Temperature control method and device, computer equipment and storage medium

Abstract

The invention relates to a temperature control method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring the real-time temperature of a controlled object, determining a temperature deviation value and a temperature deviation change rate according to the real-time temperature and a preset reference temperature, determining a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the real-time temperature and a preset temperature interval, and determining a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the fuzzy control weight coefficient and the proportional-differential-integral control weight coefficient; the preset temperature interval is determined based on a preset reference temperature, a preset dual-mode switching temperature threshold value and a preset temperature buffer area, and the temperature of the controlled object is adjusted based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient and the proportion-differential-integral control weight coefficient. And the temperature deviation value between the real-time temperature and the preset reference temperature is within the preset temperature error range. By adopting the method, the precision of temperature control can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of temperature control, and particularly to a temperature control method, device, computer device, storage medium, and computer program product. Background Art

[0002] With the development of the industrialization process, temperature has become a common and very important physical parameter in the production process and scientific experiments, and has an important impact on key indicators such as the quality of material synthesis, the efficiency of chemical reactions, and the operation safety of equipment.

[0003] In traditional solutions, temperature regulation is usually achieved by relying on a simple mechanical structure. For example, in a common bimetallic thermostat, the principle of different expansion degrees of metal sheets when the temperature changes is used to control the on-off of the circuit of heating or cooling equipment, so as to achieve autonomous temperature control.

[0004] However, due to the limitations of its working principle, the control accuracy of traditional temperature control solutions is often relatively low, and it is easily affected by environmental factors, making it difficult to meet the requirements of high-precision temperature control. That is, traditional solutions have the defect of low temperature control accuracy. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a temperature control method, device, computer device, computer-readable storage medium, and computer program product that can improve the temperature control accuracy.

[0006] In a first aspect, the present application provides a temperature control method. The method includes:

[0007] Obtain the real-time temperature of the controlled object;

[0008] Determine the temperature deviation value and the temperature deviation change rate according to the real-time temperature and the preset reference temperature;

[0009] Determine the fuzzy control weight coefficient and the proportional-integral-derivative control weight coefficient according to the real-time temperature and the preset temperature range, where the preset temperature range is determined based on the preset reference temperature, the preset dual-mode switching temperature threshold, and the preset temperature buffer range;

[0010] Adjust the temperature of the controlled object based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient, and the proportional-integral-derivative control weight coefficient until the temperature deviation value between the real-time temperature and the preset reference temperature is within the preset temperature error range.

[0011] In a second aspect, the present application further provides a temperature control device. The device includes:

[0012] A data acquisition module, configured to acquire the real-time temperature of a controlled object;

[0013] A temperature calculation module, configured to determine a temperature deviation value and a temperature deviation change rate according to the real-time temperature and a preset reference temperature;

[0014] A dual-mode switching module, configured to determine a fuzzy control weight coefficient and a proportional-integral-derivative control weight coefficient according to the real-time temperature and a preset temperature range, where the preset temperature range is determined based on a preset reference temperature, a preset dual-mode switching temperature threshold, and a preset temperature buffer range;

[0015] A temperature control module, configured to adjust the temperature of the controlled object based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient, and the proportional-integral-derivative control weight coefficient until the temperature deviation value of the controlled object is within a preset temperature error range.

[0016] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in the above-mentioned temperature control method embodiment are implemented.

[0017] In a fourth aspect, the present application further provides 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 in the above-mentioned temperature control method embodiment are implemented.

[0018] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned temperature control method embodiment are implemented.

[0019] The above temperature control method, device, computer equipment, storage medium and computer program product are different from traditional solutions. By obtaining the real-time temperature of the controlled object, more accurate and timely temperature data can be provided for subsequent temperature control. Then, according to the real-time temperature of the controlled object and the preset reference temperature, the temperature deviation value and the temperature deviation change rate are determined. The temperature deviation value intuitively reflects the gap between the current temperature of the controlled object and the reference temperature, and the temperature deviation change rate reflects the change speed of the temperature of the controlled object approaching or deviating from the reference temperature, which can provide a more accurate temperature change trend for subsequent temperature control. Then, according to the real-time temperature and the preset temperature range, the fuzzy control weight coefficient and the proportional-integral-derivative control weight coefficient are determined. Among them, the preset temperature range is determined based on the preset reference temperature, the preset dual-mode switching temperature threshold and the preset temperature buffer range. In this way, the temperature control mode applicable to the current temperature of the controlled object can be dynamically adjusted according to the real-time temperature of the controlled object, thereby improving the temperature control accuracy. Finally, according to the temperature deviation value and the temperature deviation change rate, the temperature of the controlled object is continuously adjusted until the temperature deviation value of the controlled object is within the preset temperature error range, effectively improving the temperature control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is an application environment diagram of the temperature control method in an embodiment;

[0021] Figure 2 It is a flowchart of the temperature control method in an embodiment;

[0022] Figure 3 It is a flowchart of the temperature control method in another embodiment;

[0023] Figure 4 It is a schematic diagram of the membership function in an embodiment;

[0024] Figure 5 It is a flowchart of the temperature control method in yet another embodiment;

[0025] Figure 6 It is a dual-mode switching schematic diagram in an embodiment;

[0026] Figure 7 It is a flowchart of the temperature control method in a detailed embodiment;

[0027] Figure 8 It is a schematic diagram of a fuzzy-PID composite controller in an embodiment;

[0028] Figure 9 It is a structural block diagram of the temperature control device in an embodiment;

[0029] Figure 10Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0030] To make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] The temperature control method provided by the embodiments of the present application can be applied to, for example Figure 1 the application environment shown in the figure. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or other network servers.

[0032] Specifically, it can be that an operator sends the real-time temperature of the controlled object to the server 104 through the terminal 102. The real-time temperature of the controlled object can be collected in real time by a collection device such as a temperature sensor. The server 104 determines the temperature deviation value and the temperature deviation change rate according to the real-time temperature and the preset reference temperature. Then, the server 104 determines the fuzzy control weight coefficient and the PID (Proportional-Integral-Derivative) control weight coefficient according to the real-time temperature and the preset temperature range (hereinafter, "Proportional-Integral-Derivative" is abbreviated as "PID"). The preset temperature range is determined based on the preset reference temperature, the preset dual-mode switching temperature threshold and the preset temperature buffer range. Finally, based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient and the PID control weight coefficient, the temperature of the controlled object is adjusted until the temperature deviation value of the controlled object is within the preset temperature error range.

[0033] Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0034] In one embodiment, as Figure 2 shown, a temperature control method is provided. Taking the method applied to Figure 1 the server 104 in the figure as an example, the method includes the following steps:

[0035] S100, obtain the real-time temperature of the controlled object.

[0036] Among them, the controlled object can be a device, apparatus, module, component, etc. that needs to be temperature-regulated, such as a heating element of an industrial device, a heat generation module of a household appliance, etc.

[0037] Specifically, various temperature sensors, such as thermistors, thermocouples, etc., can be used to collect the real-time temperature of the controlled object in real time, convert the temperature of the controlled object into an electrical signal or a digital signal, and send it to the server. Then, the server obtains the real-time temperature of the controlled object. For example, in an industrial processing scenario, the controlled object is a heating module of a certain industrial device, and the temperature sensor collects the temperature of the heating module in real time and uploads the real-time temperature of the heating module to the server, and the server performs subsequent temperature control operations.

[0038] S200. Determine the temperature deviation value and the temperature deviation change rate according to the real-time temperature and the preset reference temperature.

[0039] Among them, the reference temperature is a preset desired temperature value that the controlled object is expected to reach, and it is the goal of the entire temperature control process. The temperature deviation value is the difference between the real-time temperature of the controlled object and the reference temperature, which can be indicated by subtracting the reference temperature from the real-time temperature, and it reflects the gap between the current real-time temperature of the controlled object and the reference temperature. The temperature deviation change rate refers to the change of the temperature deviation value with time, which reflects the speed and trend of the temperature changing towards the target value, and can be obtained by calculating the difference between the temperature deviation values of the controlled object in adjacent time steps.

[0040] Specifically, the server can compare the obtained real-time temperature of the controlled object with the preset reference temperature, calculate the temperature deviation value, and then combine the temperature deviation values of each time step to calculate the temperature deviation change rate, so as to facilitate the subsequent analysis of the temperature dynamic change of the controlled device.

[0041] S300. Determine the fuzzy control weight coefficient and the proportional-integral-derivative (PID) control weight coefficient according to the real-time temperature and the preset temperature range.

[0042] Among them, the fuzzy control weight coefficient is used to measure the proportion of fuzzy control in the final temperature regulation, and the PID control weight coefficient is used to measure the proportion of PID control in the final temperature regulation. The preset temperature range is determined based on the preset reference temperature, the preset dual-mode switching temperature threshold, and the preset temperature buffer range. The preset dual-mode switching temperature threshold is a preset fixed temperature value, and the dual-mode switching temperature threshold includes a positive threshold and a negative threshold , is equal to the sum of the preset reference temperature and the preset temperature buffer range . equal to the preset reference temperature and the preset temperature buffer range When the real-time temperature of the controlled object is between the positive threshold and the negative threshold, the temperature control mode completely changes from fuzzy control to PID control. The preset buffer temperature range refers to a temperature range near the reference temperature. When the temperature deviation value between the real-time temperature of the controlled object and the preset reference temperature is within the range of the preset buffer temperature range, it can be considered that the real-time temperature of the controlled object is very close to the preset reference temperature. The preset dual-mode switching temperature threshold and the preset buffer temperature range are used to determine whether the real-time temperature of the controlled device enters a specific control area, so as to determine whether to switch the control strategy subsequently.

[0043] Specifically, in this embodiment, temperature adjustment can be achieved through the PID (Proportional-Integral-Derivative) control algorithm and the fuzzy control algorithm. Before performing temperature adjustment, it is necessary to first determine the fuzzy control weight coefficient and the PID control weight coefficient. For example, when the real-time temperature of the controlled device is relatively low, the fuzzy control weight coefficient is relatively large, and the PID control weight coefficient is relatively small. Among them, the preset reference temperature, the preset dual-mode switching temperature threshold, and the preset temperature buffer range can divide the temperature axis into multiple temperature ranges. Exemplarily, taking the preset reference temperature as the base point, the control target is to make the temperature of the controlled object continuously approach the preset reference temperature , at this time, the temperature axis can be divided into 5 temperature ranges, which are: , , , , and . Among them, is the preset temperature buffer range, representing the difference between the preset reference temperature and the preset dual-mode switching temperature threshold . The dual-mode switching temperature threshold includes a positive threshold and a negative threshold . is equal to the sum of the preset reference temperature and the preset temperature buffer range . is equal to the difference between the preset reference temperature and the preset temperature buffer range .

[0044] Thus, when the real-time temperature of the controlled object is within different preset temperature ranges, different temperature control strategies can be adopted, that is, the corresponding fuzzy control weight coefficients and PID control weight coefficients are different. For example, the closer the temperature of the controlled object is to the preset dual-mode switching temperature threshold (when the real-time temperature of the controlled object is greater than the preset reference temperature, the closer it is , when the real-time temperature of the controlled object is less than the preset reference temperature, the closer it is ), the larger the PID control weight coefficient and the smaller the fuzzy control weight coefficient. That is, fuzzy control is adopted when the real-time temperature of the controlled object is in the dynamic rising (or falling) stage, and PID control is adopted when the real-time temperature of the controlled object is in the constant temperature steady state (at this time, the real-time temperature fluctuates slightly around the preset reference temperature). By setting the fuzzy control weight coefficient and the PID control weight coefficient between the above two stages, the control method can gradually change from fuzzy control to PID control, reduce the jitter generated during dual-mode switching, and improve the reliability and stability of the temperature control process.

[0045] S400. Based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient, and the proportional-integral-derivative control weight coefficient, adjust the temperature of the controlled object until the temperature deviation value between the real-time temperature and the preset reference temperature is within the preset temperature error range.

[0046] Following the above steps, after determining the temperature deviation value and the temperature deviation change rate, the server can perform temperature adjustment according to a pre-set algorithm, such as the PID control algorithm, the fuzzy control algorithm, etc. Exemplarily, if the temperature deviation value is positive, it means that the real-time temperature of the controlled object is higher than the reference temperature. If the temperature deviation change rate is also positive, it means that the temperature deviation value is still increasing. At this time, the temperature of the controlled device can be reduced by a large margin. If the temperature deviation value is positive, it means that the real-time temperature of the controlled object is higher than the reference temperature. If the temperature deviation change rate is negative, it means that the temperature deviation value is decreasing. At this time, the temperature of the controlled device can be reduced by a small margin. In this embodiment, the corresponding temperature adjustment amounts can be calculated by using the PID control algorithm and the fuzzy control algorithm respectively first, and then combined with the fuzzy control weight coefficient and the PID control weight coefficient to determine the final target temperature adjustment amount. Based on this target temperature adjustment amount, the real-time temperature of the controlled device is adjusted until the temperature deviation value of the controlled object is within the preset temperature error range.

[0047] It should be noted that the above process of adjusting the temperature of the controlled object is continuous. After each adjustment of the temperature of the controlled object, the server will obtain the real-time temperature of the controlled object again and continue to calculate the temperature deviation value and the temperature deviation change rate of the controlled object until the temperature deviation value of the controlled object is within the preset temperature error range.

[0048] The above temperature control method, different from the traditional solutions, can provide more accurate and timely temperature data for subsequent temperature control by obtaining the real-time temperature of the controlled object. Then, based on the real-time temperature of the controlled object and the preset reference temperature, the temperature deviation value and the temperature deviation change rate are determined. The temperature deviation value intuitively reflects the gap between the current temperature of the controlled object and the reference temperature, and the temperature deviation change rate reflects the changing speed at which the temperature of the controlled object approaches or deviates from the reference temperature, which can provide a more accurate temperature change trend for subsequent temperature control. Then, based on the real-time temperature and the preset temperature range, the fuzzy control weight coefficient and the PID control weight coefficient are determined, where the preset temperature range is determined based on the preset reference temperature, the preset dual-mode switching temperature threshold, and the preset temperature buffer range. In this way, the temperature control mode applicable to the current temperature of the controlled object can be dynamically adjusted according to the real-time temperature of the controlled object, thereby improving the temperature control accuracy. Finally, based on the temperature deviation value and the temperature deviation change rate, the temperature of the controlled object is continuously adjusted until the temperature deviation value of the controlled object is within the preset temperature error range, effectively improving the temperature control accuracy.

[0049] In one embodiment, as Figure 3 shown, S400 includes:

[0050] S410, determining the proportional-integral-derivative (PID) control temperature adjustment amount according to the temperature deviation value.

[0051] S420, determining the fuzzy control temperature adjustment amount according to the temperature deviation value and the temperature deviation change rate.

[0052] S430, determining the target temperature adjustment amount based on the fuzzy control weight coefficient, the proportional-integral-derivative (PID) control weight coefficient, the fuzzy control temperature adjustment amount, and the PID control temperature adjustment amount.

[0053] S440, adjusting the temperature of the controlled object based on the target temperature adjustment amount until the temperature deviation value between the real-time temperature and the preset reference temperature is within the preset temperature error range.

[0054] Among them, the PID control temperature adjustment amount refers to the temperature adjustment amount calculated by the PID control algorithm, and the fuzzy control temperature adjustment amount refers to the temperature adjustment amount calculated by the fuzzy control algorithm.

[0055] The PID control algorithm can be implemented by a PID controller, where the PID controller is a feedback controller that can be used in industrial control processes. It can calculate the control signal based on the deviation between the given value and the actual value to adjust the output of the system, enabling the system to reach the desired state stably, quickly, and accurately. The fuzzy control algorithm is based on fuzzy mathematics theory and mimics the fuzzy reasoning and decision-making process of humans to control complex systems that are difficult to establish precise mathematical models, especially suitable for systems with high nonlinearity, time-variation, and uncertainty.

[0056] In this embodiment, the PID controller can be composed of three parts: proportional (P), integral (I), and derivative (D). The value input to the PID controller is the temperature deviation value. The three parts of proportional (P), integral (I), and derivative (D) process the temperature deviation value differently, and then add their results to obtain the final control signal, thereby determining the PID control temperature adjustment amount to adjust the temperature of the controlled object. The inputs of the fuzzy control algorithm are the temperature deviation value and the rate of change of the temperature deviation, and the output is the fuzzy control temperature adjustment amount. It can first perform fuzzy processing on the temperature deviation value and the rate of change of the temperature deviation based on the preset membership function, and then perform reasoning and defuzzification in combination with the preset fuzzy rules to finally output the fuzzy control temperature adjustment amount to adjust the temperature of the controlled object.

[0057] Further, determine the PID control temperature adjustment amount and the fuzzy control temperature adjustment amount After that, it is also necessary to combine the fuzzy control weight coefficient and the PID control weight coefficient to determine the target temperature adjustment amount . For example, the target temperature adjustment amount can be calculated based on Equation (1):

[0058] (1)

[0059] where the sum of the fuzzy control weight coefficient and the PID control weight coefficient is 1, and Equation (1) can be simplified to Equation (2):

[0060] (2)

[0061] Finally, apply the calculated target temperature adjustment amount to the controlled object, such as adjusting the power of the heating device, the cooling capacity of the refrigeration device, etc., to cause the temperature of the controlled object to change, and then return to the step of obtaining the real-time temperature of the controlled object, and continuously repeat the above process until the real-time temperature of the controlled object is equal to the preset reference temperature.

[0062] In this embodiment, the PID control algorithm is combined with the fuzzy control algorithm. The fuzzy control algorithm has good adaptability and robustness and can handle complex and uncertain systems. The PID control algorithm has high control precision and stability. By dynamically adjusting the weight coefficients of the two according to different temperature states, the advantages of the two control methods can be fully utilized, and the precision of the temperature control process is improved.

[0063] In one embodiment, S410 includes: determining a proportional control quantity according to the temperature deviation value and a preset proportional control coefficient, determining an integral control quantity according to the temperature deviation value and a preset integral control coefficient, determining a derivative control quantity according to the temperature deviation value and a preset derivative control coefficient, and determining a proportional-integral-derivative control temperature adjustment quantity based on the proportional control quantity, the integral control quantity, and the derivative control quantity.

[0064] Among them, the PID control algorithm includes proportional control, integral control, and derivative control. Proportional control can immediately generate a control action proportional to the magnitude of the temperature deviation when the temperature of the controlled object deviates. The larger the proportional control coefficient, the stronger the control action, and correspondingly the larger the proportional control quantity, and the faster the temperature change rate of the controlled object. The main function of integral control is to reduce the steady-state error. When there is a temperature deviation value in the controlled object, the integral control will continuously accumulate the temperature deviation value and generate a continuous control action, thereby determining the integral control quantity until the temperature deviation value is zero. The larger the integral control coefficient, the stronger the control action, and correspondingly the larger the integral control quantity, and the faster the temperature change rate of the controlled object. Derivative control is mainly used to predict the temperature change trend of the controlled object, so as to control the temperature of the controlled object in advance. Differentiating the temperature deviation value with respect to time to obtain the temperature deviation change rate, and the derivative control generates a control action according to the temperature deviation change rate, thereby determining the derivative control quantity. The larger the derivative control coefficient, the stronger the control action, and correspondingly the larger the derivative control quantity, and the faster the temperature change rate of the controlled object.

[0065] Exemplarily, the specific calculation formula for the PID control temperature adjustment quantity finally output based on the PID control algorithm is shown in Equation (3):

[0066] (3)

[0067] In Equation (3), is the PID control temperature adjustment quantity, is the proportional control coefficient, is the integral control coefficient, is the derivative control coefficient, is the real-time temperature deviation value of the controlled object.

[0068] In this embodiment, the PID control algorithm is used for temperature control, which can adjust the temperature of the controlled object in real time according to the temperature deviation value of the controlled object. Through the combined action of proportional, integral, and differential, the control quantity is adjusted in a timely manner, enabling the controlled object to be adjusted to and maintained at the reference temperature as quickly as possible, thereby improving the accuracy and efficiency of temperature control.

[0069] In one embodiment, S420 includes: performing fuzzy processing on the temperature deviation value and the temperature deviation change rate according to a preset membership function to obtain fuzzy input quantities, performing fuzzy inference on the fuzzy input quantities based on preset fuzzy rules to obtain a fuzzy set, and performing defuzzification on the fuzzy set to determine the fuzzy control temperature adjustment quantity.

[0070] Among them, the fuzzy control algorithm can handle the control problems of complex systems in the form of fuzzy rules. The control accuracy of the fuzzy control algorithm is positively correlated with its own dimension. The more dimensions of the fuzzy control, the higher the control accuracy. Correspondingly, the complexity of the fuzzy rules and the control calculation amount will also increase.

[0071] In this embodiment, the temperature deviation value and the temperature deviation change rate of the controlled object can be used as the input variables of the fuzzy control algorithm, and the output variable of the fuzzy control algorithm is the fuzzy control temperature adjustment quantity. Then, it is necessary to determine the universes of discourse and scale factors of the input and output variables. The universe of discourse is the value range, and the scale factor is used to map the actual input and output values to the range that the fuzzy control algorithm can handle, thereby converting the actual physical quantity into a value suitable for fuzzy operations.

[0072] Furthermore, determine the number of fuzzy linguistic variables and the membership function. Fuzzy linguistic variables are words used for fuzzy description. For example, in this embodiment, 7 fuzzy linguistic variables can be set, namely {NB (Negative Big), NM (Negative Medium), NS (Negative Small), ZO (Zero Point), PS (Positive Small), PM (Positive Medium), PB (Positive Big)}. NB (Negative Big) indicates that the variable is in a relatively large negative value range, meaning that the attribute or state of the object being described reaches a relatively large degree in the negative direction. NM (Negative Medium) represents that the value of the variable is at a medium level in the negative direction, and its absolute value is smaller than the range represented by NB. NS (Negative Small): It shows that the variable takes a small value in the negative direction, and its absolute value is smaller than NM. ZO (Zero Point): It means that the variable is close to or at the zero position, which is the transition point between positive and negative values. PS (Positive Small): Opposite to NS, it is that the variable takes a small value in the positive direction. PM (Positive Medium): It means that the variable reaches a medium level in the positive direction, and its value is greater than PS and less than PB. PB (Positive Big): It indicates that the variable is in a relatively large value range in the positive direction, which is a description of a relatively high degree in the positive direction.

[0073] Among them, the curve forms of membership functions are diverse, including but not limited to triangular, trapezoidal, Gaussian distribution type, S-shaped, etc. Considering that the triangular membership function has a simple expression, is easy to calculate, has good anti-interference performance, and has strong control ability in the steady-state region, for the sake of simplifying the calculation, the membership functions of input variables and output variables can both adopt triangular membership functions, such as Figure 4 shown. In the figure, the x-axis represents the input variable or output variable, and the Ux-axis represents the membership degree of the input variable or output variable. Based on the membership function, the input variable can be mapped into a pre-set fuzzy language variable, so as to realize the fuzzification process of the input variable and obtain a fuzzy input quantity.

[0074] Then, the expert experience can be expressed as language rules in advance to form fuzzy rules. The expert experience is summarized by the expert according to his actual operation and control process, analyzing the appropriate output variables corresponding to different input variables, and converting these experiences into specific language rules, which are pre-stored in the fuzzy rule base corresponding to the fuzzy control algorithm. For example, the fuzzy rule can be "If the temperature deviation value is positive large and the temperature deviation change rate is positive small, then the fuzzy control temperature adjustment amount is positive large". Finally, combining the above fuzzy rules, the fuzzy input quantity obtained through the membership function processing can be subjected to fuzzy reasoning to obtain a fuzzy set, and then the fuzzy set is defuzzified to convert the fuzzy set into an accurate control quantity, so as to obtain the fuzzy control temperature adjustment amount.

[0075] Exemplarily, in this embodiment, the specific fuzzification and defuzzification formulas are shown in Equation (4):

[0076] (4)

[0077] In Equation (4), is a scale factor, which is used to map the actual input and output values to the range of the domain that the fuzzy control algorithm can process, so as to convert the actual physical quantity into a value suitable for fuzzy operation. The specific fuzzy rules are shown in Table 1 below. In the table, E represents the fuzzified temperature deviation value, EC represents the fuzzified temperature deviation change rate, and U represents the output fuzzy set, which can be defuzzified to determine the fuzzy control temperature adjustment amount.

[0078] Table 1 Fuzzy Rule Table

[0079]

[0080] In this embodiment, the fuzzy control algorithm can quickly respond according to information such as temperature deviation and deviation change rate, timely adjust the control quantity, so that the controlled object can quickly tend to the reference temperature. And because the fuzzy control algorithm is applicable to processing temperature control scenarios with nonlinearity, time-variation and uncertainty, by adjusting the temperature of the controlled object through the fuzzy control algorithm, better temperature control performance can be achieved and the temperature control accuracy can be improved.

[0081] In one embodiment, as Figure 5 shown, S300 includes:

[0082] S310, comparing the real-time temperature with the preset temperature range to determine the temperature range where the real-time temperature is located.

[0083] S320, determining the fuzzy control weight coefficient and the proportional-integral-derivative (PID) control weight coefficient according to the temperature range where the real-time temperature is located.

[0084] Among them, the closer the real-time temperature of the controlled object is to the preset dual-mode switching temperature threshold, the larger the PID control weight coefficient. Exemplarily, the known relationship between the fuzzy control weight coefficient and the PID control weight coefficient is:

[0085] (5)

[0086] Referring to the dual-mode switching diagram shown in Figure 6 and based on the preset reference temperature, the preset dual-mode switching temperature threshold and the preset buffer temperature range, with the preset reference temperature as the base point, the control objective is to make the temperature of the controlled object continuously approach the preset reference temperature . At this time, the temperature axis can be divided into 5 temperature ranges, which are respectively: , , , and . Among them, is the preset temperature buffer range, representing the difference between the preset reference temperature and the preset dual-mode switching temperature threshold , is equal to the sum of the preset reference temperature and the preset temperature buffer range , is equal to the difference between the preset reference temperature and the preset temperature buffer range , which can be expressed by Equation (6):

[0087] (6)

[0088] When the real-time temperature of the controlled object In the temperature range or , the fuzzy control weight coefficient and the PID control weight coefficient are respectively:

[0089] (7)

[0090] At this time, there is still a large deviation between the temperature of the controlled object and the preset reference temperature. The temperature of the controlled object should be in the dynamic rising or falling stage. Since the fuzzy control has the characteristics of fast dynamic response speed and strong anti-interference ability, adopting the fuzzy control algorithm to adjust the temperature of the controlled object at this time can make the real-time temperature of the controlled object approach the preset reference temperature at a faster speed, thereby reducing the temperature adjustment time.

[0091] When the real-time temperature of the controlled object is in the temperature range , according to the similar triangle relationship in Figure 6 , the fuzzy control weight coefficient and the PID control weight coefficient are respectively:

[0092] (8)

[0093] When the real-time temperature of the controlled object is in the temperature range , according to the similar triangle relationship in Figure 6 , the fuzzy control weight coefficient and the PID control weight coefficient are respectively:

[0094] (9)

[0095] At this time, the real-time temperature of the controlled object has gradually approached the preset reference temperature, but there is still a certain deviation. In these two temperature ranges, fuzzy control and PID control can be used simultaneously to adjust the temperature. According to equations (8) and (9), it can be seen that the fuzzy control weight coefficient gradually decreases as the real-time temperature of the controlled object approaches the preset reference temperature, while the PID control weight coefficient gradually increases, indicating that the temperature control mode gradually switches from fuzzy control to PID control. Compared with simply using threshold switching or fuzzy switching, the gradual switching method in this embodiment can effectively reduce the jitter caused by threshold switching and simultaneously reduce the complexity of fuzzy switching. This is because if threshold switching is used, due to the possible differences in the outputs of the two control modes, the dual-mode switching moment may cause severe jitter, affecting the stability of temperature control. And if fuzzy switching is used, it will introduce a complex calculation process and reduce the temperature control efficiency. The dual-mode switching method in this embodiment combines the advantages of threshold switching and fuzzy switching. Before the dual-mode switching threshold, weight coefficients are assigned to the two control modes, enabling the temperature control mode to gradually change from fuzzy control to PID control, effectively reducing the system jitter at the dual-mode switching moment, improving the reliability and stability of the temperature control process. Moreover, combining the idea of similar triangles, the formulas (8) and (9) for assigning weight coefficients to the dual-mode are very simple, effectively reducing the complexity of fuzzy control and improving the temperature control efficiency.

[0096] When the real-time temperature of the controlled object is in the temperature range , the fuzzy control weight coefficient and the PID control weight coefficient are respectively:

[0097] (10)

[0098] At this time, the real-time temperature of the controlled object is very close to the preset reference temperature. At this time, the controlled object is in a steady state. If fuzzy control is still used, it may cause large fluctuations in the real-time temperature of the controlled object, which is instead not conducive to temperature control. Therefore, the fuzzy control weight coefficient is set to 0 at this time, and the PID control weight coefficient is set to 1. The PID control algorithm can slightly adjust the temperature of the controlled object to approach the preset reference temperature within a narrow temperature range, which is suitable for the steady state stage of temperature control, thereby improving the temperature control accuracy and the stability of the temperature control process.

[0099] Combined with the appendix Figure 6 It can be seen that the closer the real-time temperature of the controlled object is to the dual-mode temperature switching threshold , the larger the proportion of the PID control algorithm, that is, the closer the PID control weight coefficient is to 1, until the real-time temperature of the controlled object just reaches the dual-mode temperature switching threshold When the temperature control mode is successfully switched, the PID control weight coefficient becomes 1 and the fuzzy control coefficient becomes 0 thereafter.

[0100] In this embodiment, the dual-mode switching method adopted is to start allocating weights to the outputs of the two modes (fuzzy control and PID control) before the switching threshold, so that the temperature control process gradually switches from fuzzy control to PID control. Fuzzy control has a fast dynamic response speed and strong anti-interference ability, which can quickly approximate the real-time temperature of the controlled object to the preset reference temperature. When the temperature deviation value is small, PID control has the characteristic of zero steady-state error. Using PID control can slightly adjust the real-time temperature of the controlled object to obtain better steady-state performance. In this way, the advantages of the fuzzy control algorithm and the PID control algorithm can be combined to improve the temperature control effect. Moreover, in this embodiment, the dual-mode switching method of gradually switching from fuzzy control to PID control can not only reduce the system jitter in the temperature control process, but also reduce the computational complexity and the amount of calculation. In this way, it can reduce the defects of complex fuzzy switching structure and large amount of calculation, and at the same time has the simplicity of threshold switching, making the final temperature control show the "dual-mode gradual change" characteristic, alleviating the chattering problem of threshold switching, and having the advantages of both threshold switching and fuzzy switching, which can effectively improve the accuracy, reliability and stability of temperature control.

[0101] To provide a clearer explanation of the temperature control method provided in this application, the following combines Figure 7 and One a detailed embodiment for illustration. The detailed embodiment includes the following steps:

[0102] S710, Obtain the real-time temperature of the controlled object, and determine the temperature deviation value and the temperature deviation change rate according to the real-time temperature and the preset reference temperature.

[0103] S720, Compare the real-time temperature with the preset temperature range to determine the temperature range where the real-time temperature is located.

[0104] S730, Determine the fuzzy control weight coefficient and the proportional-integral-derivative control weight coefficient according to the temperature range where the real-time temperature is located.

[0105] S740, Determine the proportional control amount, the integral control amount and the derivative control amount according to the temperature deviation value, the preset proportional control coefficient, the preset integral control coefficient and the preset derivative control coefficient, and determine the proportional-integral-derivative control temperature adjustment amount based on the proportional control amount, the integral control amount and the derivative control amount.

[0106] S750, perform fuzzy processing on the temperature deviation value and the rate of change of temperature deviation according to a preset membership function to obtain fuzzy input quantities, perform fuzzy inference on the fuzzy input quantities based on preset fuzzy rules to obtain a fuzzy set, and defuzzify the fuzzy set to determine the fuzzy control temperature adjustment quantity.

[0107] S760, determine the target temperature adjustment quantity based on the fuzzy control weight coefficient, the proportional-integral-derivative control weight coefficient, the fuzzy control temperature adjustment quantity, and the proportional-integral-derivative control temperature adjustment quantity.

[0108] S770, adjust the temperature of the controlled object based on the target temperature adjustment quantity until the temperature deviation value between the real-time temperature and the preset reference temperature is within the preset temperature error range.

[0109] It should be noted that this detailed embodiment can be applied to a fuzzy-PID composite controller as Figure 7 shown, Figure 8 where is the preset reference temperature, E is the temperature deviation value of the controlled object, Ed is the rate of change of temperature deviation of the controlled object, is the fuzzy control temperature adjustment quantity, is the PID control temperature adjustment quantity, is the target temperature adjustment quantity, is the real-time temperature of the controlled object.

[0110] Among them, E and Ed can be calculated based on the following formula:

[0111] (11)

[0112] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0113] Based on the same inventive concept, an embodiment of the present application further provides a temperature control device for implementing the temperature control method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the temperature control device provided below can refer to the limitations on the temperature control method in the above text, and will not be repeated here.

[0114] In one embodiment, as Figure 9 shown, a temperature control device 900 is provided, including: a data acquisition module 910, a temperature calculation module 920, and a temperature control module 930, where:

[0115] The data acquisition module 910 is configured to acquire the real-time temperature of the controlled object.

[0116] The temperature calculation module 920 is configured to determine a temperature deviation value and a temperature deviation change rate according to the real-time temperature and a preset reference temperature.

[0117] The dual-mode switching module 930 is configured to determine a fuzzy control weight coefficient and a proportional-integral-derivative control weight coefficient according to the real-time temperature and a preset temperature range, and the preset temperature range is determined based on a preset reference temperature, a preset dual-mode switching temperature threshold, and a preset temperature buffer range.

[0118] The temperature control module 940 is configured to adjust the temperature of the controlled object based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient, and the proportional-integral-derivative control weight coefficient until the temperature deviation value between the real-time temperature and the preset reference temperature is within a preset temperature error range.

[0119] In one embodiment, the temperature control module 940 is configured to determine a proportional-integral-derivative control temperature adjustment amount according to the temperature deviation value, determine a fuzzy control temperature adjustment amount according to the temperature deviation value and the temperature deviation change rate, determine a target temperature adjustment amount based on the fuzzy control weight coefficient, the proportional-integral-derivative control weight coefficient, the fuzzy control temperature adjustment amount, and the proportional-integral-derivative control temperature adjustment amount, and adjust the temperature of the controlled object based on the target temperature adjustment amount until the temperature deviation value of the controlled object is within a preset temperature error range.

[0120] In one embodiment, the temperature control module 940 is further configured to determine a proportional control amount according to the temperature deviation value and a preset proportional control coefficient, determine an integral control amount according to the temperature deviation value and a preset integral control coefficient, determine a derivative control amount according to the temperature deviation value and a preset derivative control coefficient, and determine a proportional-integral-derivative control temperature adjustment amount based on the proportional control amount, the integral control amount, and the derivative control amount.

[0121] In one embodiment, the temperature control module 940 is further configured to perform fuzzy processing on the temperature deviation value and the temperature deviation change rate according to a preset membership function to obtain fuzzy input quantities, perform fuzzy inference on the fuzzy input quantities based on preset fuzzy rules to obtain a fuzzy set, and perform defuzzification on the fuzzy set to determine a fuzzy control temperature adjustment quantity.

[0122] In one embodiment, the dual-mode switching module 930 is configured to compare the real-time temperature with a preset temperature range to determine the temperature range where the real-time temperature is located, and determine a fuzzy control weight coefficient and a proportional-integral-derivative control weight coefficient according to the temperature range where the real-time temperature is located. Among them, the closer the real-time temperature of the controlled object is to the preset dual-mode switching temperature threshold, the larger the proportional-integral-derivative control weight coefficient.

[0123] Each module in the above temperature control device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0124] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 10 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the real-time temperature of the controlled object. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a temperature control method.

[0125] Those skilled in the art can understand that Figure 10 the structure shown in

[0126] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above-mentioned embodiment of the temperature control method are implemented.

[0127] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned embodiment of the temperature control method are implemented.

[0128] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above-mentioned embodiment of the temperature control method are implemented.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. And the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0131] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0132] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A temperature control method, characterized in that: The method comprises: Get the real-time temperature of the controlled object; Determining a temperature deviation value and a temperature deviation change rate according to the real-time temperature and a preset reference temperature; Determine a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the real-time temperature and a preset temperature interval, wherein the preset temperature interval is determined based on a preset reference temperature, a preset dual-mode switching temperature threshold, and a preset temperature buffer zone; Based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient and the proportional-differential-integral control weight coefficient, the temperature of the controlled object is adjusted until the temperature deviation value between the real-time temperature and the preset reference temperature is within a preset temperature error range.

2. The method according to claim 1, characterized in that The step of determining the fuzzy control weight coefficient and the proportional-differential-integral control weight coefficient according to the real-time temperature and the preset temperature range includes: Comparing the real-time temperature with the preset temperature range to determine the temperature range in which the real-time temperature is located; Determining a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the temperature range in which the real-time temperature is located; Among them, the closer the real-time temperature of the controlled object is to the preset dual-mode switching temperature threshold, the larger the proportional-differential-integral control weight coefficient is, and the smaller the fuzzy control weight coefficient is.

3. The method according to claim 1 or 2, characterized in that: The step of adjusting the temperature of the controlled object based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient, and the proportional-differential-integral control weight coefficient until the temperature deviation value of the controlled object is within a preset temperature error range includes: Determining a proportional-differential-integral control temperature adjustment amount according to the temperature deviation value; Determining a fuzzy control temperature adjustment amount according to the temperature deviation value and the temperature deviation change rate; Determining a target temperature adjustment amount based on the fuzzy control weight coefficient, the proportional-differential-integral control weight coefficient, the fuzzy control temperature adjustment amount, and the proportional-differential-integral control temperature adjustment amount; Based on the target temperature adjustment amount, the temperature of the controlled object is adjusted until the temperature deviation value of the controlled object is within a preset temperature error range.

4. The method according to claim 3, characterized in that Determining the proportional-differential-integral control temperature adjustment amount according to the temperature deviation value includes: Determining a proportional control amount according to the temperature deviation value and a preset proportional control coefficient; Determining an integral control amount according to the temperature deviation value and a preset integral control coefficient; Determining a differential control amount according to the temperature deviation value and a preset differential control coefficient; Based on the proportional control amount, the integral control amount, and the differential control amount, a proportional-differential-integral control temperature adjustment amount is determined.

5. The method according to claim 3, characterized in that: Determining the fuzzy control temperature adjustment amount according to the temperature deviation value and the temperature deviation change rate includes: According to a preset membership function, the temperature deviation value and the temperature deviation change rate are fuzzy processed to obtain a fuzzy input quantity; Based on preset fuzzy rules, fuzzy reasoning is performed on the fuzzy input quantity to obtain a fuzzy set; The fuzzy set is defuzzified to determine the fuzzy control temperature adjustment amount.

6. A temperature control device, characterized in that: The device comprises: A data acquisition module is used to obtain the real-time temperature of the controlled object; A temperature calculation module, used to determine a temperature deviation value and a temperature deviation change rate according to the real-time temperature and a preset reference temperature; A dual-mode switching module, used to determine a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the real-time temperature and a preset temperature interval, wherein the preset temperature interval is determined based on a preset reference temperature, a preset dual-mode switching temperature threshold, and a preset temperature buffer zone; The temperature control module is used to adjust the temperature of the controlled object based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient and the proportional-differential-integral control weight coefficient until the temperature deviation value of the controlled object is within a preset temperature error range.

7. The device according to claim 6, characterized in that The temperature control module is used to determine a proportional-differential-integral control temperature adjustment amount according to the temperature deviation value, determine a fuzzy control temperature adjustment amount according to the temperature deviation value and the temperature deviation change rate, determine a target temperature adjustment amount based on the fuzzy control weight coefficient, the proportional-differential-integral control weight coefficient, the fuzzy control temperature adjustment amount and the proportional-differential-integral control temperature adjustment amount, and adjust the temperature of the controlled object based on the target temperature adjustment amount until the temperature deviation value of the controlled object is within a preset temperature error range.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images