Multi-energy complementary heating system for near-zero energy consumption building

Abstract

The invention discloses a multi-energy complementary heating system for a near-zero energy consumption building, which relates to the technical field of building energy-saving equipment and comprises a microclimate regulation and energy optimization integration module, a multi-energy complementary heating system module, an intelligent control and execution module and a user interaction and feedback module, the microclimate adjustment and energy optimization integrated module monitors microclimate data of the surrounding environment of the building in real time through a high-precision sensor, and the microclimate adjustment and energy optimization integrated module monitors the microclimate data of the surrounding environment of the building in real time through the high-precision sensor; and factors having key influences on the heating effect are accurately extracted by utilizing a Pearson's correlation coefficient analysis algorithm and a neural network algorithm, and the system can dynamically calculate the optimal heating strategy and parameters based on the key factors, so that the operation efficiency of the heating system is remarkably improved, unnecessary energy waste is reduced, and the energy consumption is reduced. Therefore, a remarkable energy-saving effect is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of building energy conservation, and in particular to a multi-energy complementary heating system for a near-zero energy consumption building. Background Art

[0002] Traditional building heating systems often rely solely on a single type of energy for energy utilization. Not only are they energy-intensive and polluting, they are also difficult to cope with energy price fluctuations and unstable supply. In addition, traditional building heating systems have many shortcomings in microclimate regulation and energy optimization. First, most traditional building heating systems lack real-time microclimate monitoring capabilities and cannot accurately perceive changes in the building's surrounding environment, resulting in delayed adjustments to heating strategies and difficulty in adapting to complex and changeable microclimate conditions. Secondly, traditional systems mostly use static or simple rule control for energy optimization, lack the support of advanced energy optimization algorithms, and are unable to dynamically adjust heating strategies and parameters based on real-time data and heating demand, resulting in energy waste and unsatisfactory heating effects.

[0003] At the same time, the traditional traditional heating system lacks accurate classification and effective utilization mechanism of thermal energy quality, resulting in the failure of high-temperature thermal energy to be fully used in high-energy-efficiency fields such as heating, while medium and low-temperature thermal energy is often ignored or inefficiently used. Summary of the invention

[0004] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a multi-energy complementary heating system for near-zero energy buildings. It can realize real-time monitoring and precise analysis of the microclimate of the building's surrounding environment by integrating high-precision sensors, data analysis units and energy optimization algorithms, and dynamically calculate the optimal heating strategy and parameters based on the current microclimate conditions and heating needs inside the building; at the same time, it adopts an energy cascade utilization strategy, makes good use of high-precision monitoring and thermodynamic analysis, and realizes the precise division and efficient utilization of thermal energy quality.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A multi-energy complementary heating system for a near-zero energy consumption building, the system comprising:

[0007] Microclimate regulation and energy optimization integrated module: The module monitors the microclimate data of the building's surrounding environment in real time through high-precision sensors, processes the data using the built-in data analysis unit, extracts the key factors that affect the heating effect, and dynamically calculates the heating strategy and parameters based on the energy optimization algorithm, according to the current microclimate conditions and the heating demand inside the building;

[0008] Intelligent control and execution module: Receives heating strategies and parameter instructions sent by the microclimate regulation and energy optimization integration module in real time, parses the instructions, clarifies the specific control requirements of each heating device, sends control signals to each heating device through the communication interface according to the parsed instructions, monitors the operating status and feedback information of the equipment, monitors the operating status and performance indicators of the system in real time, identifies potential problems and takes corresponding repair measures, and continuously optimizes the control strategy through self-learning algorithms;

[0009] Multi-energy complementary heating system module: configure a variety of heating equipment according to the building scale and heating needs, so that the equipment can be interconnected and connected to the system control center for unified scheduling; receive the control signal sent by the intelligent control and execution module, and adjust the operating parameters and working hours of each heating equipment according to the strategy;

[0010] Energy cascade utilization strategy: Use high-precision sensors to monitor the temperature and flow data of each heat source in real time to accurately determine the quality of thermal energy. High-temperature thermal energy is transferred to the indoor heating medium through a high-efficiency heat exchanger to heat the indoor temperature. Medium-temperature thermal energy is used in scenarios with higher temperature thermal energy demand, and low-temperature thermal energy is used to meet the daily living needs of building residents and for building dehumidification.

[0011] Furthermore, the microclimate regulation and energy optimization integrated module uses the Pearson correlation coefficient analysis algorithm to extract key factors that affect the heating effect, including sunshine duration, wind direction frequency, and temperature fluctuation range. Based on the extracted key factors and the heating demand inside the building, the neural network algorithm is used to calculate the optimal heating strategy and parameters, including the start and stop time, operating power and working mode of the heating equipment.

[0012] Furthermore, the microclimate regulation and energy optimization integrated module uses the Pearson correlation coefficient analysis algorithm to extract the key factors that affect the heating effect, and the calculation formula is: Among them, X and Y represent the observation value series of microclimate factors and heating effects, respectively. i and Y i is the corresponding observed value, and is the mean, n is the number of observations, and based on the calculated correlation coefficient values, the microclimate factors that are correlated with the heating effect are identified as key factors.

[0013] Furthermore, the multi-energy complementary heating system module is configured with a variety of heating equipment according to the building scale and heating needs, so that the equipment can be interconnected and connected to the system control center for unified scheduling. At the same time, it receives the optimal heating strategy and parameters output by the microclimate regulation and energy optimization integration module, and adjusts the operating parameters and working hours of each heating equipment according to the strategy.

[0014] Furthermore, the intelligent control and execution module receives the heating strategies and parameter instructions sent by the microclimate regulation and energy optimization integration module in real time, parses the instructions, clarifies the specific control requirements of each heating equipment, sends control signals to each heating equipment through the communication interface according to the parsed instructions, monitors the operating status and feedback information of the equipment, monitors the operating status and performance indicators of the system in real time, identifies potential problems and takes corresponding repair measures, and continuously optimizes the control strategy through self-learning algorithms.

[0015] Furthermore, the intelligent control and execution module uses a PID control algorithm to adjust the operating parameters of the heating equipment according to the parameter instructions sent by the microclimate regulation and energy optimization integration module. Specifically, according to the deviation between the target temperature and the actual temperature of the heating system and the historical data of the deviation, a control signal is calculated, and the calculation formula is: Among them, K p is the proportional gain, K i is the integral gain, K d is the differential gain, e(t) is the deviation at time t, (τ) is the integral of the deviation, It is the differential of the deviation. According to the calculated control signal u(t), the operating parameters of the heating equipment are adjusted.

[0016] Furthermore, the intelligent control and execution module uses the Q-learning algorithm to continuously optimize the control strategy, and its calculation formula is: Q(s t ,a t )←Q(s t ,a t )+α[r t+1 +γmax a′ Q(s t+1 ,a′)-Q(s t ,a t )], where s t and a t are the system state and action taken at time t, r t+1 Is to perform action a t After the state s t Transfer to state s t+1 The instant reward obtained when α is the learning rate, γ is the discount factor, and max a′ Q(s t+1 ,a′) is in the new state s t+1 The Q value with the largest expected benefit among all actions is taken, and the control strategy of the heating system is gradually optimized through continuous iteration.

[0017] Furthermore, the energy cascade utilization strategy is equipped with high-precision temperature sensors and flow sensors to monitor the temperature and flow data of each heat source device in real time. Based on the collected temperature and flow data, a thermodynamic analysis algorithm is used to calculate and judge the quality of thermal energy, and the thermal energy is divided into different quality levels of high temperature, medium temperature, and low temperature. The high-temperature thermal energy is directly used for heating, and a highly efficient heat exchanger is used to achieve efficient transfer to the indoor heating medium. The flow rate of the heating medium is adjusted through a fuzzy control algorithm. The medium-temperature thermal energy is flexibly applied to scenarios such as swimming pool heating and industrial preheating according to actual needs. The low-temperature thermal energy is used for heating domestic hot water and dehumidifying the interior of the building. The waste heat generated during the process of thermal energy utilization is recovered through a heat recovery device, and a closed-loop energy utilization system is re-formed according to the quality and available value.

[0018] Furthermore, the energy cascade utilization strategy uses a thermodynamic analysis algorithm to judge the quality of thermal energy, determine thermodynamic parameters, obtain the parameters of temperature T, pressure P, enthalpy H, and entropy S of the heat energy source, and calculate the available energy E. x , taking heat transfer as an example, the formula for available energy E x is: where: Q is the magnitude of heat transfer, T0 is the ambient reference temperature, and T is the temperature of the heat source. According to the calculated magnitude of available energy and temperature range, the quality level of thermal energy is divided. High-temperature thermal energy: when T > 80 °C and E x > E xnigh , where: 80 °C is the temperature threshold for high temperature, and E xnigh is the available energy threshold corresponding to high temperature. Medium-temperature thermal energy: when 50 °C < T ≤ 80 °C and , where: 50 °C is the lower temperature threshold for medium temperature, and E xmedium is the lower available energy threshold corresponding to medium temperature. Low-temperature thermal energy: when T ≤ 50 °C and E x ≤ E xmedium .

[0019] Furthermore, the energy cascade utilization strategy uses a fuzzy control algorithm to regulate the efficient transfer of the indoor heating medium. The input and output variables are defined. The input variables are the indoor temperature deviation e and the temperature deviation change rate Δe, and the output variable is the flow rate adjustment amount u of the heating medium. Fuzzy sets are defined, and the input variables e, Δe, and the output variable u are converted into fuzzy linguistic variables, "Negative Big" NB, "Negative Medium" NM, "Negative Small" NS, "Zero" ZO, "Positive Small" PS, "Positive Medium" PM, "Positive Big" PB. Fuzzy rules are formulated based on professional knowledge: if e is NL and Δe is NL, then u is PL; if e is NL and Δe is ZE, then u is PL. According to the fuzzy values of the input variables and the fuzzy rules, fuzzy inference is carried out to obtain the fuzzy value of the output variable. The fuzzy value of the output variable is converted into an accurate value of the flow rate through the centroid method. The centroid method calculation formula is: where: μu (x) is the membership function of the output variable, and x is the value of the variable.

[0020] Compared with the prior art, the multi-energy complementary heating system for near-zero energy consumption buildings has the following beneficial effects:

[0021] 1. The present invention uses a high-precision sensor to monitor the microclimate data of the building's surrounding environment in real time through a microclimate regulation and energy optimization integrated module, and uses the Pearson correlation coefficient analysis algorithm and the neural network algorithm to accurately extract the factors that have a key impact on the heating effect. Based on these key factors, the system can dynamically calculate the optimal heating strategy and parameters, significantly improve the operating efficiency of the heating system, and reduce unnecessary energy waste, thereby achieving significant energy-saving effects.

[0022] 2. The present invention receives and executes the instructions from the microclimate regulation and energy optimization integrated module in real time through the intelligent control and execution module, and accurately adjusts the operating parameters of each heating equipment through the PID control algorithm to ensure the optimization of the heating effect. In addition, the system also has self-learning and optimization capabilities, and can continuously optimize the control strategy based on operating data and user feedback, further improving the intelligence and automation level of the system.

[0023] 3. The present invention adopts an energy cascade utilization strategy and makes good use of high-precision monitoring and thermodynamic analysis to achieve accurate division and efficient utilization of thermal energy quality. High-temperature thermal energy is directly used for heating, and after high-efficiency heat exchanger and fuzzy control optimization, the heating efficiency and indoor comfort are significantly improved. Medium and low-temperature thermal energy is flexibly used in diversified scenarios such as swimming pool heating, industrial preheating, domestic hot water, and building dehumidification, thereby maximizing the utilization of energy. The waste heat recovery mechanism builds a closed-loop energy system, further improving energy utilization efficiency and environmental benefits. Overall, this strategy not only reduces building energy consumption, but also promotes the sustainable use of energy, providing strong support for the development of near-zero energy buildings.

[0024] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment 1

[0027] This embodiment describes in detail a near-zero energy building multi-energy complementary heating system, which aims to realize intelligent, efficient and energy-saving building heating by integrating four modules: microclimate regulation and energy optimization, multi-energy complementary heating, intelligent control and execution, and user interaction and feedback, so as to meet the heating needs under different microclimate conditions.

[0028] The integrated module of microclimate regulation and energy optimization deploys high-precision sensors around the building to collect microclimate data such as wind speed, wind direction, humidity, and solar radiation intensity in real time. These data are the basis for subsequent analysis and optimization strategy formulation. The collected data is transmitted to the system control center via wired or wireless means. In the control center, the data is first cleaned to remove noise and outliers to ensure data accuracy. The processed data is deeply analyzed using the Pearson correlation coefficient method. The calculation formula is: Among them, X and Y represent the observation value series of microclimate factors and heating effects, respectively. i and Y i is the corresponding observed value, and is the mean, n is the number of observations. According to the calculated correlation coefficient value, the microclimate factors correlated with the heating effect are identified as key factors, and the key factors that have a significant impact on the heating effect are extracted, such as sunshine duration, wind direction frequency, and temperature fluctuation range. Based on the extracted key factors and the heating demand inside the building, the system uses a neural network algorithm to calculate the optimal heating strategy and parameters. These parameters include the start and stop time, operating power, and working mode of the heating equipment, aiming to ensure the heating effect while maximizing energy efficiency.

[0029] The multi-energy complementary heating system module rationally configures a variety of heating equipment such as solar energy, geothermal energy, and air source heat pumps according to the building scale and heating needs. These devices are interconnected through the Internet of Things technology and connected to the system control center. By receiving the optimal heating strategy and parameters output by the microclimate regulation and energy optimization integrated module, the system automatically controls the operating parameters and working hours of each heating equipment. Through intelligent scheduling, it ensures that various energy sources are complementarily utilized to achieve the best heating effect and energy efficiency.

[0030] The intelligent control and execution module receives the heating strategies and parameter instructions sent by the integrated module for microclimate regulation and energy optimization in real time, parses the instruction content, clarifies the specific control requirements for each heating device, sends control signals to each heating device through the communication interface, and uses the PID control algorithm to adjust the operating parameters of the heating device according to the parameter instructions sent by the integrated module for microclimate regulation and energy optimization. Specifically, according to the deviation between the target temperature and the actual temperature of the heating system, as well as the historical data of this deviation, a control signal is calculated, and its calculation formula is: where, K p is the proportional gain, K i is the integral gain, K d is the derivative gain, e(t) is the deviation at time t, is the integral of the deviation, is the derivative of the deviation. According to the calculated control signal u(t), the operating parameters of the heating device are adjusted to achieve precise control of the device. At the same time, the system has remote control and fault diagnosis functions to ensure the stability and reliability of the device operation. And the system monitors the operating status and feedback information of each heating device, real-time monitors the operating status and performance indicators of the system, and uses self-learning algorithms to continuously optimize the control strategy to improve the adaptive ability and operating efficiency.

[0031] For the energy cascade utilization strategy, high-precision sensors are used to collect the temperature and flow data of each heat source device. According to the thermodynamic analysis algorithm, the quality of the thermal energy is calculated and judged, the thermodynamic parameters are determined, and the parameters of temperature T, pressure P, enthalpy H, and entropy S of the heat energy source are obtained. The available energy E x is calculated. Taking heat transfer as an example, the calculation formula of the available energy E x is: By comparing the relationship between the available energy E x and the temperature, the thermal energy is divided into different quality levels of high temperature, medium temperature, and low temperature. Temperature thresholds and available energy E x thresholds are set to automatically judge the quality of the thermal energy. High-temperature thermal energy: when T>80°C and E x >E xnigh where: 80°C is the temperature threshold for high temperature, and E xnigh is the available energy threshold corresponding to high temperature. Medium-temperature thermal energy: when 50°C < T ≤ 80°C and where: 50°C is the lower temperature threshold for medium temperature, and E xmedium is the lower available energy threshold corresponding to medium temperature. Low-temperature thermal energy: when T ≤ 50°C and E x ≤E xmediumWhen, high-temperature heat energy: directly input into the heating system, using high-efficiency heat exchanger, adjusting the flow of heating medium through fuzzy control algorithm, defining input and output variables, input variables are indoor temperature deviation e, temperature deviation change rate Δe, output variable is the flow adjustment amount u of heating medium, defining fuzzy set, converting input variables e, Δe and output variable u into fuzzy linguistic variables, "negative large" NB, "negative medium" NM, "negative small" NS, "zero" ZO, "positive small" PS, "positive medium" PM, "positive large" PB, formulating fuzzy rules based on experience and professional knowledge: if e is NL and Δe is NL, then u is PL, if e is NL and Δe is ZE, then u is PL, performing fuzzy reasoning based on the fuzzy value of input variable and fuzzy rules, obtaining the fuzzy value of output variable, converting the fuzzy value of output variable into precise value through centroid method, the centroid calculation formula is: Realize efficient indoor heating. Medium-temperature thermal energy: flexibly applied to swimming pool heating and industrial preheating scenarios according to actual needs. Low-temperature thermal energy: used for heating domestic hot water and dehumidification inside buildings. The waste heat generated in the process of thermal energy utilization is recovered through the heat recovery device. The recovered waste heat is redistributed to various heat source equipment and heating systems according to its quality and usable value, forming a closed-loop energy utilization system.

[0032] Furthermore, it also includes a user interaction and feedback module that provides an intuitive and easy-to-use operating interface, which allows users to set heating parameters according to their own needs, view heating effects and system operating status information, and the module displays system operating status, energy consumption data and other information to users in real time, helping users understand energy consumption during the heating process, thereby conducting more effective energy-saving management. At the same time, a user feedback channel is established to collect users' opinions and suggestions on the use of the system, and continuously optimize system functions and user experience.

[0033] In summary, through the near-zero energy building multi-energy complementary heating system of this embodiment, intelligent management and optimized scheduling of the heating process are realized. According to the real-time microclimate conditions and the heating demand inside the building, the heating strategy and parameters are automatically adjusted to ensure the heating effect while maximizing energy savings. In addition, the introduction of the user interaction and feedback module improves the transparency of the system and user participation, and promotes the formation of energy-saving behaviors.

[0034] Embodiment 2

[0035] On the basis of the first embodiment, a multi-energy complementary heating system for a near-zero energy consumption building in this embodiment particularly emphasizes the application of the energy cascade utilization strategy.

[0036] High-precision temperature sensors and flow sensors are deployed at the inlet and outlet of each heat source equipment (solar collector, ground source heat pump, air source heat pump) to collect temperature and flow data in real time.

[0037] Responsible for collecting sensor data, preprocessing (filtering, denoising) and storing it in the database, executing thermodynamic analysis algorithms, calculating the quality of thermal energy and classifying it into grades.

[0038] Calculate the available energy of the heat source, obtain the parameters of the heat source temperature, ambient reference temperature, pressure, enthalpy, and entropy, use the available energy calculation formula to evaluate the quality of thermal energy, and divide the thermal energy into three quality levels: high temperature, medium temperature, and low temperature according to the calculated available energy size and temperature range.

[0039] Define input variables (indoor temperature deviation e, temperature deviation change rate Δe) and output variables (flow adjustment amount u of heating medium), convert input variables and output variables into fuzzy linguistic variables (NB, NM, NS, ZO, PS, PM, PB), and formulate a fuzzy rule base based on experience and professional knowledge. "When the indoor temperature deviation is negative and the deviation change rate is negative, the heating medium flow adjustment amount should be positive". Through the fuzzy reasoning mechanism, according to the fuzzy value of the input variable and the fuzzy rule base, calculate the fuzzy value of the output variable, and use the center of gravity method to convert the fuzzy value of the output variable into an exact value to adjust the flow of the heating medium.

[0040] High-temperature thermal energy is directly input into the high-efficiency heat exchanger and efficiently transferred to the indoor heating system through optimized operating parameters and fuzzy control algorithm adjustment. Medium-temperature thermal energy is flexibly applied to swimming pool heating and industrial preheating scenarios according to actual needs, and low-temperature thermal energy is used for heating domestic hot water and dehumidification inside buildings.

[0041] Installed at the end of the thermal energy utilization process, it recovers waste heat and redistributes it to the energy utilization system based on its quality and usable value.

[0042] Embodiment 3

[0043] Based on the first embodiment, this embodiment describes in detail the strategy for optimizing the multi-energy complementary heating system of a near-zero energy building based on a neural network algorithm and a Q-learning algorithm.

[0044] The microclimate regulation and energy optimization integrated module arranges high-precision sensors around the building to collect microclimate data such as wind speed, wind direction, humidity, and solar radiation intensity in real time. The collected data is transmitted to the data storage unit of the system control center for temporary storage, and data cleaning is performed to remove noise and outliers to ensure the accuracy and reliability of the data. The cleaned microclimate data is deeply analyzed using the neural network algorithm, and the key factors that have a significant impact on the heating effect are extracted through the Pearson correlation coefficient method, such as sunshine duration, wind direction frequency, and temperature fluctuation range. Based on the extracted key factors and the heating demand inside the building, a neural network model is constructed, and the microclimate data vector and heating demand are input. The network weights and biases are continuously adjusted through the training process, and the optimal heating strategy and parameters are finally output. Specifically, the input layer receives the microclimate data vector x and the heating demand q, and the hidden layer performs a nonlinear transformation on the input data, where the output of the i-th hidden layer node is h i =f(∑ j w ij x j +b i ), where f is the activation function, w ij is the weight, b i is the bias, x j is the output of the input layer or the previous hidden layer. The output layer calculates the optimal heating strategy and parameter vector y=[y1,y2,…,y n ], where each y k Represents the parameters of a heating device and strategy, calculated as y k =g(∑ i v ki h i +c k ), where g is the activation function of the output layer, v ki is the output layer weight, c k To bias the output layer, the neural network adjusts the weight w through the training process ij 、v ki and bias b i 、c k , the optimal heating strategy and parameters are obtained, including the start and stop time, operating power and working mode of the heating equipment.

[0045] The multi-energy complementary heating system module is configured with a variety of heating equipment according to the building scale and heating needs to achieve interconnection between the equipment and access the system control center. By receiving the optimal heating strategy and parameters calculated by the neural network algorithm of the microclimate regulation and energy optimization integration module, it automatically adjusts the operating parameters and working hours of each heating equipment to ensure the optimization of the heating effect and the maximization of energy utilization.

[0046] The intelligent control and execution module receives and analyzes the heating strategies and parameter instructions sent by the microclimate regulation and energy optimization integration module in real time, clarifies the specific control requirements of each heating device, sends control signals to each heating device through the communication interface, ensures the accurate execution of the heating equipment, and monitors the operating status and feedback information of the equipment, adjusts the control strategy in real time to cope with changes in the external environment and changes in internal heating demand, and continuously optimizes the control strategy through the Q-learning algorithm. The calculation formula is: Q(s t ,a t )←Q(s t ,a t )+α[r t+1 +γmax a′ Q(s t+1 ,a′)-Q(s t ,a t )], where s t and a t are the system state and action taken at time t, r t+1 Is to perform action a t After the state s t Transfer to state s t+1 The instant reward obtained when α is the learning rate, γ is the discount factor, and max a′ Q(s t+1 ,a′) is in the new state s t+1 The Q value with the largest expected benefit among all possible actions is taken, and the control strategy of the heating system is gradually optimized through continuous iteration to improve the operating efficiency and performance of the system.

[0047] The user interaction and feedback module provides a user-friendly operation interface, allowing users to set heating requirements and view heating effects. Users can easily adjust heating parameters through this interface to meet personalized needs. At the same time, the module will provide real-time feedback to users on system operating status, energy consumption data and other information to help users understand the system's working conditions and energy consumption, and promote users' awareness of energy-saving management.

[0048] To sum up, through the optimization of the neural network algorithm of this embodiment, the heating strategy and parameter calculation of the multi-energy complementary heating system are more accurate and efficient, the system can better adapt to the complex and changeable microclimate conditions and heating needs inside the building, and achieve continuous optimization of the heating effect and maximization of energy utilization.

[0049] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

Claims

1. A multi-energy complementary heating system for a near-zero energy building, characterized in that: The system includes: Microclimate regulation and energy optimization integrated module: The module monitors the microclimate data of the building's surrounding environment in real time through high-precision sensors, processes the data using the built-in data analysis unit, extracts the key factors that affect the heating effect, and dynamically calculates the heating strategy and parameters based on the energy optimization algorithm, according to the current microclimate conditions and the heating demand inside the building; Intelligent control and execution module: Receives heating strategies and parameter instructions sent by the microclimate regulation and energy optimization integration module in real time, parses the instructions, clarifies the specific control requirements of each heating device, sends control signals to each heating device through the communication interface according to the parsed instructions, monitors the operating status and feedback information of the equipment, monitors the operating status and performance indicators of the system in real time, identifies potential problems and takes corresponding repair measures, and continuously optimizes the control strategy through self-learning algorithms; Multi-energy complementary heating system module: configure a variety of heating equipment according to the building scale and heating needs, so that the equipment can be interconnected and connected to the system control center for unified scheduling; receive the control signal sent by the intelligent control and execution module, and adjust the operating parameters and working hours of each heating equipment according to the strategy; Energy cascade utilization strategy: Use high-precision sensors to monitor the temperature and flow data of each heat source in real time to accurately determine the quality of thermal energy. High-temperature thermal energy is transferred to the indoor heating medium through a high-efficiency heat exchanger to heat the indoor temperature. Medium-temperature thermal energy is used in scenarios with higher temperature thermal energy demand, and low-temperature thermal energy is used to meet the daily living needs of building residents and for building dehumidification.

2. A multi-energy complementary heating system for a near-zero energy consumption building according to claim 1, characterized in that: The microclimate regulation and energy optimization integrated module uses the Pearson correlation coefficient analysis algorithm to extract key factors that affect the heating effect, including sunshine duration, wind direction frequency, and temperature fluctuation range. Based on the extracted key factors and the heating demand inside the building, the neural network algorithm is used to calculate the optimal heating strategy and parameters, including the start and stop time, operating power and working mode of the heating equipment.

3. A multi-energy complementary heating system for a near-zero energy building according to claim 2, characterized in that: The microclimate regulation and energy optimization integrated module uses the Pearson correlation coefficient analysis algorithm to extract the key factors that affect the heating effect, and its calculation formula is: Among them, X and Y represent the observation value series of microclimate factors and heating effects, respectively. i and Y i is the corresponding observed value, and is the mean, n is the number of observations, and based on the calculated correlation coefficient values, the microclimate factors that are correlated with the heating effect are identified as key factors.

4. The multi-energy complementary heating system for a near-zero energy building according to claim 1, characterized in that: The multi-energy complementary heating system module is configured with a variety of heating equipment according to the building scale and heating needs, so that the equipment can be interconnected and connected to the system control center for unified scheduling. At the same time, it receives the optimal heating strategy and parameters output by the microclimate regulation and energy optimization integration module, and adjusts the operating parameters and working hours of each heating equipment according to the strategy.

5. The multi-energy complementary heating system for a near-zero energy building according to claim 1, characterized in that: The intelligent control and execution module receives the heating strategies and parameter instructions sent by the microclimate regulation and energy optimization integration module in real time, parses the instructions, clarifies the specific control requirements of each heating equipment, sends control signals to each heating equipment through the communication interface according to the parsed instructions, monitors the operating status and feedback information of the equipment, monitors the operating status and performance indicators of the system in real time, identifies potential problems and takes corresponding repair measures, and continuously optimizes the control strategy through a self-learning algorithm.

6. The multi-energy complementary heating system for a near-zero energy consumption building according to claim 1, characterized in that: The intelligent control and execution module uses the PID control algorithm to adjust the operating parameters of the heating equipment according to the parameter instructions sent by the microclimate regulation and energy optimization integrated module. Specifically, according to the deviation between the target temperature and the actual temperature of the heating system and the historical data of the deviation, the control signal is calculated, and the calculation formula is: Among them, K p is the proportional gain, K i is the integral gain, K d is the differential gain, e(t) is the deviation at time t, is the integral of the deviation, It is the differential of the deviation. According to the calculated control signal u(t), the operating parameters of the heating equipment are adjusted.

7. A multi-energy complementary heating system for a near-zero energy consumption building according to claim 6, characterized in that: The intelligent control and execution module uses the Q-learning algorithm to continuously optimize the control strategy, and its calculation formula is: Q(s t ,a t )←Q(s t ,a t )+α[r t+1 +γmax a′ Q(s t+1 ,a′)-Q(s t ,a t )], where s t and a t are the system state and action taken at time t, r t+1 Is to perform action a t After the state s t Transfer to state s t+1 The instant reward obtained when α is the learning rate, γ is the discount factor, and max a′ Q(s t+1 ,a′) is in the new state s t+1 The Q value with the largest expected benefit among all actions is taken, and the control strategy of the heating system is gradually optimized through continuous iteration.

8. The multi-energy complementary heating system for a near-zero energy building according to claim 1, characterized in that: The energy cascade utilization strategy is equipped with high-precision temperature sensors and flow sensors to monitor the temperature and flow data of each heat source equipment in real time. Based on the collected temperature and flow data, the thermodynamic analysis algorithm is used to calculate and judge the quality of thermal energy, and the thermal energy is divided into different quality levels of high temperature, medium temperature and low temperature. High-temperature thermal energy is directly used for heating, and a high-efficiency heat exchanger is used to achieve efficient transfer to the indoor heating medium. The flow rate of the operating heating medium is adjusted by a fuzzy control algorithm. Medium-temperature thermal energy is flexibly applied to swimming pool heating and industrial preheating scenarios according to actual needs. Low-temperature thermal energy is used for heating domestic hot water and dehumidification inside buildings. The waste heat generated in the process of thermal energy utilization is recovered by a heat recovery device, and a closed-loop energy utilization system is re-formed according to quality and usable value.

9. The multi-energy complementary heating system for a near-zero energy consumption building according to claim 8, characterized in that: The above energy cascade utilization strategy uses a thermodynamic analysis algorithm to judge the quality of thermal energy, determine thermodynamic parameters, obtain the parameters of temperature T, pressure P, enthalpy H, and entropy S of the thermal energy source, and calculate the available energy E x , taking heat transfer as an example, the formula for calculating the available energy E x is as follows: Where: Q is the magnitude of heat transfer, T0 is the environmental reference temperature, and T is the temperature of the heat source. According to the calculated available energy magnitude and temperature range, the thermal energy quality level is divided. High-temperature thermal energy: when T > 80 °C and E x > E xnigh , where: 80 °C is the temperature threshold for high temperature, and E xnigh is the available energy threshold corresponding to high temperature. Medium-temperature thermal energy: when 50 °C < T ≤ 80 °C and , where: 50 °C is the lower temperature threshold for medium temperature, and E xmedium is the lower available energy threshold corresponding to medium temperature. Low-temperature thermal energy: when T ≤ 50 °C and E x ≤ E xmedium at this time.

10. The multi-energy complementary heating system for a near-zero energy consumption building according to claim 8, characterized in that: The energy cascade utilization strategy uses a fuzzy control algorithm to regulate the efficient transfer of indoor heating medium, defines input and output variables, the input variables are indoor temperature deviation e, temperature deviation change rate Δe, and the output variable is the flow adjustment amount u of the heating medium. Fuzzy sets are defined, and the input variables e, Δe, and output variables u are converted into fuzzy linguistic variables, "negative large" NB, "negative medium" NM, "negative small" NS, "zero" ZO, "positive small" PS, "positive medium" PM, "positive large" PB. Fuzzy rules are formulated based on professional knowledge: if e is NL and Δe is NL, then u is PL; if e is NL and Δe is ZE, then u is PL. Fuzzy reasoning is performed based on the fuzzy values ​​of the input variables and fuzzy rules to obtain the fuzzy values ​​of the output variables. The fuzzy values ​​of the output variables are converted into accurate flow values ​​through the centroid method. The centroid calculation formula is: Where: μ u (x) is the membership function of the output variable, and x is the value of the variable.

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