Heating regulation method and system based on distributed intelligent algorithm and adaptive control

Abstract

The application discloses a heat supply regulation and control method and system based on a distributed intelligent algorithm and adaptive control, and belongs to the technical field of intelligent heat supply, and the method specifically comprises the following steps: collecting building data, including temperature, energy consumption, heat supply system form, and climate condition information; preprocessing the collected building data; coupling a distributed intelligent algorithm with a heat supply system model; optimizing and regulating the operation parameters of the heat supply system; utilizing an adaptive control strategy; dynamically regulating the heat supply system in real time according to real-time data of the heat supply system and the optimization result of the distributed intelligent algorithm; continuously monitoring and evaluating the operation effect of the heat supply system; adjusting and optimizing according to the actual situation; and ensuring the safe, stable and efficient operation of the heat supply system, thereby improving the regulation and control capacity and reducing the overall energy consumption of the heat supply system.

Description

Technical Field

[0001] This invention belongs to the field of intelligent heating technology, specifically a heating regulation method and system based on distributed intelligent algorithms and adaptive control. Background Technology

[0002] Centralized heating is an important basic infrastructure for people's livelihood in my country. While solving the problem of winter heating in my country, the large amount of energy consumed by centralized heating cannot be ignored. In recent years, a new type of power-distributed centralized heating system has been developed. Its core is to install variable frequency water pumps in each user to replace the regulating valves in the traditional heating system. This avoids the waste of pressure head and can significantly save the energy consumption of the centralized heating system. The key to the power-distributed centralized heating system is the operation and maintenance part.

[0003] At the same time, the heating process mostly uses traditional PID control to control the heating system. Because traditional PID control has a long response time, high overshoot, and is greatly affected by external factors, it results in the underutilization of energy and serious waste.

[0004] For example, Chinese patent CN105371357B discloses a heating network regulation method and system. This method involves analyzing the impact of multiple meteorological parameters on indoor temperature through the building envelope. The analysis data includes the attenuation of these multiple meteorological parameters. Based on actual heating data, the lag time of the heating network is determined. Then, a first relationship is established, and predicted values ​​of the multiple meteorological parameters at a lag time interval are obtained. Based on the predicted values, a pre-set indoor temperature value, and the first relationship, the current water supply temperature is determined and adjusted so that the indoor temperature reaches the pre-set value after the lag time interval. This solution, by determining the first relationship and predicting the multiple meteorological parameter values ​​at a fixed time interval and the pre-set indoor temperature value, achieves pre-adjustment of the water supply temperature. This ensures that the indoor temperature after the lag time interval matches the outdoor multiple meteorological parameter values ​​at that moment, and improves indoor temperature comfort even when outdoor temperature changes abnormally.

[0005] For example, Chinese Patent CN1 15962509A discloses a heating control method and system, belonging to the field of smart heating technology. It solves the problem that existing control methods rely too heavily on design values, resulting in high system energy consumption and energy waste. The heating control method includes: acquiring historical operating data from heat exchange stations and users; cleaning the data to establish a sample database; establishing a differential pressure calculation model based on the sample database, which represents the correspondence between the actual supply and return water pressure difference for users and the outdoor temperature; identifying the most unfavorable user; calculating the actual supply and return water pressure difference for the most unfavorable user based on the differential pressure calculation model; adjusting the frequency conversion operation of the water pump based on the actual supply and return water pressure difference and the set supply and return water pressure difference for the most unfavorable user; and establishing a water supply temperature calculation model based on the sample database using machine learning algorithms to adjust the secondary side water supply temperature. This invention's method does not rely on design values, does not operate at constant flow rates, provides a basis for water pump frequency conversion, and reduces system energy consumption.

[0006] The defects of the above patents are: 1) Comparing indoor and outdoor temperatures can easily lead to lag and make precise adjustment impossible; 2) Considering only water temperature and not flow rate, although energy consumption is reduced, the pressure of the water supply pipeline is increased, and the high temperature and increased pressure increase the losses of the heating system. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention proposes a heating regulation method and system based on distributed intelligent algorithms and adaptive control. The method collects building data, including temperature, energy consumption, heating system type, and climate conditions. The collected building data is preprocessed, and the distributed intelligent algorithm is coupled with a heating system model to optimize and regulate the heating system's operating parameters. Using an adaptive control strategy, the heating system is dynamically regulated in real-time based on real-time data and the optimization results of the distributed intelligent algorithm. The system continuously monitors and evaluates its operational effectiveness, making adjustments and optimizations according to actual conditions to ensure the safe, stable, and efficient operation of the heating system.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] The heating regulation method based on distributed intelligent algorithms and adaptive control includes the following specific steps:

[0010] Step S1: Collect building data, including: temperature, energy consumption, heating system type, and climate conditions.

[0011] Step S2: Preprocess the collected building data;

[0012] Step S3: Couple the distributed intelligent algorithm with the heating system model to optimize and control the operating parameters of the heating system;

[0013] Step S4: Using an adaptive control strategy, the heating system is dynamically adjusted in real time based on real-time data from the heating system and the optimization results of the distributed intelligent algorithm.

[0014] Step S5: Continuously monitor and evaluate the operating performance of the heating system, and make adjustments and optimizations based on the actual situation to ensure the safe, stable and efficient operation of the heating system.

[0015] Specifically, the preprocessing in step S2 includes: data cleaning and sorting, which processes redundant and duplicate data in the collected building data.

[0016] Specifically, the steps of step S3 are as follows:

[0017] Step S301: Establish a mathematical model of the heating system, including a heat source model, a dynamic pipe model, and a radiator model;

[0018] Step S302: Select a distributed intelligent algorithm, including genetic algorithm and particle swarm algorithm;

[0019] Step S303: Couple the selected distributed intelligent algorithm with the established mathematical model of the heating system. The formula for calculating the coupling factor is:

[0020] Oh = sup(λ) ry ×ry+λ gd ×gd)-inf(λ sr ×sr),

[0021] Where Oh represents the coupling factor between the distributed intelligent algorithm and the mathematical model of the heating system, sup() represents the upper bound function, inf() represents the lower bound function, ry represents the heat source model of the heating system, and λ ry λ represents the influence coefficient of the heat source model of the heating system, gd represents the dynamic pipe model of the heating system, and λ represents the influence coefficient of the heat source model of the heating system. gd λ represents the influence coefficient of the dynamic piping model of the heating system, sr represents the radiator model of the heating system, and λ represents the influence coefficient of the dynamic piping model of the heating system. sr This represents the influence coefficient of the radiator model in the heating system;

[0022] Step S304: Optimize and control the operating parameters of the heating system, and continuously iterate until the optimal solution is reached.

[0023] Specifically, the calculation formula for iterative optimization is as follows:

[0024] v i =ηv i +α×random(0,1)×(pw i )+β×random(0,1)×(gwi ),

[0025] Among them, v i Let η represent the velocity of the i-th particle, and w represent the optimization capability coefficient. i Let represent the position of the i-th particle, random(0,1) represent a random function in the interval 0 to 1, α and β represent the learning constants that affect the particle, p represents the local optimum, and g represents the global optimum.

[0026] Specifically, the steps of step S4 are as follows:

[0027] Step S401: Use a PID controller to regulate and control the heating system. The specific formula is as follows:

[0028]

[0029] Where u(t) represents the control quantity of the PID controller for regulating the heating system, and K p T represents the proportionality constant. j T represents the integration constant. d Let represent the differential constant, and e(t) represent the error signal;

[0030] Step S402: Optimize the PID controller using a neural network model. The specific formula is as follows:

[0031]

[0032] Among them, u yh (t) represents the control quantity of the optimized PID controller for regulating the heating system, u(t-1) represents the control quantity of the PID controller for regulating the heating system at the previous moment, and x n (t) represents the input of the neural network model, S n (t) represents the output of the neural network model;

[0033] Step S403: The formula for the convergence function of the heating system regulation and control is:

[0034] Tj=σ1(T sz -T sw ) 2 +σ2(Q sz -Q dq ) 2 ,

[0035] Where Tj represents the convergence function of the heating system regulation and control, σ1 represents the weighting coefficient of temperature regulation and control, σ2 represents the weighting coefficient of flow regulation and control, and T sz This indicates the temperature set in the heating system, T. swQ represents the average temperature of the current building environment. sz Q represents the flow rate set for the heating system. dq The current flow rate set in the heating system;

[0036] Step S404: Perform regulation control based on the optimized PID controller and the heating system regulation control convergence function to minimize the heating system regulation control convergence function.

[0037] Specifically, the neural network models in step S402 include LSTM, BP neural network, and RCNN.

[0038] A heating regulation system based on distributed intelligent algorithms and adaptive control includes: a building data collection module, a preprocessing module, a coupling optimization module, an adaptive control module, and a monitoring and evaluation module.

[0039] The building data collection module is used to collect building data, including: temperature, energy consumption, heating system type, and climate condition information.

[0040] The preprocessing module is used to preprocess the collected building data;

[0041] The coupling optimization module is used to couple the distributed intelligent algorithm with the heating system model to optimize and control the operating parameters of the heating system.

[0042] The adaptive control module is used to dynamically regulate the heating system in real time based on real-time data of the heating system and the optimization results of the distributed intelligent algorithm using an adaptive control strategy.

[0043] The monitoring and evaluation module is used to continuously monitor and evaluate the operating effect of the heating system, and make adjustments and optimizations according to the actual situation to ensure the safe, stable and efficient operation of the heating system.

[0044] Specifically, the coupling optimization module includes: a coupling unit and an optimization unit;

[0045] The coupling unit is used to couple the selected distributed intelligent algorithm with the established mathematical model of the heating system.

[0046] The optimization unit is used to optimize and regulate the operating parameters of the heating system.

[0047] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a heating regulation method based on a distributed intelligent algorithm and adaptive control.

[0048] A computer-readable storage medium storing computer instructions that, when executed, perform steps of a heating regulation method based on distributed intelligent algorithms and adaptive control.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] 1. This invention proposes a heating regulation system based on distributed intelligent algorithms and adaptive control, and optimizes and improves its architecture, operation steps and processes. The system has the advantages of simple process, low investment and operating costs and low production costs.

[0051] 2. This invention proposes a heating regulation method based on distributed intelligent algorithms and adaptive control. By coupling the selected distributed intelligent algorithm with the established mathematical model of the heating system, the operating parameters of the heating system are optimized and regulated, which greatly reduces the energy consumption of the heating system's transmission and distribution.

[0052] 3. This invention proposes a heating regulation method based on distributed intelligent algorithms and adaptive control. It utilizes a neural network model to optimize the PID controller, regulate and control the heating system, establishes a convergence function for the heating system regulation and control, and converges the heating system regulation and control convergence function to minimize the convergence function, thereby improving the regulation and control capability and reducing the overall energy consumption of the heating system. Attached Figure Description

[0053] Figure 1 This is a flowchart of the heating regulation method based on distributed intelligent algorithms and adaptive control according to the present invention;

[0054] Figure 2 This is a diagram of the heating regulation system architecture based on distributed intelligent algorithms and adaptive control of the present invention.

[0055] Figure 3 This is an electronic device diagram of the heating regulation method based on distributed intelligent algorithms and adaptive control according to the present invention. Detailed Implementation

[0056] To facilitate understanding of the technical means, creative features, and achieved objectives and effects of this invention, it should be noted in the description of this invention that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "number one," "number two," and "number three" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The invention will be further described below in conjunction with specific embodiments.

[0057] Example 1

[0058] Please see Figure 1 The present invention provides an embodiment of a heating regulation method based on distributed intelligent algorithms and adaptive control, comprising the following specific steps:

[0059] Step S1: Collect building data, including: temperature, energy consumption, heating system type, and climate conditions.

[0060] Building data also includes: building area, number of floors, building orientation, exterior wall structure, terminal equipment, and exterior window structure;

[0061] Step S2: Preprocess the collected building data;

[0062] Step S3: Couple the distributed intelligent algorithm with the heating system model to optimize and control the operating parameters of the heating system;

[0063] Step S4: Using an adaptive control strategy, the heating system is dynamically adjusted in real time based on real-time data from the heating system and the optimization results of the distributed intelligent algorithm.

[0064] Step S5: Continuously monitor and evaluate the operating performance of the heating system, and make adjustments and optimizations based on the actual situation to ensure the safe, stable and efficient operation of the heating system.

[0065] The preprocessing in step S2 includes: data cleaning and sorting, which processes redundant and duplicate data in the collected building data.

[0066] The specific steps of step S3 are as follows:

[0067] Step S301: Establish a mathematical model of the heating system, including a heat source model, a dynamic pipe model, and a radiator model;

[0068] The specific formula for the heat source model is: Among them, Tout T represents the output temperature of the heating system. in Q represents the initial temperature of the fluid entering the heating system for heating. max This indicates the maximum heat capacity of the heating system, m. lt C represents the mass of the heating fluid entering the thermal system. lt This indicates the specific heat capacity of the fluid entering the heating system; the fluid can be water or other substances.

[0069] The specific formula for the dynamic pipeline model is: Among them, T′ out T′ represents the outlet temperature of the pipe. in T represents the inlet temperature of the pipe. b R represents the changing temperature. gd C represents the thermal resistance of the pipe. gd The exp(·) represents the specific heat capacity of the pipe wall, and exp(·) represents an exponential function.

[0070] The specific formula for the heat sink model is: Q sr =K sr m sr (T out -T′ in ), where Q sr K represents the heat dissipation of the radiator. sr The heat transfer coefficient of the radiator is represented by m. sr Indicates the mass of the heat dissipation fluid in the radiator;

[0071] Step S302: Select a distributed intelligent algorithm, including genetic algorithm and particle swarm algorithm;

[0072] Step S303: Couple the selected distributed intelligent algorithm with the established mathematical model of the heating system. The formula for calculating the coupling factor is:

[0073] Oh = sup(λ) ry ×ry+λ gd ×gd)-inf(λ sr ×sr),

[0074] Where Oh represents the coupling factor between the distributed intelligent algorithm and the mathematical model of the heating system, sup() represents the upper bound function, inf() represents the lower bound function, ry represents the heat source model of the heating system, and λ ry λ represents the influence coefficient of the heat source model of the heating system, gd represents the dynamic pipe model of the heating system, and λ represents the influence coefficient of the heat source model of the heating system. gd λ represents the influence coefficient of the dynamic piping model of the heating system, sr represents the radiator model of the heating system, and λ represents the influence coefficient of the dynamic piping model of the heating system. sr This represents the influence coefficient of the radiator model in the heating system;

[0075] Step S304: Optimize and control the operating parameters of the heating system, and continuously iterate until the optimal solution is reached.

[0076] The calculation formula for iterative optimization is:

[0077] v i =ηv i +α×random(0,1)×(pw i )+β×random(0,1)×(gw i ),

[0078] Among them, v i 77 represents the velocity of the i-th particle, 77 represents the optimization capability coefficient, and w i Let represent the position of the i-th particle, random(0,1) represent a random function in the interval 0 to 1, α and β represent the learning constants that affect the particle, p represents the local optimum, and g represents the global optimum.

[0079] When η is large, the global optimization ability is weak, but the local optimization ability is strong; when η is small, the global optimization ability is strong, but the local optimization ability is weak.

[0080] The specific steps of step S4 are as follows:

[0081] Step S401: Use a PID controller to regulate and control the heating system. The specific formula is as follows:

[0082]

[0083] Where u(t) represents the control quantity of the PID controller for regulating the heating system, and K p T represents the proportionality constant. j T represents the integration constant. d Let represent the differential constant, and e(t) represent the error signal;

[0084] Step S402: Optimize the PID controller using a neural network model. The specific formula is as follows:

[0085]

[0086] Among them, u yh (t) represents the control quantity of the optimized PID controller for regulating the heating system, u(t-1) represents the control quantity of the PID controller for regulating the heating system at the previous moment, and x n (t) represents the input of the neural network model, S n (t) represents the output of the neural network model;

[0087] Step S403: The formula for the convergence function of the heating system regulation and control is:

[0088] Tj=σ1(T sz -T sw ) 2 +σ2(Q sz -Q dq ) 2 ,

[0089] Where Tj represents the convergence function of the heating system regulation and control, σ1 represents the weighting coefficient of temperature regulation and control, σ2 represents the weighting coefficient of flow regulation and control, and T sz This indicates the temperature set in the heating system, T. sw Q represents the average temperature of the current building environment. sz Q represents the flow rate set for the heating system. dq The current flow rate set in the heating system;

[0090] Step S404: Perform regulation control based on the optimized PID controller and the heating system regulation control convergence function to minimize the heating system regulation control convergence function.

[0091] The neural network models in step S402 include: LSTM, BP neural network and RCNN.

[0092] Example 2

[0093] Please see Figure 2 Another embodiment of the present invention provides a heating regulation system based on distributed intelligent algorithms and adaptive control, comprising: a building data collection module, a preprocessing module, a coupling optimization module, an adaptive control module, and a monitoring and evaluation module;

[0094] The building data collection module is used to collect building data, including: temperature, energy consumption, heating system type, and climate condition information.

[0095] The preprocessing module is used to preprocess the collected building data;

[0096] The coupling optimization module is used to couple the distributed intelligent algorithm with the heating system model to optimize and control the operating parameters of the heating system.

[0097] The adaptive control module is used to dynamically regulate the heating system in real time based on real-time data of the heating system and the optimization results of the distributed intelligent algorithm using an adaptive control strategy.

[0098] The monitoring and evaluation module is used to continuously monitor and evaluate the operating effect of the heating system, and make adjustments and optimizations according to the actual situation to ensure the safe, stable and efficient operation of the heating system.

[0099] The coupling optimization module includes: coupling units and optimization units;

[0100] The coupling unit is used to couple the selected distributed intelligent algorithm with the established mathematical model of the heating system.

[0101] The optimization unit is used to optimize and regulate the operating parameters of the heating system.

[0102] Example 3

[0103] Please see Figure 3 An electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a heating regulation method based on a distributed intelligent algorithm and adaptive control.

[0104] A computer-readable storage medium storing computer instructions that, when executed, perform steps of a heating regulation method based on distributed intelligent algorithms and adaptive control.

[0105] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0110] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A heating regulation method based on distributed intelligent algorithms and adaptive control, characterized in that, The specific steps include the following: Step S1: Collect building data, including: temperature, energy consumption, heating system type, and climate conditions. Step S2: Preprocess the collected building data; Step S3: Couple the distributed intelligent algorithm with the heating system model to optimize and control the operating parameters of the heating system; Step S4: Using an adaptive control strategy, the heating system is dynamically adjusted in real time based on real-time data from the heating system and the optimization results of the distributed intelligent algorithm. Step S5: Continuously monitor and evaluate the operating performance of the heating system, and make adjustments and optimizations based on the actual situation to ensure the safe, stable and efficient operation of the heating system; The specific steps of step S3 are as follows: Step S301: Establish a mathematical model of the heating system, including a heat source model, a dynamic pipe model, and a radiator model; Step S302: Select a distributed intelligent algorithm, including genetic algorithm and particle swarm algorithm; Step S303: Couple the selected distributed intelligent algorithm with the established mathematical model of the heating system. The formula for calculating the coupling factor is: ， Where Oh represents the coupling factor between the distributed intelligent algorithm and the mathematical model of the heating system. This indicates a function that takes the upper limit. This indicates a function that takes the lower bound. This represents the heat source model of the heating system. This represents the influence coefficient of the heat source model of the heating system. This represents a dynamic piping model of the heating system. This represents the influence coefficient of the dynamic piping model of the heating system. This represents a radiator model of a heating system. This represents the influence coefficient of the radiator model in the heating system; Step S304: Optimize and control the operating parameters of the heating system, and continuously iterate until the optimal solution is reached.

2. The heating regulation method based on distributed intelligent algorithms and adaptive control as described in claim 1, characterized in that, The preprocessing in step S2 includes: data cleaning and sorting, which processes redundant and duplicate data in the collected building data.

3. The heating regulation method based on distributed intelligent algorithms and adaptive control as described in claim 2, characterized in that, The calculation formula for iterative optimization is: ， in, This represents the velocity of the i-th particle. This represents the optimization capability coefficient. This represents the position of the i-th particle. This represents a random function within the interval 0 to 1. and denoted by the particle learning constant, p represents the local optimum, and g represents the global optimum.

4. The heating regulation method based on distributed intelligent algorithms and adaptive control as described in claim 3, characterized in that, The specific steps of step S4 are as follows: Step S401: Use a PID controller to regulate and control the heating system. The specific formula is as follows: ， in, This represents the control quantity used by the PID controller to regulate and control the heating system. Represents a proportionality constant. Denotes the integration constant. Represents the differential constant. Indicates the error signal; Step S402: Optimize the PID controller using a neural network model. The specific formula is as follows: ， in, This represents the control quantity used by the optimized PID controller to regulate and control the heating system. This represents the control quantity that the PID controller used to regulate and control the heating system at the previous moment. This represents the input to the neural network model. This represents the output of the neural network model; Step S403: The formula for the convergence function of the heating system regulation and control is: ， in, This represents the convergence function of the heating system regulation and control. This represents the weighting coefficient for temperature regulation control. This represents the weighting coefficient for flow regulation control. This indicates the temperature set in the heating system. This indicates the average temperature of the current building environment. This indicates the flow rate set for the heating system. The current flow rate set in the heating system; Step S404: Perform regulation control based on the optimized PID controller and the heating system regulation control convergence function to minimize the heating system regulation control convergence function.

5. The heating regulation method based on distributed intelligent algorithms and adaptive control as described in claim 4, characterized in that, The neural network models in step S402 include: LSTM, BP neural network and RCNN.

6. A heating regulation system based on distributed intelligent algorithms and adaptive control, implemented based on the heating regulation method based on distributed intelligent algorithms and adaptive control as described in any one of claims 1-5, characterized in that, include: The building data collection module, preprocessing module, coupling optimization module, adaptive control module, and monitoring and evaluation module are included. The building data collection module is used to collect building data, including: temperature, energy consumption, heating system type, and climate condition information. The preprocessing module is used to preprocess the collected building data; The coupling optimization module is used to couple the distributed intelligent algorithm with the heating system model to optimize and control the operating parameters of the heating system; the specific steps are as follows: Establish a mathematical model of the heating system, including a heat source model, a dynamic pipe model, and a radiator model; Choose distributed intelligent algorithms, including genetic algorithms and particle swarm optimization algorithms; The selected distributed intelligent algorithm is coupled with the established mathematical model of the heating system. The coupling factor is calculated using the following formula: ， Where Oh represents the coupling factor between the distributed intelligent algorithm and the mathematical model of the heating system. This indicates a function that takes the upper limit. This indicates a function that takes the lower bound. This represents the heat source model of the heating system. This represents the influence coefficient of the heat source model of the heating system. This represents a dynamic piping model of the heating system. This represents the influence coefficient of the dynamic piping model of the heating system. This represents a radiator model of a heating system. This represents the influence coefficient of the radiator model in the heating system; The operating parameters of the heating system are optimized and controlled, and iterative optimization is continuously carried out until the optimal solution is reached. The adaptive control module is used to dynamically regulate the heating system in real time based on real-time data of the heating system and the optimization results of the distributed intelligent algorithm using an adaptive control strategy. The monitoring and evaluation module is used to continuously monitor and evaluate the operating effect of the heating system, and make adjustments and optimizations according to the actual situation to ensure the safe, stable and efficient operation of the heating system.

7. The heating regulation system based on distributed intelligent algorithms and adaptive control as described in claim 6, characterized in that, The coupling optimization module includes: a coupling unit and an optimization unit; The coupling unit is used to couple the selected distributed intelligent algorithm with the established mathematical model of the heating system. The optimization unit is used to optimize and regulate the operating parameters of the heating system.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the heating regulation method based on distributed intelligent algorithm and adaptive control as described in any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, It stores computer instructions, which, when executed, perform the steps of the heating regulation method based on distributed intelligent algorithms and adaptive control as described in any one of claims 1-5.

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