A temperature control method and related equipment

Abstract

The present application provides a temperature control method and related equipment, including: periodically obtaining the set temperature value and the actual temperature value of the temperature control system, calculating the error value and the error deviation value at the current moment; using the error value and the error deviation value at the current moment as the input variables of the fuzzy PID algorithm for fuzzification and defuzzification processing, and obtaining the coefficient correction value of the fuzzy PID algorithm; according to the coefficient correction value and the initial value of the parameter obtained by the self-tuning algorithm, obtaining the coefficient target value of the control parameter, and according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment, obtaining the fuzzy control temperature value output by the temperature control system at the current moment, so as to perform temperature control. The PID control parameters adjusted by the temperature control self-tuning algorithm are used as the initial parameters of the fuzzy PID, thereby combining the improved self-tuning algorithm with the fuzzy PID control, so that the PID control parameters are adaptively changed with the environmental changes during the entire temperature control process, thereby improving the temperature control effect.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of temperature control, and in particular to a temperature control method and related equipment. Background Art

[0002] At present, some equipment in the photovoltaic, lithium battery, semiconductor, packaging and other industries have a great demand for temperature control. The current temperature control products mainly include thermostats, temperature control modules, etc., which mainly use a control algorithm (PID algorithm) that integrates proportional, integral and differential to control temperature. The existing PID temperature control algorithm mostly achieves a better temperature control effect by manually adjusting the PID control parameters continuously.

[0003] However, the above algorithm still has some defects in practical application: for example, the existing PID control parameters require a lot of manual experiments to get a more accurate value, and when the environment changes significantly, manual re-debugging is required, wasting a lot of time and human resources in the whole process. Summary of the invention

[0004] The embodiments of the present application provide a temperature control method and related equipment for adaptively adjusting control parameters during the entire temperature control process to reduce manpower and time costs.

[0005] A first aspect of an embodiment of the present application provides a temperature control method, which is applied to a temperature control system. The method includes:

[0006] Periodically obtaining the set temperature value and the actual temperature value of the temperature control system, calculating the error value at the current moment and the error value at the previous moment, and determining the error deviation value at the current moment;

[0007] The current moment error value and the current moment error deviation value are used as input variables of the fuzzy PID algorithm for fuzzification, and the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm is output; wherein the control parameter includes the proportional coefficient, the integral coefficient and the differential coefficient in the fuzzy PID algorithm;

[0008] Defuzzifying the coefficient fuzzy value to obtain a coefficient correction value of the fuzzy PID algorithm;

[0009] According to the coefficient correction value and the initial parameter value of the control parameter, the coefficient target value of the control parameter is obtained, and according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment, the fuzzy control temperature value output by the temperature control system at the current moment is obtained to perform temperature control; wherein, the initial parameter value of the control parameter is obtained after calculation by the temperature control self-tuning algorithm.

[0010] Optionally, the fuzzification of the error value at the current moment and the error deviation value at the current moment as input variables of a fuzzy PID algorithm, and outputting the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm includes:

[0011] Dividing the current moment error value and the current moment error deviation value into a plurality of fuzzy sets, and setting a corresponding membership function in each fuzzy set;

[0012] According to the membership function, obtaining the membership values ​​of the current moment error value and the current moment error deviation value in each fuzzy set;

[0013] Obtaining a fuzzy control rule table of the proportional coefficient, the integral coefficient and the differential coefficient in the control parameters;

[0014] In combination with the fuzzy control rule table, the membership value is subjected to parameter inference in the fuzzy PID algorithm to obtain the coefficient fuzzy value; wherein the coefficient fuzzy value includes the fuzzy value of the proportional coefficient, the fuzzy value of the integral coefficient and the fuzzy value of the differential coefficient.

[0015] Optionally, defuzzifying the coefficient fuzzy value to obtain the coefficient correction value of the fuzzy PID algorithm includes:

[0016] Based on the fuzzy control rule table, the coefficient fuzzy value is defuzzified using an inference and deduction method, and the coefficient correction value of fuzzy setting is output; wherein the inference and deduction method is any one or more of the maximum membership method, the coefficient weighted average method or the centroid method.

[0017] Optionally, the temperature control self-tuning algorithm includes:

[0018] Obtaining the current temperature value, target temperature value, temperature threshold, sampling period and relay output amplitude of the temperature control system at the current moment; wherein the relay output amplitude is used to represent the maximum voltage value output by the temperature control system;

[0019] According to the current temperature value and the target temperature value, a current temperature difference value is obtained; according to the temperature threshold value, the current temperature difference value and the current temperature value, a reference set temperature value is obtained;

[0020] When the temperature control system performs full-speed heating at the current temperature value, a first recording time and a second recording time are recorded; wherein the first recording time is used to represent the recording time when the temperature value of the temperature control system reaches the target temperature value, and the second recording time is used to represent the recording time when the temperature control system rises to the highest temperature due to inertia;

[0021] Based on the energy supply parameter function, the reference set temperature value, the current temperature value, the first recording time, the second recording time and the sampling period are calculated to obtain energy supply parameters; based on the environmental parameter function, the energy supply parameter, the first recording time and the second recording time are calculated to obtain environmental parameters;

[0022] According to the maximum temperature value corresponding to the maximum temperature, the reference set temperature value and the relay output amplitude, the proportional coefficient value of the control parameter is obtained; according to the maximum temperature value, the reference set temperature value, the first recording time, the second recording time, the sampling period, the environmental parameters and the temperature threshold, the integral coefficient value of the control parameter is obtained; the integral coefficient value is proportionally calculated to obtain the differential coefficient value of the control parameter.

[0023] Optionally, the calculation method of the initial value of the parameter includes:

[0024] Obtaining a proportional coefficient value corresponding to a proportional coefficient, an integral coefficient value corresponding to an integral coefficient, and a differential coefficient value corresponding to a differential coefficient in the parameter initial value of the control parameter;

[0025] The proportional coefficient value is used as the initial value of the proportional coefficient corresponding to the proportional coefficient in the control parameter; the proportional coefficient value, the integral coefficient value and the sampling period are calculated to obtain the initial value of the integral coefficient corresponding to the integral coefficient in the control parameter; the proportional coefficient value, the differential coefficient value and the sampling period are calculated to obtain the initial value of the differential coefficient corresponding to the differential coefficient in the control parameter; wherein the initial value of the proportional coefficient, the initial value of the integral coefficient and the initial value of the differential coefficient are the initial values ​​of the parameters.

[0026] Optionally, obtaining the coefficient target value of the control parameter according to the coefficient correction value and the initial value of the control parameter includes:

[0027] Calculating the proportional coefficient correction value and the proportional coefficient initial value to obtain the proportional coefficient target value of the proportional coefficient;

[0028] Calculating the integral coefficient correction value and the integral coefficient initial value to obtain a proportional integral target value of the integral coefficient;

[0029] The differential coefficient correction value and the differential coefficient initial value are calculated to obtain the differential coefficient target value of the differential coefficient.

[0030] Optionally, obtaining the fuzzy control temperature value output by the temperature control system at the current moment according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment includes:

[0031] The coefficient target value, the current error value and the current error deviation value are input into the PID output function of the fuzzy PID algorithm to obtain the fuzzy controlled temperature value; wherein the PID output function is PID_OUT(k)=K p *E(k)+K i *∑E(k)+K d *E c (k), where k is used to describe the kth moment, and K p is used to describe the proportional coefficient target value corresponding to the proportional coefficient in the coefficient target value, the E(k) is used to describe the error value at the current moment of the kth moment, the K i It is used to describe the integral coefficient target value corresponding to the integral coefficient in the coefficient target value, the K d It is used to describe the differential coefficient target value corresponding to the differential coefficient in the coefficient target value, and the E c (k) is used to describe the error deviation value at the current moment of the kth moment.

[0032] A second aspect of an embodiment of the present application provides a temperature control system, comprising:

[0033] An acquisition unit, used for periodically acquiring a set temperature value and an actual temperature value of the temperature control system, calculating an error value at a current moment and an error value at a previous moment, and determining an error deviation value at the current moment;

[0034] An output unit, used to fuzzify the error value at the current moment and the error deviation value at the current moment as input variables of the fuzzy PID algorithm, and output the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm; wherein the control parameter includes the proportional coefficient, integral coefficient and differential coefficient in the fuzzy PID algorithm;

[0035] The acquisition unit is further used to defuzzify the coefficient fuzzy value to obtain the coefficient correction value of the fuzzy PID algorithm;

[0036] The acquisition unit is also used to obtain the coefficient target value of the control parameter according to the coefficient correction value and the parameter initial value of the control parameter, and obtain the fuzzy control temperature value output by the temperature control system at the current moment according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment, so as to perform temperature control; wherein the parameter initial value of the control parameter is obtained after calculation by the temperature control self-tuning algorithm.

[0037] The temperature control system provided in the second aspect of the embodiment of the present application is used to execute the temperature control method described in the first aspect.

[0038] A third aspect of the present application provides a temperature control device, comprising:

[0039] CPU, memory, input and output interfaces, wired or wireless network interfaces, and power supply;

[0040] The memory is a short-term storage memory or a persistent storage memory;

[0041] The central processing unit is configured to communicate with the memory and execute instruction operations in the memory to perform the temperature control method described in the first aspect.

[0042] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes instructions. When the instructions are executed on a computer, the computer executes the temperature control method described in the first aspect.

[0043] A fifth aspect of the embodiments of the present application provides a computer program product, which includes instructions. When the instructions are executed on a computer, the computer executes the temperature control method described in the first aspect.

[0044] It can be seen from the above technical scheme that the embodiments of the present application have the following advantages: through a temperature control method disclosed in the embodiments of the present application, in the fuzzy PID control algorithm, since the initial value of the control parameter has a greater impact on the fuzzy PID control, the PID control parameters tuned by the temperature control self-tuning algorithm are used as the initial parameters of the fuzzy PID, thereby combining the improved self-tuning algorithm with the fuzzy PID control, so that the PID control parameters can be adaptively changed with environmental changes during the entire temperature control process, thereby improving the temperature control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 A schematic diagram of a temperature control method disclosed in an embodiment of the present application;

[0047] Figure 2 A schematic flow chart of another temperature control method disclosed in an embodiment of the present application;

[0048] Figure 3 A schematic flow chart of another temperature control method disclosed in an embodiment of the present application;

[0049] Figure 4 A schematic flow chart of another temperature control method disclosed in an embodiment of the present application;

[0050] Figure 5 A schematic flow chart of another temperature control method disclosed in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of the structure of a temperature control system disclosed in an embodiment of the present application;

[0052] Figure 7 This is a schematic diagram of the structure of a temperature control device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0054] It should be noted that the descriptions involving "first", "second", etc. in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0055] The existing PID temperature control algorithms can be divided into the following three categories: (1) Classical PID control, which achieves a better temperature control effect by manually adjusting the PID control parameters continuously; (2) First, the PID control parameters are adjusted through methods such as relay feedback self-tuning, and then PID temperature control is performed; (3) Adaptive temperature control is performed by combining artificial intelligence algorithms such as neural network algorithms, fuzzy control, and genetic algorithms with classical PID algorithms.

[0056] However, in practical applications, the above algorithms still have some defects: the PID control parameters in method (1) require a large number of manual experiments to obtain a more accurate value, and when the environment changes significantly, manual debugging is required, wasting a lot of time and human resources in the whole process; the PID control parameters adjusted by the relay feedback self-tuning method in method (2) have a good temperature control effect in the PID control process, but it takes 1 to 2 oscillation cycles to calculate the PID control parameters during the self-tuning process, and a large overshoot will be generated during the self-tuning process, affecting the equipment; the artificial intelligence PID algorithm of method (3) depends on the initial parameters. When the initial parameters are given more accurately, there is a better temperature control effect. When the initial parameters are given inaccurately, the temperature control effect will be poor or even fail.

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] See also Figure 1 , Figure 1 This is a flow chart of a temperature control method disclosed in an embodiment of the present application, including steps 101 to 104.

[0059] 101. Periodically obtain the set temperature value and the actual temperature value of the temperature control system, calculate the error value at the current moment and the error value at the previous moment, and determine the error deviation value at the current moment.

[0060] In this embodiment, a fuzzy PID algorithm (FPID, FuzzyPID) is mainly designed. The FPID algorithm is applied to a temperature control system, or other equipment or devices that can perform temperature control. Furthermore, it can also be some specific electronic components. For example, a temperature module, which can be used in combination with a programmable logic controller (PLC, Programmable Logic Controller). Among them, it should be noted that the PID algorithm is a control algorithm that combines three links: proportional (Proportional, P), integral (Integral, I), and differential (Differential, D).

[0061] Specifically, in the fuzzy PID algorithm FPID, the error value between the set temperature value and the actual temperature value collected by the temperature control system at the current moment, as well as the error value at the previous moment, can be periodically obtained, and then the error value at the current moment and the error value at the previous moment can be determined. Thus, the error deviation value of the two at the current moment can be obtained through the error value at the current moment and the error value at the previous moment.

[0062] In one specific embodiment, since the input signal of the fuzzy interface module of the temperature control system is the difference E between the set temperature value of the temperature control system and the actual temperature value of the electric heating furnace, then the difference at the current moment k can be defined as E(k), and the difference at the previous moment can be defined as E(k-1). It can also be understood that the difference at this time can be changed into the error change rate or the integral of the error, etc., which is not limited here.

[0063] Furthermore, the current error deviation value E c (k) can be expressed as E c (k) = E(k) - E(k-1).

[0064] 102. The error value at the current moment and the error deviation value at the current moment are used as input variables of the fuzzy PID algorithm for fuzzification, and the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm is output.

[0065] Furthermore, by fuzzifying the current error value and the current error deviation value as input variables of the fuzzy PID algorithm, the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm can be output. It should be noted that the control parameter includes the proportional coefficient, integral coefficient and differential coefficient in the fuzzy PID algorithm.

[0066] In one of the specific embodiments, it is necessary to fuzzify the error value at the current moment and the error deviation value at the current moment. Among them, the error value at the current moment and the error deviation value at the current moment can be converted into a fuzzy variable, and then used as an input variable input value fuzzy PID algorithm, so that the fuzzy PID algorithm can output the coefficient fuzzy value of the control parameter. Among them, fuzzy control is a control method for controlling actual equipment based on the nonlinear characteristics of human language. The fuzzy control method is combined with various control methods such as PID control, Kalman filter control, PD robust adaptive control, Ziegler-Nichols control, etc., each of which plays its own advantages, so that fuzzy control can inherit previous research results and achieve better control effects. This scheme adopts a fuzzy controller using two input variables, in which the input variables are the difference of the signal (i.e. the error value at the current moment) and the change of the signal difference (i.e. the error deviation value at the previous moment). It is specifically a mamdini fuzzy controller.

[0067] Specifically, the fuzzification of input variables mainly completes two tasks: 1. Transformation of domain: The difference between the temperature setting value and the actual temperature value of the temperature control system and the rate of change of the difference are both represented by analog control quantities, and the range of change of the analog control quantity is an actual real domain. When using fuzzy control, the real domain must be transformed into the domain within the fuzzy control system, whether it is a discrete domain or a continuous domain. After the transformation, the error signal and the rate of change of the error signal become e and e. c , both values ​​must be multiplied by the proportional factor. 2. Fuzzy processing: The temperature control system has been transformed with e and e c The value is still not a fuzzy variable, so it needs to be divided into several fuzzy sets. First, transform e and e c Each of them has a "zero" zone. The left side of the "zero" zone can be divided into three zones, from left to right, namely "negative large", "negative middle" and "negative small". The right side of the "zero" zone can be divided into three zones, from left to right, namely "positive small", "positive middle" and "positive large". c The respective membership functions are specified in the internal interval of , so that the error value E and the error change rate value E at a certain moment are c After transformation, we get the values ​​e and e c , and then calculate e and e respectively according to the membership function c The membership values ​​in the corresponding fuzzy sets are then converted into fuzzy variables. c After the two variables are fuzzified, the fuzzy values ​​of the proportional coefficient correction value, the integral coefficient correction value and the differential coefficient correction value can be obtained. Figure 2 The embodiment shown.

[0068] 103. Defuzzify the coefficient fuzzy value to obtain the coefficient correction value of the fuzzy PID algorithm.

[0069] Furthermore, since a fuzzy value is obtained in the above steps, it is necessary to defuzzify the coefficient fuzzy value to obtain the coefficient correction value of the fuzzy PID algorithm.

[0070] In one of the specific embodiments, after the fuzzy inference of the fuzzy controller, the temperature control system obtains a set of fuzzy data, which is still fuzzy data. In the actual control device, an accurate value must be output for control, thereby triggering the circuit. Therefore, it is also necessary to obtain an accurate value through defuzzification control of the fuzzy data set obtained by the fuzzy controller through fuzzy inference, that is, to obtain the coefficient correction value of the fuzzy PID algorithm. Among them, the coefficient correction value includes a proportional coefficient correction value, an integral coefficient correction value and a differential coefficient correction value.

[0071] 104. According to the coefficient correction value and the initial value of the control parameter, the coefficient target value of the control parameter is obtained, and according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment, the fuzzy control temperature value output by the temperature control system at the current moment is obtained to perform temperature control.

[0072] Furthermore, the coefficient target value of the control parameter can be obtained based on the coefficient correction value and the initial value of the control parameter, and then the fuzzy control temperature value output by the temperature control system at the current moment can be obtained based on the coefficient target value, the error value at the current moment and the error deviation value at the current moment, thereby performing temperature control.

[0073] In one specific embodiment, the initial value of the PID control parameter is screened by a temperature control self-tuning algorithm (IRFST, Improved relayfeedback self-tuning method), thereby obtaining the initial value of the parameter (see Figure 3 The embodiment shown in the figure) is then updated according to the parameter initial value and the coefficient correction value obtained in the above steps to update the proportional coefficient, integral coefficient and differential coefficient of the control parameter, thereby obtaining the coefficient target value. Furthermore, in the FPID algorithm, the coefficient target value, the error value at the current moment and the error deviation value at the current moment are used as input values ​​to obtain the output value at the current moment, that is, the fuzzy control temperature value, so that the temperature of the temperature control system can be controlled according to the fuzzy control temperature value. For easy understanding, please refer to Figure 4 and Figure 5 The embodiment shown.

[0074] A temperature control method disclosed in this embodiment, in a fuzzy PID control algorithm, since the initial value of the control parameter has a greater impact on the fuzzy PID control, the PID control parameters tuned by the temperature control self-tuning algorithm are used as the initial parameters of the fuzzy PID, thereby combining the improved self-tuning algorithm with the fuzzy PID control, so that during the entire temperature control process, the PID control parameters are adaptively changed with environmental changes, thereby improving the temperature control effect.

[0075] In the fuzzy PID control algorithm, since the input value of the fuzzy controller needs to be processed fuzzy value, please refer to Figure 2 , Figure 2 This is a flow chart of another temperature control method disclosed in an embodiment of the present application, including steps 201 to 205.

[0076] 201. Periodically obtain a set temperature value and an actual temperature value of a temperature control system, calculate an error value at a current moment and an error value at a previous moment, and determine an error deviation value at a current moment.

[0077] In this embodiment, step 201 is the same as the above Figure 1 The process is similar to step 101, and will not be described in detail here.

[0078] 202. Divide the current moment error value and the current moment error deviation value into multiple fuzzy sets, set a corresponding membership function in each fuzzy set, and obtain the membership value of the current moment error value and the current moment error deviation value in each fuzzy set according to the membership function.

[0079] By the above Figure 1 As shown in step 102, in this embodiment, the error value at the current moment and the error deviation value at the current moment can be divided into multiple fuzzy sets, so that a corresponding membership function is set in each fuzzy set, and then according to the membership function, the membership value of the error value at the current moment and the error deviation value at the current moment in each fuzzy set is obtained.

[0080] In one specific embodiment, first determine e and e c Fuzzy sets and domains. Among them, combined with Figure 1 As shown in step 102, the input variable E is set to the difference between the temperature control set value and the actual temperature. The real physical quantity domain value range of the input variable E is set to [-E max ,E max ], the fuzzy set domain of the input variable E is set to {-n,-n+1,...,0,n-1,n}. The quantization factor of the input variable E is set to k e =n / E max It should be noted that, at this time, E max >n, for example, Emax is 10, n is 6. Then, k e =6 / 10=0.6.

[0081] Input variable E c Set as the rate of change of the difference between the temperature control set point and the actual temperature, input variable E c The range of the domain of accurate real physical quantities is [-E cmax ,E cmax ], input variable E c The fuzzy set domain is set to {-m,-m+1,...,0,m-1,m}, and the input variable E c The quantization factor is set to k ec =m / E cmax It should be noted that, at this time, E cmax <n, for example, E cmax is 10, and m is 6. Then, k ec =6 / 5=1.2.

[0082] Fuzzy sets are always expressed by membership functions. Commonly used membership functions include trapezoidal, triangular, sigmf, bell, Gaussian, normal distribution, etc. Therefore, in this embodiment, when selecting the shape of the membership function, a mathematical expression that occupies less memory space and is relatively simple to operate is used as much as possible. For example, in this embodiment, a triangular membership function can be selected. Since the shape of the triangle is directly connected to the slope of the straight line, it is very suitable for the input variable of the fuzzy PID parameter self-tuning controller.

[0083] 203. Obtain a fuzzy control rule table of the proportional coefficient, integral coefficient and differential coefficient in the control parameters.

[0084] At the same time, the fuzzy control rule table of the proportional coefficient, integral coefficient and differential coefficient in the control parameters can also be obtained. It should be noted that the fuzzy control rule table is used to characterize the correlation between the proportional coefficient, integral coefficient and differential coefficient, and the current error value and the current error deviation value.

[0085] Specifically, the setting of the temperature control system in fuzzy control is mainly determined by two factors: the database and the rule base. The input parameters, output parameters and their corresponding membership functions are stored in the database and expressed in a tabular form. It contains all the fuzzy control information, including all the knowledge of fuzzy reasoning and defuzzification, such as the transformation method of the domain, the reasoning algorithm, the defuzzification algorithm, etc. The rule base of the fuzzy controller is generally represented by a set of "condition-action" rules, that is, the "if-then" rules of the preceding reasoning: if is the condition and then is the result. Generally, the input is used as the condition and the output is used as the result. For example, in one of the feasible technical solutions, there is the following relationship:

[0086] R1: if isA1 and e c is B1, then U is C1

[0087] R2: ife isA2 and e c is B2, then U is C2 ...

[0089] Rn:ife isA n and e c is B n ,thenU is C n

[0090] e and e c are variables, A1, A2, ..., A n is a fuzzy set of errors, B1,B2,...,B n is a fuzzy set of error change rates, C1,C2,...,C n is a fuzzy set of U, and each rule is a fuzzy relationship corresponding to the integral space. All conditional rules in the rule base are parallel and have an "or" logical relationship. Therefore, according to the proportional coefficient k p , integration coefficient k i , differential coefficient k d The fuzzy control rules of the three parameters are formulated based on their respective functions and mutual influence. The content of the specific fuzzy control rules is not limited here.

[0091] 204. Combined with the fuzzy control rule table, the membership value is used to perform parameter reasoning in the fuzzy PID algorithm to obtain the coefficient fuzzy value.

[0092] Furthermore, combined with the above fuzzy control rule table, the membership value can be used to infer the parameters in the fuzzy PID algorithm, thereby obtaining the coefficient fuzzy value. It should be noted that the coefficient fuzzy value includes the fuzzy value of the proportional coefficient, the fuzzy value of the integral coefficient and the fuzzy value of the differential coefficient.

[0093] In one specific embodiment, the control idea of ​​the temperature control system parameter self-tuning fuzzy PID control system is to first find out the three parameters k of PID control p ,k i ,k d The fuzzy control relationship between the error and the rate of change of the error. In actual operation, the system continuously detects the error and the rate of change of the error and sends them to the fuzzy controller. The fuzzy controller continuously corrects these three parameters online according to the fuzzy control rules to improve the control accuracy of the system. Specifically, E and E c The fuzzy value (corresponding to e,e c ), and k p ,k i ,k d The membership degree of the fuzzy subset is obtained to obtain the fuzzy values ​​of the proportional coefficient, integral coefficient and differential coefficient.

[0094] Based on the fuzzy control rule table, the coefficient fuzzy value is defuzzified using the inference and deduction method, and the fuzzy-tuned coefficient correction value is output.

[0095] In this embodiment, step 205 is the same as the aforementioned Figure 1 It is similar to step 103 in the above. However, it should be noted that, in this embodiment, the coefficient fuzzy value can be defuzzified by using the fuzzy control rule table and the inference and deduction method, so as to output the coefficient correction value of the fuzzy setting. It should be noted that the inference and deduction method is any one or more of the maximum membership method, the coefficient weighted average method or the centroid method.

[0096] In one of the specific embodiments, the maximum membership method is a method of selecting a function with the largest membership as a method of accurately controlling the numerical value during the fuzzy reasoning process. If there are multiple identical maximum values ​​in this set, the output result of the defuzzification control shall take the average of these maximum values; the second method is the coefficient weighted average method; the third method is the centroid method, which uses the area centroid formed by the membership function curve of the fuzzy set and the horizontal axis as a method of accurately controlling the numerical value.

[0097] In this embodiment, the centroid method is mainly used as the output of the fuzzy controller. Since the control rule table of the fuzzy controller is queried in an offline manner, the amount of calculation of this method is not large, and it will not affect the operating speed of the temperature control system, and can meet the response speed requirements of the electric heating furnace temperature control system. Specifically, the coefficient fuzzy value is defuzzified to obtain an accurate value, which is the k required by the PID controller. p ,k i ,k d .

[0098] Then, the fuzzy value of the proportional coefficient, the fuzzy value of the integral coefficient, and the fuzzy value of the differential coefficient are defuzzified to obtain the proportional coefficient correction value ΔK p , integral coefficient correction value ΔK i , differential coefficient correction value ΔK d The value of .

[0099] A temperature control method disclosed in this embodiment utilizes the fuzzification of input variables and the defuzzification of output variables, determines fuzzy control rules and establishes fuzzy reasoning relationships, thereby effectively reducing the amount of calculation, will not affect the operating speed of the entire temperature control system, and can meet the response speed requirements of the temperature control system.

[0100] The present application also proposes a self-tuning algorithm based on relay feedback, see Figure 3 , Figure 3 This is a flow chart of another temperature control method disclosed in an embodiment of the present application, including steps 301 to 305.

[0101] 301. Obtain a current temperature value, a target temperature value, a temperature threshold, a sampling period, and a relay output amplitude of a temperature control system at a current moment.

[0102] It should be noted that this embodiment mainly describes a detailed process of a temperature control self-tuning algorithm IRFST. Specifically, it is necessary to first obtain the current temperature value, target temperature value, temperature threshold, sampling period and relay output amplitude of the temperature control system at the current moment. Among them, the relay output amplitude is used to characterize the maximum voltage value output by the temperature control system.

[0103] In one specific embodiment, in the IRFST algorithm, the current temperature value can be defined as P v , the target temperature is S p , the threshold is H, the reference set temperature is H p , the energy supply parameter is A, the environmental parameter is B, the sampling period is T, and the relay output amplitude is d. Among them, H is the temperature threshold. It is not difficult to understand that the current temperature value P in the above v , target temperature value Sp , the sampling period is T, the relay output amplitude is d, and the temperature threshold H can be obtained through real-time collection or pre-setting, and the details will not be repeated here. Furthermore, the energy supply parameter refers to the energy input during the heating process, such as power supply, etc.; the environmental parameter refers to the impact of the heating equipment itself and the environment and other factors on the entire heating system during the heating process; the target temperature value is the temperature value that is expected to be reached by artificial setting; the definition of the reference set temperature is because in the classic relay feedback self-tuning process, it is necessary to heat at full speed to the target temperature before stopping the heating. At this time, due to the continuous rise in inertia temperature, a large overshoot will be generated, which may affect the equipment. The introduction of the reference set temperature can terminate the full-speed heating in advance and reduce the overshoot. For ease of understanding, this will not be repeated in the future.

[0104] 302. Obtain a current temperature difference value according to a current temperature value and a target temperature value; obtain a reference set temperature value according to a temperature threshold, a current temperature difference value, and a current temperature value.

[0105] Furthermore, the current temperature difference value can be obtained according to the current temperature value and the target temperature value; and the reference set temperature value can be obtained according to the temperature threshold, the current temperature difference value and the current temperature value.

[0106] In one specific embodiment, the current temperature difference value can be obtained by combining the current temperature value and the target temperature value. Specifically, the current temperature difference value ΔT can be expressed as ΔT=S p -P v .

[0107] Then, the reference set temperature value can be obtained according to the temperature threshold, the current temperature difference value, and the current temperature value. Specifically, according to the temperature threshold H, the reference set temperature value H p It can be expressed as H p =P v +ΔT*H, where H ranges from 0 to 1.

[0108] 303. When the temperature control system performs full-speed heating at the current temperature value, the first recording time and the second recording time are recorded.

[0109] Furthermore, when the temperature control system performs full-speed heating at the current temperature value, it is necessary to record the first recording time and the second recording time. It should be noted that the first recording time is used to represent the recording time when the temperature value of the temperature control system reaches the target temperature value, and the second recording time is used to represent the recording time when the temperature control system rises to the highest temperature due to inertia.

[0110] In one specific embodiment, the temperature control system is now set from the current temperature P v Start heating at full speed. When the temperature reaches the target temperature S pWhen the heating stops, the time is recorded as T1. At this time, the temperature of the heating system will continue to rise due to inertia and reach the highest temperature, which is recorded as H max , and record the time at this time as T2.

[0111] 304. Based on the energy supply parameter function, the reference set temperature value, the current temperature value, the first recording time, the second recording time and the sampling period are calculated to obtain the energy supply parameters; based on the environmental parameter function, the energy supply parameters, the first recording time and the second recording time are calculated to obtain the environmental parameters.

[0112] Furthermore, when step 303 is completed, the reference set temperature value, the current temperature value, the first recording time, the second recording time and the sampling period can be calculated based on the energy supply parameter function to obtain the energy supply parameter. At the same time, the energy supply parameter, the first recording time and the second recording time can also be calculated based on the environmental parameter function to obtain the environmental parameter.

[0113] In one specific embodiment, the energy supply parameter can be obtained by the energy supply parameter function, and the environmental parameter can be obtained by the environmental parameter function. The energy supply parameter function is f(P v ,H p ,T1,T2,T), that is, A=f(P v ,H p ,T1,T2,T). The environmental parameter function is f(A,T1,T2), that is, B=f(A,T1,T2). The specific function expressions of the energy supply parameter function and the environmental parameter function are not restricted here, and will not be described in detail later.

[0114] 305. Obtain the proportional coefficient value of the control parameter according to the maximum temperature value corresponding to the maximum temperature, the reference set temperature value and the relay output amplitude; obtain the integral coefficient value of the control parameter according to the maximum temperature value, the reference set temperature value, the first recording time, the second recording time, the sampling period, the environmental parameters and the temperature threshold; perform proportional calculation on the integral coefficient value to obtain the differential coefficient value of the control parameter.

[0115] Then, the proportional coefficient value of the control parameter can be obtained according to the maximum temperature value corresponding to the maximum temperature, the reference set temperature value and the relay output amplitude. At the same time, the integral coefficient value of the control parameter can also be obtained according to the maximum temperature value, the reference set temperature value, the first recording time, the second recording time, the sampling period, the environmental parameters and the temperature threshold. At the same time, the integral coefficient value can also be proportionally calculated to obtain the differential coefficient value of the control parameter.

[0116] In one specific embodiment, the proportionality coefficient K obtained by the IRFST algorithm is p0 It can be expressed as

[0117] Integration time coefficient T i0 It can be expressed as

[0118] Differential time coefficient T d0 It can be expressed as T d0 =0.25*T i0 .

[0119] Furthermore, it should be noted that the units of T, T1, and T2 are s; H max , H p The unit is ℃; the relay output amplitude d is the maximum output voltage value, for example 110V~240V, in this embodiment, 220V can be selected.

[0120] A temperature control method disclosed in this embodiment improves the self-tuning process based on the relay feedback self-tuning method, reduces the self-tuning time in the self-tuning process, reduces the impact of the overshoot in the self-tuning process on the temperature control system or other equipment (systems, etc.), reduces the temperature fluctuation in the temperature control process, and improves the feasibility of the solution.

[0121] Combined with the above Figure 3 As shown, at this time, the method for obtaining the initial value of the PID control parameter can be found in Figure 4 , Figure 4 This is a flow chart of another temperature control method disclosed in an embodiment of the present application, including steps 401 and 402.

[0122] 401. Obtain a proportional coefficient value corresponding to a proportional coefficient, an integral coefficient value corresponding to an integral coefficient, and a differential coefficient value corresponding to a differential coefficient in a parameter initial value of a control parameter.

[0123] Combined with the above Figure 3 As shown, after the self-tuning calculation of the IRFST algorithm, the initial value of the PID control parameter at the current moment, that is, the parameter initial value, can be calculated. Specifically, the parameter initial value includes a proportional coefficient value corresponding to the proportional coefficient, an integral coefficient value corresponding to the integral coefficient, and a differential coefficient value corresponding to the differential coefficient.

[0124] In one specific embodiment, the PID control parameters adjusted by IRFST are used as reference values ​​for the FPID algorithm. Specifically, the proportional coefficient value, the integral coefficient value and the differential coefficient value correspond to Figure 3 K shown in step 305 p0 , T i0 and T d0 .

[0125] 402. The proportional coefficient value is used as the initial value of the proportional coefficient corresponding to the proportional coefficient in the control parameter; the proportional coefficient value, the integral coefficient value and the sampling period are calculated to obtain the initial value of the integral coefficient corresponding to the integral coefficient in the control parameter; the proportional coefficient value, the differential coefficient value and the sampling period are calculated to obtain the initial value of the differential coefficient corresponding to the differential coefficient in the control parameter.

[0126] Then, the proportional coefficient value can be used as the initial value of the proportional coefficient corresponding to the proportional coefficient in the control parameter. At the same time, the proportional coefficient value, the integral coefficient value and the sampling period are calculated to obtain the initial value of the integral coefficient corresponding to the integral coefficient in the control parameter. At the same time, the proportional coefficient value, the differential coefficient value and the sampling period can also be calculated to obtain the initial value of the differential coefficient corresponding to the differential coefficient in the control parameter. It should be noted that the initial value of the proportional coefficient, the initial value of the integral coefficient and the initial value of the differential coefficient are the initial values ​​of the parameters.

[0127] In one specific embodiment, the following calculation formula may be referred to.

[0128] K p ′=K p0 ;

[0129]

[0130] Among them, K p ′ is the initial value of the proportional coefficient, K i ′ is the initial value of the integral coefficient, K d ' is the initial value of the differential coefficient. Furthermore, it can be clearly known that K p ′,K i ′ and K d ′ is the initial value of the PID control parameter. The selection of its initial value has a great influence on the control of fuzzy PID.

[0131] By using a temperature control method disclosed in this embodiment, the self-tuned PID control parameters are used as the initial parameters of the fuzzy PID, so that the control parameters can be adaptively controlled according to changes in the temperature control environment, thereby improving the feasibility of the solution.

[0132] Furthermore, combined with the above Figures 1 to 4 , we can get the output value of the fuzzy PID algorithm, please refer to Figure 5 , Figure 5 This is a flow chart of another temperature control method disclosed in an embodiment of the present application, including steps 501 and 502.

[0133] 501. Calculate the proportional coefficient correction value and the proportional coefficient initial value to obtain the proportional coefficient target value of the proportional coefficient; calculate the integral coefficient correction value and the integral coefficient initial value to obtain the proportional integral target value of the integral coefficient; calculate the differential coefficient correction value and the differential coefficient initial value to obtain the differential coefficient target value of the differential coefficient.

[0134] Combined with the above Figure 2 and Figure 4 As shown, the proportional coefficient correction value and the proportional coefficient initial value can be summed to obtain the proportional coefficient target value of the proportional coefficient. At the same time, the integral coefficient correction value and the integral coefficient initial value can be summed to obtain the proportional integral target value of the integral coefficient. At the same time, the differential coefficient correction value and the differential coefficient initial value can be summed to obtain the differential coefficient target value of the differential coefficient.

[0135] In one specific embodiment, after obtaining the proportional coefficient correction value ΔK p , integral coefficient correction value ΔK i , differential coefficient correction value ΔK d After combining the value of Figure 4 The initial value of the parameter is calculated as follows:

[0136] K p =K p ′+{E,E c}K p =K p ′+ΔK p ;

[0137] K p =K i ′+{E,E c}K i =K i ′+ΔK i ;

[0138] K d =K d ′+{E,E c}K d =K d ′+ΔK d .

[0139] It should be noted that {e,e c} is the value in the fuzzy rule table (fuzzy variable, see Figure 1 and Figure 2 The embodiment shown), then through the corresponding E and E c K can be determined p ,K i ,K dIn short, the target value of the proportional coefficient K at this time p , proportional integral target value K i , differential coefficient target value K d They are respectively K obtained in the self-tuning algorithm p ′, K i ′, K d ′ and ΔK obtained by fuzzy algorithm p , ΔK i , ΔK d sum.

[0140] 502. Input the coefficient target value, the error value at the current moment and the error deviation value at the current moment into the PID output function of the fuzzy PID algorithm to obtain the fuzzy controlled temperature value.

[0141] Furthermore, the coefficient target value, the current error value and the current error deviation value can be input into the PID output function of the fuzzy PID algorithm to obtain the fuzzy controlled temperature value.

[0142] In one specific embodiment, the PID function in the FPID algorithm can be used. Specifically, the output PID_OUT(k) of the FPID at the kth moment can be expressed as:

[0143] PID_OUT(k)=K p *E(k)+K i *∑E(k)+K d *E c (k).

[0144] Furthermore, the fuzzy controlled temperature value can be obtained.

[0145] Through a temperature control method disclosed in this embodiment, the PID control parameters adjusted by the relay feedback self-tuning method can be effectively obtained, and a good temperature control effect can be achieved in the PID control process. At the same time, the initial parameters are combined with multiple parameters and are given more accurately, thereby achieving a good temperature control effect.

[0146] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0147] See also Figure 6 , Figure 6 This is a schematic diagram of the structure of a temperature control system disclosed in an embodiment of the present application.

[0148] An acquisition unit, used for periodically acquiring a set temperature value and an actual temperature value of the temperature control system, calculating an error value at a current moment and an error value at a previous moment, and determining an error deviation value at a current moment;

[0149] An output unit, used to fuzzify the current error value and the current error deviation value as input variables of the fuzzy PID algorithm, and output the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm; wherein the control parameter includes the proportional coefficient, integral coefficient and differential coefficient in the fuzzy PID algorithm;

[0150] The acquisition unit is also used to defuzzify the coefficient fuzzy value to obtain the coefficient correction value of the fuzzy PID algorithm;

[0151] The acquisition unit is also used to obtain the coefficient target value of the control parameter according to the coefficient correction value and the initial value of the parameter of the control parameter, and to obtain the fuzzy control temperature value output by the temperature control system at the current moment according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment, so as to perform temperature control; wherein the initial value of the parameter of the control parameter is obtained after calculation by the temperature control self-tuning algorithm.

[0152] Exemplarily, the system further includes: a setting unit;

[0153] A setting unit, used for dividing the error value at the current moment and the error deviation value at the current moment into a plurality of fuzzy sets, and setting a corresponding membership function in each fuzzy set;

[0154] The obtaining unit is specifically used to obtain the membership value of the error value at the current moment and the error deviation value at the current moment in each fuzzy set according to the membership function;

[0155] The acquisition unit is also used to acquire the fuzzy control rule table of the proportional coefficient, integral coefficient and differential coefficient in the control parameters;

[0156] The acquisition unit is also used to combine the fuzzy control rule table to perform parameter inference in the fuzzy PID algorithm on the membership value to obtain the coefficient fuzzy value; wherein the coefficient fuzzy value includes the fuzzy value of the proportional coefficient, the fuzzy value of the integral coefficient and the fuzzy value of the differential coefficient.

[0157] Exemplarily, the system includes:

[0158] The output unit is specifically used to defuzzify the coefficient fuzzy value based on the fuzzy control rule table using the inference and deduction method, and output the coefficient correction value of the fuzzy setting; wherein the inference and deduction method is any one or more of the maximum membership method, the coefficient weighted average method or the center of gravity method.

[0159] Exemplarily, the system further comprises: a recording unit and a computing unit;

[0160] An acquisition unit, specifically used to acquire the current temperature value, target temperature value, temperature threshold, sampling period and relay output amplitude of the temperature control system at the current moment; wherein the relay output amplitude is used to represent the maximum voltage value output by the temperature control system;

[0161] The acquisition unit is also used to obtain the current temperature difference value according to the current temperature value and the target temperature value; and obtain the reference set temperature value according to the temperature threshold, the current temperature difference value and the current temperature value;

[0162] A recording unit, used for recording a first recording time and a second recording time when the temperature control system performs full-speed heating at a current temperature value; wherein the first recording time is used to represent the recording time when the temperature value of the temperature control system reaches the target temperature value, and the second recording time is used to represent the recording time when the temperature control system rises to the highest temperature due to inertia;

[0163] A calculation unit, configured to calculate the reference set temperature value, the current temperature value, the first recording time, the second recording time and the sampling period based on the energy supply parameter function to obtain the energy supply parameter; and to calculate the energy supply parameter, the first recording time and the second recording time based on the environmental parameter function to obtain the environmental parameter;

[0164] The calculation unit is also used to obtain the proportional coefficient value of the control parameter according to the maximum temperature value corresponding to the maximum temperature, the reference set temperature value and the relay output amplitude; obtain the integral coefficient value of the control parameter according to the maximum temperature value, the reference set temperature value, the first recording time, the second recording time, the sampling period, the environmental parameters and the temperature threshold; and perform proportional calculation on the integral coefficient value to obtain the differential coefficient value of the control parameter.

[0165] Exemplarily, the system includes:

[0166] An acquisition unit, specifically used to acquire a proportional coefficient value corresponding to the proportional coefficient, an integral coefficient value corresponding to the integral coefficient, and a differential coefficient value corresponding to the differential coefficient in the parameter initial value of the control parameter;

[0167] The calculation unit is specifically used to use the proportional coefficient value as the initial value of the proportional coefficient corresponding to the proportional coefficient in the control parameter; calculate the proportional coefficient value, the integral coefficient value and the sampling period to obtain the initial value of the integral coefficient corresponding to the integral coefficient in the control parameter; calculate the proportional coefficient value, the differential coefficient value and the sampling period to obtain the initial value of the differential coefficient corresponding to the differential coefficient in the control parameter; wherein the initial value of the proportional coefficient, the initial value of the integral coefficient and the initial value of the differential coefficient are the initial values ​​of the parameters.

[0168] Exemplarily, the system includes:

[0169] The calculation unit is specifically used to calculate the proportional coefficient correction value and the proportional coefficient initial value to obtain the proportional coefficient target value of the proportional coefficient;

[0170] The calculation unit is also used to calculate the integral coefficient correction value and the integral coefficient initial value to obtain the proportional integral target value of the integral coefficient;

[0171] The calculation unit is also used to calculate the differential coefficient correction value and the differential coefficient initial value to obtain the differential coefficient target value of the differential coefficient.

[0172] Exemplarily, the system includes:

[0173] The acquisition unit is specifically used to input the coefficient target value, the current error value and the current error deviation value into the PID output function of the fuzzy PID algorithm to obtain the fuzzy control temperature value; wherein the PID output function is PID_OUT(k)=K p *E(k)+K i *∑E(k)+K d *E c (k), k is used to describe the kth moment, K p It is used to describe the proportional coefficient target value corresponding to the proportional coefficient in the coefficient target value, E(k) is used to describe the error value at the current moment of the kth moment, K i Used to describe the integral coefficient target value corresponding to the integral coefficient in the coefficient target value, K d It is used to describe the differential coefficient target value corresponding to the differential coefficient in the coefficient target value, E c (k) is used to describe the error deviation value at the current moment at the kth moment.

[0174] See below Figure 7 , a schematic diagram of the structure of a temperature control device disclosed in an embodiment of the present application includes:

[0175] CPU 701, memory 705, input / output interface 704, wired or wireless network interface 703 and power supply 702;

[0176] The memory 705 is a temporary storage memory or a permanent storage memory;

[0177] The CPU 701 is configured to communicate with the memory 705 and execute the instructions in the memory 705 to perform the aforementioned Figures 1 to 5 The temperature control method in any of the illustrated embodiments.

[0178] The embodiment of the present application also provides a chip system, the chip system includes at least one processor and a communication interface, the communication interface and the at least one processor are interconnected through a line, and the at least one processor is used to run a computer program or instruction to execute the aforementioned Figure 5 The temperature control method in the illustrated embodiment.

[0179] The embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium includes instructions, when the instructions are executed on a computer, the computer executes the aforementioned Figures 1 to 5 The temperature control method in any of the illustrated embodiments.

[0180] The present application also provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the aforementioned Figures 1 to 5 The temperature control method in any of the illustrated embodiments.

[0181] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0182] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0183] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0185] If the integrated unit 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, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform 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 (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A temperature control method, characterized in that: Applied to a temperature control system, the method comprises: Periodically obtaining the set temperature value and the actual temperature value of the temperature control system, calculating the error value at the current moment and the error value at the previous moment, and determining the error deviation value at the current moment; The current moment error value and the current moment error deviation value are used as input variables of the fuzzy PID algorithm for fuzzification, and the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm is output; Defuzzifying the coefficient fuzzy value to obtain a coefficient correction value of the fuzzy PID algorithm; According to the coefficient correction value and the initial parameter value of the control parameter, the coefficient target value of the control parameter is obtained, and according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment, the fuzzy control temperature value output by the temperature control system at the current moment is obtained to perform temperature control; wherein, the initial parameter value of the control parameter is obtained after calculation by the temperature control self-tuning algorithm.

2. The temperature control method according to claim 1, characterized in that: The method of fuzzifying the error value at the current moment and the error deviation value at the current moment as input variables of the fuzzy PID algorithm and outputting the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm includes: Dividing the current moment error value and the current moment error deviation value into a plurality of fuzzy sets, and setting a corresponding membership function in each fuzzy set; According to the membership function, obtaining the membership values ​​of the current moment error value and the current moment error deviation value in each fuzzy set; Obtaining a fuzzy control rule table of the proportional coefficient, the integral coefficient and the differential coefficient in the control parameters; In combination with the fuzzy control rule table, the membership value is subjected to parameter inference in the fuzzy PID algorithm to obtain the coefficient fuzzy value; wherein the coefficient fuzzy value includes the fuzzy value of the proportional coefficient, the fuzzy value of the integral coefficient and the fuzzy value of the differential coefficient.

3. The temperature control method according to claim 2, characterized in that: The defuzzifying the coefficient fuzzy value to obtain the coefficient correction value of the fuzzy PID algorithm includes: Based on the fuzzy control rule table, the coefficient fuzzy value is defuzzified using an inference and deduction method, and the coefficient correction value of fuzzy setting is output; wherein the inference and deduction method is any one or more of the maximum membership method, the coefficient weighted average method or the centroid method.

4. The temperature control method according to claim 1, characterized in that: The temperature control self-tuning algorithm includes: Obtaining the current temperature value, target temperature value, temperature threshold, sampling period and relay output amplitude of the temperature control system at the current moment; wherein the relay output amplitude is used to represent the maximum voltage value output by the temperature control system; According to the current temperature value and the target temperature value, a current temperature difference value is obtained; according to the temperature threshold value, the current temperature difference value and the current temperature value, a reference set temperature value is obtained; When the temperature control system performs full-speed heating at the current temperature value, a first recording time and a second recording time are recorded; wherein the first recording time is used to represent the recording time when the temperature value of the temperature control system reaches the target temperature value, and the second recording time is used to represent the recording time when the temperature control system rises to the highest temperature due to inertia; Based on the energy supply parameter function, the reference set temperature value, the current temperature value, the first recording time, the second recording time and the sampling period are calculated to obtain energy supply parameters; based on the environmental parameter function, the energy supply parameter, the first recording time and the second recording time are calculated to obtain environmental parameters; According to the maximum temperature value corresponding to the maximum temperature, the reference set temperature value and the relay output amplitude, the proportional coefficient value of the control parameter is obtained; according to the maximum temperature value, the reference set temperature value, the first recording time, the second recording time, the sampling period, the environmental parameters and the temperature threshold, the integral coefficient value of the control parameter is obtained; the integral coefficient value is proportionally calculated to obtain the differential coefficient value of the control parameter.

5. The temperature control method according to claim 4, characterized in that: The calculation method of the initial value of the parameter includes: Obtaining a proportional coefficient value corresponding to a proportional coefficient, an integral coefficient value corresponding to an integral coefficient, and a differential coefficient value corresponding to a differential coefficient in the parameter initial value of the control parameter; The proportional coefficient value is used as the initial value of the proportional coefficient corresponding to the proportional coefficient in the control parameter; the proportional coefficient value, the integral coefficient value and the sampling period are calculated to obtain the initial value of the integral coefficient corresponding to the integral coefficient in the control parameter; the proportional coefficient value, the differential coefficient value and the sampling period are calculated to obtain the initial value of the differential coefficient corresponding to the differential coefficient in the control parameter; wherein the initial value of the proportional coefficient, the initial value of the integral coefficient and the initial value of the differential coefficient are the initial values ​​of the parameters.

6. The temperature control method according to claim 5, characterized in that: The step of obtaining the coefficient target value of the control parameter according to the coefficient correction value and the initial value of the control parameter comprises: Calculating the proportional coefficient correction value and the proportional coefficient initial value to obtain the proportional coefficient target value of the proportional coefficient; Calculating the integral coefficient correction value and the integral coefficient initial value to obtain a proportional integral target value of the integral coefficient; The differential coefficient correction value and the differential coefficient initial value are calculated to obtain the differential coefficient target value of the differential coefficient.

7. The temperature control method according to claim 1, characterized in that: The step of obtaining the fuzzy control temperature value output by the temperature control system at the current moment according to the coefficient target value, the current moment error value and the current moment error deviation value comprises: The coefficient target value, the current error value and the current error deviation value are input into the PID output function of the fuzzy PID algorithm to obtain the fuzzy controlled temperature value; wherein the PID output function is PID_OUT(k)=K p *E(k)+K i *∑E(k)+K d *E c (k), where k is used to describe the kth moment, and K p is used to describe the proportional coefficient target value corresponding to the proportional coefficient in the coefficient target value, the E(k) is used to describe the error value at the current moment of the kth moment, the K i It is used to describe the integral coefficient target value corresponding to the integral coefficient in the coefficient target value, the K d It is used to describe the differential coefficient target value corresponding to the differential coefficient in the coefficient target value, and the E c (k) is used to describe the error deviation value at the current moment of the kth moment.

8. A temperature control system, characterized in that: The system comprises: An acquisition unit, used for periodically acquiring a set temperature value and an actual temperature value of the temperature control system, calculating an error value at a current moment and an error value at a previous moment, and determining an error deviation value at the current moment; An output unit, used to fuzzify the error value at the current moment and the error deviation value at the current moment as input variables of the fuzzy PID algorithm, and output the coefficient fuzzy value of the control parameter in the fuzzy PID algorithm; wherein the control parameter includes the proportional coefficient, integral coefficient and differential coefficient in the fuzzy PID algorithm; The acquisition unit is further used to defuzzify the coefficient fuzzy value to obtain the coefficient correction value of the fuzzy PID algorithm; The acquisition unit is also used to obtain the coefficient target value of the control parameter according to the coefficient correction value and the parameter initial value of the control parameter, and obtain the fuzzy control temperature value output by the temperature control system at the current moment according to the coefficient target value, the error value at the current moment and the error deviation value at the current moment, so as to perform temperature control; wherein the parameter initial value of the control parameter is obtained after calculation by the temperature control self-tuning algorithm.

9. A temperature control device, characterized in that: The device comprises: CPU, memory, input and output interfaces, wired or wireless network interfaces, and power supply; The memory is a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the temperature control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes instructions, and when the instructions are executed on a computer, the computer is caused to execute the temperature control method according to any one of claims 1 to 7.

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