. Control accuracy: For some complex, nonlinear or time-varying systems, traditional PID control may not be able to achieve very high control accuracy. The present application can solve the problem of poor temperature control accuracy after actual conditions change due to fixed parameters in the general PID temperature control method. The present invention can adaptively adjust the PID control parameters and automatically set the PID parameters according to fuzzy empirical rules to achieve temperature control accuracy and control speed under different conditions.

[0080] like Figure 7 As shown, an adaptive temperature control device comprises:

[0081] A memory 30, for storing computer programs;

[0082] The processor 31 is used to implement the steps of the adaptive temperature control method mentioned in the above embodiment when executing the computer program.

[0083] The adaptive temperature control device provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer, or a desktop computer.

[0084] Among them, the processor 31 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 31 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 31 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 31 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0085] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 30 is at least used to store the following computer program, wherein, after the computer program is loaded and executed by the processor 31, it can implement the relevant steps of the adaptive temperature control method disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 30 may also include operating devices and data, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating device may include Windows, Unix, Linux, etc. The data may include but is not limited to the adaptive temperature control method, etc.

[0086] In some embodiments, the adaptive temperature control device may further include a display screen, an input and output interface, a communication interface, a power supply, and a communication bus.

[0087] Those skilled in the art will understand that Figure 3 The structure shown in the figure does not constitute a limitation of the adaptive temperature control device, and may include more or fewer components than shown in the figure.

[0088] The adaptive temperature control device provided in the embodiment of the present application includes a memory 30 and a processor 31. When the processor 31 executes the program stored in the memory 30, the adaptive temperature control method can be implemented.

[0089] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps recorded in the above method embodiment are implemented.

[0090] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory), ROM, random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

[0091] The above is a detailed introduction to an adaptive temperature control method, device, equipment and medium provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can refer to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

[0092] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

Claims

1. An adaptive temperature control method, which performs temperature control based on the three basic parameters Kp, Ki, and Kd of PID, characterized in that: The following steps are involved: S1, obtaining the actual temperature and the target temperature and initializing the temperature difference between the actual temperature value and the target temperature value, as well as the rate of change of the difference; S2. Establish a mapping between the actual temperature, the target temperature and the fuzzy value in the fuzzy control according to the input fuzzy processing quantization factor and the output proportional factor, wherein the calculation formula of the output proportional factor is: , yu is the basic domain of the controlled object, h is the fuzzy domain of the controller output, and u represents Kp, Ki, and Kd; S3. Establish an adaptive coefficient m, where the calculation formula of m is: , ts is the target temperature, ta is the actual temperature; and the output proportional factor is corrected, the correction formula is: ; S4. Calculate Kp, Ki, and Kd parameters in real time based on the fuzzy control rule table, and use the Kp, Ki, and Kd parameters to perform PID operation to achieve temperature control.

2. The adaptive temperature control method according to claim 1, characterized in that: The fuzzy subset {NB, NM, NS, 0, PS, PM, PB} in the fuzzy control.

3. The adaptive temperature control method according to claim 1, characterized in that: The fuzzy domains of the difference between the actual value of the initial temperature and the target value E and the rate of change of the difference Ec are both [-6,6], and the basic domain and ; The fuzzy domains of the outputs Kp, Ki, and Kd are all [-7,7], and the basic domains are [0,100], [0,10], and [0,20] respectively.

4. The adaptive temperature control method according to claim 1, characterized in that: The calculation formula of the quantization factor of the input fuzzy processing is: , where [Ymin, Ymax] is the basic domain of the input quantity, and 2n is the number of elements contained in the fuzzy subset domain of the input quantity.

5. The adaptive temperature control method according to claim 1, characterized in that: When setting the fuzzy subsets, each of the fuzzy subsets is provided with a number of state values ​​corresponding to the fuzzy variables, and the state values ​​include the change value or amplitude of the corresponding fuzzy variable.

6. The adaptive temperature control method according to claim 5, characterized in that: The fuzzy control rule table is provided with corresponding rules between different fuzzy subsets, and any two fuzzy subsets are provided with several corresponding rules. The state values ​​correspond to their respective membership values. Specifically, when the input deviation is large, the membership function is smooth, and when the input deviation is small, the membership function changes rapidly.

7. The adaptive temperature control method according to claim 1, characterized in that: When determining the activation status of the fuzzy control rule base, it includes obtaining an input value, wherein the input value is a precise control value; determining a membership value corresponding to the precise control value, and confirming the activation status of the rules in the fuzzy control rule base according to the membership value; wherein the fuzzy control rule table is: When the difference is negative and large, and the difference changes to negative, the difference has a tendency to increase, and the output is taken to be positive and large to prevent the difference from becoming larger as soon as possible and eliminate the existing negative and large difference; When the difference is negative and the difference change is positive, the control system operates in the direction of reducing the difference; When the difference value changes to a positive or medium value, it is not suitable to change its trend and the output is taken as zero; When the difference changes in a positive direction, the output is in the middle; When the difference is negative, the output should change so that the difference is eliminated as quickly as possible, and the output selection should be the same as when the difference is negative; When the difference is negative, the system approaches a steady state.

8. An adaptive temperature control device, used to implement the adaptive temperature control method according to any one of claims 1 to 7, characterized in that: It comprises a mica sheet, a heating platform, a temperature sensor, and a controller, wherein the mica sheet is controlled by the controller to heat the heating platform; The temperature sensor is used to obtain the actual temperature and the target temperature and initialize the temperature difference between the actual temperature value and the target temperature value, as well as the rate of change of the difference; The controller is electrically connected to the temperature sensor and is used to execute the steps in the following modules, specifically including: Mapping module: used to establish the mapping between actual temperature and target temperature and fuzzy value in fuzzy control according to the input fuzzy processing quantization factor and output proportional factor, wherein the calculation formula of the output proportional factor is: , yu is the basic domain of the controlled object, h is the fuzzy domain of the controller output, and u represents Kp, Ki, and Kd; Adaptive module: used to establish the adaptive coefficient m, where the calculation formula of m is , ts is the target temperature, ta is the actual temperature; and the output proportional factor is corrected, the correction formula is: ; Temperature control module: used for calculating Kp, Ki, Kd parameters in real time based on the fuzzy control rule table, and performing PID operation with the Kp, Ki, Kd parameters to achieve temperature control of the heating platform.

9. An adaptive temperature control device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the adaptive temperature control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the adaptive temperature control method according to any one of claims 1 to 7 are implemented.

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