Automatic valve regulation and control method and equipment for heat supply pipeline and medium

Abstract

The invention discloses an automatic regulation and control method and device for a valve of a heat supply pipeline and a medium, and relates to the field of automatic control over a heat supply system.The method comprises the steps that the opening value of the valve of a target heat supply pipeline is obtained in real time, and according to the opening value, the heat difference and the heat difference change rate of the target heat supply pipeline are predicted through a fitted polynomial regression model; converting the predicted heat difference and the predicted heat difference change rate into corresponding membership values through a membership function, and obtaining a total membership value of a preset fuzzy set; performing matching in a fuzzy rule base based on the membership value, and determining a fuzzy value; performing defuzzification on the fuzzy value according to the total contribution and the total membership value of a preset fuzzy set to obtain a specific value of the valve adjustment amount; and adjusting the control parameters of the automatic controller based on the specific value. And by continuously optimizing automatic control parameters, the steady-state error of the system is reduced, the control precision of the system is improved, and it is ensured that the heat supply effect is more stable.

Description

Technical Field

[0001] This application relates to the technical field of automatic control of heating systems, and specifically relates to a method, device, and medium for automatically regulating the valves of heating pipelines. Background Art

[0002] With the rapid development of the urban informatization process, the scale of heating systems is also continuously expanding. In a heating pipe network, valves are mainly used to regulate the flow rate and temperature of hot water or heat medium to ensure the stable operation of the heating system under different load conditions. With the change of heating demands, such as the demand differences in different regions and the load fluctuations at different time periods, the demand for intelligent control of heating pipe networks is increasing day by day. Therefore, the performance requirements for valves are also continuously increasing.

[0003] To meet the various performance requirements of valves, many manufacturers produce various types of valves. However, there are differences in the manufacturing processes and quality controls of valves produced by different manufacturers, resulting in a deviation between the actual opening degree and the theoretical value of the valves, thus leading to a mismatch in the opening degree. Moreover, the valves in the heating pipeline system are in a high-temperature and high-pressure environment for a long time, which may cause deformation or aging of the valve materials, thereby affecting the stability of its opening degree control. As a result, a large amount of manpower still needs to be invested at the end of automatic regulation. When traditional automatic control faces a complex heating pipe network system, it may be difficult to adapt to different working conditions due to fixed parameters. Summary of the Invention

[0004] To solve the above problems, this application proposes a method for automatically regulating the valves of heating pipelines, including:

[0005] Obtain the opening degree value of the valve of the target heating pipeline in real time, and based on the opening degree value, predict the heat difference and the heat difference change rate of the target heating pipeline through a fitted polynomial regression model;

[0006] Convert the predicted heat difference and the predicted heat difference change rate into membership degree values corresponding to each preset fuzzy set through the membership functions corresponding to each preset fuzzy set, and obtain the total membership degree value of the set of preset fuzzy sets;

[0007] Match based on the membership degree values in the fuzzy rule base to determine the fuzzy value of the valve adjustment amount corresponding to the target heating pipeline;

[0008] Defuzzify the fuzzy value according to the total contribution of the set of preset fuzzy sets and the total membership degree value to obtain the specific value of the valve adjustment amount; the total contribution is the sum of the contribution values of each preset fuzzy set to the defuzzification result;

[0009] Adjust the control parameters of the automatic controller based on the specific value.

[0010] On the other hand, the present application also proposes a valve automatic control device for a heating pipeline, including:

[0011] At least one processor; and,

[0012] A memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a valve automatic control method for a heating pipeline as described in the above example.

[0014] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set as: a valve automatic control method for a heating pipeline as described in the above example.

[0015] The valve automatic control method for a heating pipeline proposed by the present application can bring the following beneficial effects:

[0016] By continuously optimizing the automatic control parameters, the steady-state error of the system is reduced, the control accuracy of the system is improved, and the heating effect is ensured to be more stable. Fuzzy control can handle uncertainty and nonlinear problems and has better adaptability to various disturbances and changes that may occur in the heating pipe network system. Real-time adjustment of the automatic control parameters can avoid excessive overshoot or oscillation of the system, make the response of the system smoother, and enhance the stability of the system. The automatic control of the valve is realized, manual intervention is reduced, and the working intensity of the operator is lowered. Automatic control can improve the reliability and consistency of the system and reduce the influence of human factors on the operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0018] Figure 1 is a schematic flow chart of a valve automatic control method for a heating pipeline in an embodiment of the present application;

[0019] Figure 2 is a schematic flow chart of the specific process of automatic control in an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of a valve automatic control device for a heating pipeline in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments of this application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0022] The following will detail the technical solutions provided by each embodiment of this application in conjunction with the drawings.

[0023] As Figure 1 shown, an automatic regulation method for the valve of a heating pipeline provided by an embodiment of this application includes:

[0024] S101: Obtain the opening value of the valve of the target heating pipeline in real time, and based on the opening value, predict the heat difference and the rate of change of the heat difference of the target heating pipeline through the fitted polynomial regression model.

[0025] Specifically, determine the target heating pipeline that needs valve control, monitor the valve of the target heating pipeline in real time, obtain the opening value of the valve in the current target heating pipeline, input the current opening value of the valve into the polynomial regression model that has been fitted and trained, and use the polynomial regression model to predict the heat difference and the rate of change of the heat difference of the target heating pipeline for a period of time in the future, to obtain the predicted heat difference and the predicted rate of change of the heat difference, so as to determine the valve adjustment amount in the future based on the predicted heat difference and the predicted heat difference.

[0026] Among them, before automatically controlling the target heating pipeline, as Figure 2 shown, it is also necessary to initialize the parameters of the automatic controller corresponding to the target heating pipeline in advance, so as to adjust the valve of the target heating pipeline through the initialized automatic controller.

[0027] Specifically, obtain the historical actual heat of the target heating pipeline corresponding to the historical preset collection time, determine the historical heat difference corresponding to the historical preset collection time based on the historical actual heat, initialize the control parameters in the automatic controller based on the historical heat difference and the preset rules, and perform initial control on the valve in the target heating pipeline through the initialized automatic controller.

[0028] Among them, the control parameters in the automatic controller include the integral parameter (k i ), the derivative parameter (k d ), and the proportional parameter (k p ), and automatic control is achieved according to Formula 1: and Formula 2: e(t) = r(t) - y(t).

[0029] Among them, the preset rules are as follows: Set the initial values of the integral parameter and the differential parameter to 0, and iteratively increase the value of the proportional parameter in the automatic controller based on a preset increment. When the output value shows continuous oscillation, that is, the change rate of the output value is not higher than a preset threshold, record the period (P u ) of the oscillation. According to Equation 3: K u = 4 / (π*Au), determine the critical gain of the control parameter based on the change period and change amplitude of the output value. According to Equation 4: k p 0 = 0.6*K u , k i 0 = 0.5*P u , k d 0 = 0.125*P u , and determine the initial values of the control parameters in the automatic controller.

[0030] Among them, u(t) is the output of the controller, that is, the valve adjustment amount; e(t) is the difference between the target heat and the actual heat; k p is the proportional coefficient, which determines the response speed to the deviation; k i is the integral coefficient, which is used to eliminate the steady-state error; k d is the differential coefficient, which responds to the change rate of the deviation.

[0031] It should be noted that initialization is a process of clearing and setting the internal state of the device, which can ensure that the controller is in a definite state, thereby avoiding errors caused by uncertainty and non-specific states during the automatic operation process. Through the initialized automatic controller, the valve of the heating pipeline can be accurately adjusted according to the preset parameters and algorithms, so as to achieve precise control of environmental parameters such as indoor temperature and humidity.

[0032] In the embodiment of the present application, it also includes the fitting process of the polynomial regression model. Specifically, obtain the historical heating data in the target heating pipeline and preprocess the historical heating data; the historical heating data includes historical valve openings, historical actual heat, and historical target heat. Based on the preprocessed historical actual heat and historical target heat, obtain the historical heat difference and historical heat difference change rate of the target heating pipeline. According to the historical valve opening, historical heat difference, and historical heat difference change rate, fit the polynomial regression model, and evaluate the fitted polynomial regression model based on a preset evaluation index.

[0033] Determine the regression order of the polynomial regression model according to the heating influence factor of the target heating pipeline. Based on the regression order, perform a preliminary fit on the polynomial regression model. Construct a polynomial feature matrix based on the historical valve opening, historical heat difference, and historical heat difference change rate. Solve the polynomial feature matrix by the least squares method to obtain the regression coefficients, and input the regression coefficients into the preliminarily fitted polynomial regression model to obtain the finally fitted polynomial regression model.

[0034] S102: Through the membership functions corresponding to the preset fuzzy sets, convert the predicted heat difference and the predicted heat difference change rate into the membership degree values corresponding to the preset fuzzy sets, and obtain the total membership degree value of the preset fuzzy set collection.

[0035] Specifically, obtain the preset fuzzy set collection and the membership functions corresponding to the preset fuzzy sets. In this embodiment, the preset fuzzy set collection includes {Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Big (PB)}.

[0036] The membership function of the Negative Big fuzzy set is: where u NB (e) represents the membership degree belonging to Negative Big, E NB and E NM are the boundary values defining the Negative Big and Negative Medium fuzzy sets; the membership function of the Negative Medium fuzzy set is: where u NM (e) represents the membership degree belonging to Negative Big, E NM and E NS are the boundary values defining the Negative Medium and Negative Small fuzzy sets. And so on for the membership functions of other preset fuzzy sets.

[0037] Furthermore, take the predicted heat difference and the predicted heat difference change rate as inputs and input them into the membership functions corresponding to the preset fuzzy sets, respectively obtain the membership values of the predicted heat difference and the heat difference change rate corresponding to the preset fuzzy sets, and obtain the total membership degree value of the heat difference and the heat difference change rate in the preset fuzzy set collection.

[0038] It should be noted that the preset fuzzy sets are a set of membership functions obtained by performing fuzzy processing on control variables (such as temperature error, flow error, etc.). Common fuzzy sets usually classify error values or error change rates and formulate different fuzzy levels according to the classification. The core of fuzzy control is to handle uncertainty and fuzziness, and usually describes and controls the behavior of the system through fuzzy sets, membership functions, and fuzzy rules. In practical applications, using historical data to determine fuzzy sets and membership functions can make the control system more in line with the actual operating conditions and improve the accuracy and effect of control.

[0039] S103: Match in the fuzzy rule base based on the membership degree value to determine the fuzzy value of the valve adjustment amount corresponding to the target heating pipeline.

[0040] Specifically, query in the fuzzy rule base based on the first membership degree value corresponding to the heat difference and the second membership degree value corresponding to the heat difference change rate to obtain the corresponding fuzzy rule, and determine the fuzzy value of the valve adjustment amount corresponding to the target heating pipeline according to the fuzzy rule.

[0041] It should be noted that the fuzzy rules are formulated based on experience and expert knowledge. For example: if e(t) is negative large and Δe(t) is negative large, then u is positive large; if e(t) is negative small and Δe(t) is negative small, then u is positive small; if e(t) is negative large and Δe(t) is negative large, then u is positive large; if e(t) is negative large and Δe(t) is negative medium, then u is positive large; if e(t) is negative large and Δe(t) is negative small, then u is positive medium; if e(t) is negative large and Δe(t) is zero, then u is positive medium; if e(t) is negative large and Δe(t) is positive small, then u is positive medium; if e(t) is negative large and Δe(t) is positive medium, then u is positive small; if e(t) is negative large and Δe(t) is positive large, then u is positive small; if e(t) is negative medium and Δe(t) is negative large, then u is positive large; if e(t) is negative medium and Δe(t) is negative medium, then u is positive medium; if e(t) is negative medium and Δe(t) is negative small, then u is positive medium; if e(t) is negative medium and Δe(t) is zero, then u is positive medium; if e(t) is negative medium and Δe(t) is positive small, then u is positive small; if e(t) is negative medium and Δe(t) is positive medium, then u is positive small; if e(t) is negative medium and Δe(t) is positive large, then u is zero; if e(t) is negative small and Δe(t) is negative large, then u is positive medium; if e(t) is negative small and Δe(t) is negative medium, then u is positive medium; if e(t) is negative small and Δe(t) is negative small, then u is positive small; if e(t) is negative small and Δe(t) is zero, then u is positive small; if e(t) is negative small and Δe(t) is positive small, then u is positive small; if e(t) is negative small and Δe(t) is positive medium, then u is zero; if e(t) is negative small and Δe(t) is positive large, then u is negative small; if e(t) is positive small and Δe(t) is negative large, then u is positive medium; if e(t) is positive small and Δe(t) is negative medium, then u is positive medium; if e(t) is positive small and Δe(t) is negative small, then u is positive small; if e(t) is positive small and is zero, then u is positive small; if e(t) is positive small and Δe(t) is positive small, then u is zero; if e(t) is positive small and Δe(t) is positive medium, then u is negative small; if e(t) is positive small and Δe(t) is positive large, then u is negative small; if e(t) is positive medium and Δe(t) is negative large, then u is positive small; if e(t) is positive medium and Δe(t) is negative medium, then u is positive small; if e(t) is positive medium and Δe(t) is negative small, then u is zero; if e(t) is positive medium and Δe(t) is zero, then u is zero; if e(t) is positive medium and Δe(t) is positive small, then u is negative small; if e(t) is positive medium and Δe(t) is positive medium, then u is negative medium; if e(t) is positive medium and Δe(t) is positive large, then u is negative large; if e(t) is positive large and Δe(t) is negative large, then u is positive small;If e(t) is positive large and Δe(t) is negative medium, then u is zero; if e(t) is positive large and Δe(t) is negative small, then u is negative small; if e(t) is positive large and Δe(t) is zero, then u is negative small; if e(t) is positive large and Δe(t) is positive small, then u is negative medium; if e(t) is positive large and Δe(t) is positive medium, then u is negative large; if e(t) is positive large and Δe(t) is positive large, then u is negative large. Here, e(t) is the difference between the target heat and the actual heat, Δe(t) is the change rate of the heat difference, and u represents the membership degree belonging to the fuzzy set.;

[0042] It should be noted that k p , k i , k d correspond to the boundaries of different fuzzy sets respectively. The specific boundaries can be initially set according to the numerical values. As shown in Table 1, they are the boundary values corresponding to the parameters of different preset fuzzy sets. Generally, a relatively large range of adjustment is required for the proportional coefficient, while the adjustment ranges of the integral coefficient and the differential coefficient are relatively small.

[0043]

[0044] Table 1

[0045] The input error e(t) and the error change rate Δe(t) are fuzzified to determine their membership degrees belonging to each fuzzy set (such as negative large, negative medium, negative small, etc.). Then, according to the rules in the fuzzy rule base, the fuzzy value of the output variable (the adjustment amount of the automatic control parameter) is determined through the fuzzy inference method.

[0046] For example, for the rule "If e(t) is negative large and Δe(t) is negative large, then it is positive large", if the membership degree of the input variable e(t) is (the membership degree of e(t) belonging to negative large), and the membership degree of Δe(t) is (the membership degree of Δe(t) belonging to negative large), then the membership degree of the output variable belonging to positive large can be obtained through the minimum operation: u PB (Δk p ) = min{u NB (e), u NB (Δe)}.

[0047] S104: Defuzzify the fuzzy value according to the total contribution of the preset fuzzy set set and the total membership degree value to obtain the specific value of the valve adjustment amount; the total contribution is the sum of the contribution values of each preset fuzzy set to the defuzzification result.

[0048] Specifically, obtain the intervals and membership functions corresponding to each preset fuzzy set. Based on the membership functions and the central values of the intervals, determine the contribution values of each preset fuzzy set to the defuzzification result, and perform weighted summation on the contribution values of each preset fuzzy set to the defuzzification result to obtain the total contribution of the preset fuzzy set set.

[0049] For each fuzzy set, determine its corresponding interval and membership function. For example, the interval corresponding to the fuzzy set "PB (Positive Big)" is [a, b], and its membership function is u PB (Δk p ), calculate the contribution of each fuzzy set to the defuzzification result. For the "PB (Positive Big)" fuzzy set, its contribution is: Calculate the total contribution, that is, sum up the contributions of all fuzzy sets, denoted as where i represents different fuzzy sets, a i and b i are the interval endpoints of the corresponding fuzzy set. Calculate the total membership degree. Sum up the membership degrees of all fuzzy sets, denoted as T = ∑ i u i (Δk p ). The final defuzzification result is

[0050] S105: Adjust the control parameters of the automatic controller based on the specific value.

[0051] Specifically, adjust the PID parameters according to the fuzzy inference result: k p = k p 0 + Δk p , k i = k i 0 + Δk i , k d = k d 0 + Δk d , where k p 0, k i 0, k d 0 are the initial PID parameters, and Δk p , Δk i , Δk d are the adjustment amounts obtained according to the fuzzy inference. Based on the adjustment amounts, adjust the control parameters of the automatic controller.

[0052] When traditional automatic control faces a complex heat supply pipe network system, it may be difficult to adapt to different working condition changes due to fixed parameters. The method disclosed in this application can adjust the automatic control parameters in real time according to the difference between the target heat and the actual heat and its change rate, so as to more accurately control the valve opening and make the actual heat closer to the target heat. By continuously optimizing the automatic control parameters, the steady-state error of the system is reduced, the control accuracy of the system is improved, and the heat supply effect is ensured to be more stable. Fuzzy control can handle uncertainty and nonlinear problems, and has better adaptability to various disturbances and changes that may occur in the heat supply pipe network system. Real-time adjustment of the automatic control parameters can avoid excessive overshoot or oscillation of the system, make the response of the system more stable, and enhance the stability of the system. The automatic control of the valve is realized, manual intervention is reduced, and the working intensity of the operator is lowered. Automatic control can improve the reliability and consistency of the system and reduce the influence of human factors on the system operation.

[0053] As Figure 3 shown, the embodiment of this application also proposes a valve automatic regulation device for a heat supply pipeline, including:

[0054] At least one processor; and,

[0055] A memory communicatively connected to the at least one processor; wherein,

[0056] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a valve automatic regulation method for a heat supply pipeline as described in any one of the above embodiments.

[0057] The embodiment of this application also provides a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set as: a valve automatic regulation method for a heat supply pipeline as described in any one of the above embodiments.

[0058] The embodiments in this application are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0059] The device and medium provided by the embodiment of this application correspond one by one to the method. Therefore, the device and medium also have beneficial technical effects similar to those of the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium are not described here again.

[0060] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0061] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0062] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0064] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0065] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0066] A computer-readable medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0067] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0068] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for automatically controlling a valve of a heating pipeline, characterized in that: include: Acquire the opening value of the valve of the target heating pipeline in real time, and predict the heat difference and heat difference change rate of the target heating pipeline according to the opening value through the fitted polynomial regression model; By using the membership functions corresponding to the preset fuzzy sets, the predicted heat difference and the predicted heat difference change rate are converted into the membership values ​​corresponding to the preset fuzzy sets, and the total membership value of the preset fuzzy set is obtained; Based on the membership value, matching is performed in a fuzzy rule library to determine a fuzzy value of the valve adjustment amount corresponding to the target heating pipeline; According to the total contribution of the preset fuzzy set and the total membership value, the fuzzy value is defuzzified to obtain the specific value of the valve adjustment amount; the total contribution is the sum of the contribution values ​​of the preset fuzzy sets to the defuzzification result; Based on the specific value, the control parameters of the automation controller are adjusted.

2. The method for automatic valve control of a heating pipeline according to claim 1, characterized in that: Before predicting the heat difference and heat difference change rate of the target heating pipeline according to the opening value by using the fitted polynomial regression model, the method further includes: Acquire historical heating data in the target heating pipeline, and pre-process the historical heating data; the historical heating data includes historical valve opening, historical actual heat, and historical target heat; Based on the pre-processed historical actual heat and historical target heat, the historical heat difference and the historical heat difference change rate of the target heating pipeline are obtained; A polynomial regression model is fitted according to the historical valve opening, the historical heat difference and the historical heat difference change rate, and the fitted polynomial regression model is evaluated based on a preset evaluation index.

3. The method for automatic valve control of a heating pipeline according to claim 2, characterized in that: The fitting of the polynomial regression model according to the historical valve opening, the historical heat difference and the historical heat difference change rate specifically includes: Determining the regression order of the polynomial regression model according to the heating influencing factor of the target heating pipeline; Based on the regression order, preliminarily fitting the polynomial regression model; Based on the historical valve opening, the historical heat difference and the historical heat difference change rate, a polynomial characteristic matrix is ​​constructed, and the polynomial characteristic matrix is ​​solved by a least square method to obtain a regression coefficient; The regression coefficients are input into the initially fitted polynomial regression model to obtain the final fitted polynomial regression model.

4. The method for automatic valve control of a heating pipeline according to claim 1, characterized in that: The matching in the fuzzy rule base based on the membership value to determine the fuzzy value of the valve adjustment amount corresponding to the target heating pipeline specifically includes: Based on the first membership value corresponding to the heat difference and the second membership value corresponding to the heat difference change rate, query in a fuzzy rule library to obtain a corresponding fuzzy rule; According to the fuzzy rule, a fuzzy value of the valve adjustment amount corresponding to the target heating pipeline is determined.

5. The method for automatic valve control of a heating pipeline according to claim 1, characterized in that: Before defuzzifying the fuzzy value according to the total contribution of the preset fuzzy set and the total membership value to obtain the specific valve adjustment amount, the method further includes: Obtaining the intervals and membership functions corresponding to the preset fuzzy sets; Determining the contribution value of each preset fuzzy set to the defuzzification result based on the membership function and the center value of the interval; The contribution values ​​of the preset fuzzy sets to the defuzzification result are weightedly summed to obtain the total contribution of the preset fuzzy set set.

6. The method for automatic valve control of a heating pipeline according to claim 1, characterized in that: Before obtaining the opening value of the valve of the target heating pipeline in real time, the method further includes: Acquire the historical actual heat corresponding to the target heating pipeline within the historical preset collection time, and determine the historical heat difference corresponding to the historical preset collection time based on the historical actual heat; Initializing control parameters in the automation controller based on the historical heat difference and preset rules; The valves in the target heating pipeline are initially controlled by the initialized automation controller.

7. The method for automatic valve control of a heating pipeline according to claim 6, characterized in that: Initializing the control parameters in the automation controller based on the historical heat difference and the preset rules specifically includes: Iteratively adjusting control parameters in the automation controller based on a preset increment; Inputting the historical heat differences into the automation controller in sequence to obtain output values ​​of the automation controller; When the rate of change of the output value is not higher than a preset threshold, determining the critical gain of the control parameter based on the change period and change amplitude of the output value; An initial value of a control parameter in the automation controller is determined according to the critical gain.

8. The method for automatic valve control of a heating pipeline according to claim 7, characterized in that: The control parameters include integral parameters, differential parameters and proportional parameters; The iterative adjustment of the control parameters in the automation controller based on the preset increment specifically includes: Setting the initial values ​​of the integral parameter and the differential parameter to 0; Based on a preset increment, a value of a proportional parameter in the automation controller is iteratively increased.

9. An automatic valve control device for a heating pipeline, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for automatic valve control of a heating pipeline as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured as: a method for automatically controlling a valve of a heating pipeline as described in any one of claims 1 to 8.

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