CN118816279A - Air source heat pump zero-electricity heating control method, device, equipment and storage medium - Google Patents

Abstract

The present invention provides a method, device, equipment and storage medium for controlling zero-electricity heating of an air source heat pump, wherein the method comprises: collecting and evaluating heat energy of multiple non-electric heat sources to obtain dynamic weight coefficients of the heat sources; performing intelligent scheduling and control processing on multiple non-electric heat sources according to the dynamic weight coefficients of the heat sources to obtain a dynamic scheduling plan for the heat sources; performing adaptive control processing on the working mode of the heat pump according to the dynamic scheduling plan for the heat sources to obtain working parameters of the heat pump; performing dynamic prediction and demand-side management processing on the user's heat load according to the working parameters of the heat pump to obtain a demand-side control strategy; performing parameter coordination and optimization control processing on the air source heat pump according to the demand-side control strategy to obtain heating control parameters, and performing zero-electricity heating control. This method maximizes the use of non-electric heat sources through intelligent scheduling and adaptive control, realizes zero-electricity heating of the air source heat pump, and improves the energy utilization efficiency and operational reliability of the system.

Description

Air source heat pump zero-electricity heating control method, device, equipment and storage medium

Technical Field

The present invention relates to the field of heating control, and in particular to a method, device, equipment and storage medium for controlling zero-electricity heating of an air source heat pump.

Background Art

With the intensification of the global energy crisis and the increasing prominence of environmental problems, finding efficient, energy-saving and environmentally friendly heating methods has become a research hotspot in the heating field. As an efficient heating technology, air source heat pumps have been widely used in recent years because they can use the heat in the ambient air to achieve heating, have a high energy efficiency ratio and good environmental friendliness. However, traditional air source heat pumps still rely on electric-driven compressors and related equipment to achieve heat transfer, which to a certain extent limits their energy-saving effects, especially in areas with high electricity costs or limited electricity supply, further promoting the demand for more efficient and energy-saving heating solutions.

Current technology has conducted a lot of research on improving the heating efficiency of air source heat pumps, including optimizing the structural design of heat pumps, improving compressor performance, and improving control strategies. However, even so, traditional air source heat pumps still rely heavily on electricity in cold weather or when the load is high, and cannot completely avoid electricity consumption, which limits energy efficiency and economy. Therefore, how to use non-electric heat sources to minimize the electricity consumption of air source heat pumps and achieve "zero-electricity heating" has become a difficult problem that needs to be solved urgently by current technology.

Some studies have attempted to reduce the electricity dependence of air source heat pumps by introducing non-electric heat sources such as solar energy, geothermal energy, and biomass energy. However, existing technologies still have deficiencies in the dynamic scheduling of non-electric heat sources and the adaptive control of heat pump operating modes, making it difficult for these technologies to achieve effective heat source utilization and system coordination, and unable to provide stable and reliable zero-electricity heating under different working conditions.

Summary of the invention

The main purpose of the present invention is to solve the technical problem that it is difficult to achieve effective heat source utilization and system coordination in the control of existing air source heat pumps.

A first aspect of the present invention provides an air source heat pump zero-electricity heating control method, the air source heat pump zero-electricity heating control method comprising:

Collect and evaluate the heat energy of multiple non-electric heat sources of air source heat pumps to obtain the dynamic weight coefficient of the heat source;

Perform intelligent scheduling and control processing on multiple non-electric heat sources according to the dynamic weight coefficients of the heat sources to obtain a dynamic scheduling plan for the heat sources;

According to the heat source dynamic scheduling plan, the heat pump operating mode is adaptively controlled to obtain the heat pump operating parameters;

Dynamically predict the user's heat load and perform demand-side management processing according to the heat pump operating parameters to obtain a demand-side control strategy;

The air source heat pump is subjected to parameter coordination optimization control processing according to the demand-side control strategy to obtain heating control parameters, and the air source heat pump is subjected to zero-electricity heating control according to the heating control parameters.

Optionally, in a first implementation of the first aspect of the present invention, the heat energy collection and evaluation processing of multiple non-electric heat sources of the air source heat pump to obtain the dynamic weight coefficient of the heat source includes:

Performing heat energy characteristic detection processing on multiple non-electric heat sources of the air source heat pump to obtain heat source characteristic data, wherein the heat source characteristic data includes heat source temperature, heat stability and heat size;

Performing heat energy availability analysis on multiple non-electric heat sources according to the heat source characteristic data to obtain a heat source availability index, wherein the heat source availability index includes available heat and thermal efficiency of the heat source;

Performing dynamic modeling processing on multiple non-electric heat sources according to the heat source availability index to obtain a heat source dynamic model, wherein the heat source dynamic model includes a prediction function of heat source performance changing over time;

A weight calculation process is performed on multiple non-electric heat sources according to the heat source dynamic model to obtain a heat source dynamic weight coefficient.

Optionally, in a second implementation of the first aspect of the present invention, performing intelligent scheduling control processing on multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain a dynamic scheduling plan for the heat source includes:

Performing availability prediction processing on multiple non-electric heat sources according to the heat source dynamic weight coefficient to obtain heat source availability prediction data, wherein the heat source availability prediction data includes the estimated available heat and thermal efficiency of each non-electric heat source in a future time period;

Analyze and process the historical heat consumption data of users to obtain a user heat demand prediction model, wherein the user heat demand prediction model is used to predict the change of user heat load in a future time period;

Optimizing the configuration of multiple non-electric heat sources according to the heat source availability prediction data and the user heating demand prediction model to obtain an initial heat source scheduling plan, wherein the initial heat source scheduling plan includes the usage ratio and duration of each non-electric heat source in different time periods;

The system thermal inertia and heat source switching cost are evaluated and processed according to the initial heat source scheduling scheme to obtain a heat source dynamic scheduling plan.

Optionally, in a third implementation of the first aspect of the present invention, the adaptive control processing of the heat pump operating mode according to the heat source dynamic scheduling plan to obtain the heat pump operating parameters includes:

According to the heat source dynamic scheduling plan, available working modes of the heat pump are identified and processed to obtain a heat pump mode set, wherein the heat pump mode set includes a direct heating mode, a heat pump warming mode, a cascade working mode and a heat storage mode;

Performing performance evaluation processing on each working mode in the heat pump mode set to obtain a mode performance index, wherein the mode performance index includes an energy efficiency ratio, heating capacity and response speed in each working mode;

Performing a suitability calculation process on each working mode according to the mode performance index to obtain a mode suitability matrix, wherein the mode suitability matrix represents the suitability of each working mode under different operating conditions;

The heat pump operating mode is dynamically selected according to the mode suitability matrix to obtain the heat pump operating parameters.

Optionally, in a fourth implementation of the first aspect of the present invention, the dynamically predicting the user's heat load and performing demand-side management processing according to the heat pump operating parameters to obtain a demand-side control strategy includes:

Extracting and processing the user's heat load characteristics according to the heat pump operating parameters and historical heat usage data to obtain a heat load characteristic vector, wherein the heat load characteristic vector includes daytime load distribution, periodic fluctuations and abnormal heat usage patterns;

Performing correlation analysis on the heat load characteristic vector and environmental factor data, predicting the user heat load in the future period, and obtaining a heat load prediction sequence, wherein the heat load prediction sequence includes heat load prediction values on different time scales;

The demand-side management measures are optimized according to the heat load forecast sequence to obtain a demand-side control strategy, which includes a preheating control scheme, a peak-valley regulation strategy, a regional differentiated heating scheme and a user participation incentive mechanism.

Optionally, in a fifth implementation of the first aspect of the present invention, performing parameter coordination optimization control processing on the air source heat pump according to the demand-side control strategy to obtain heating control parameters, and performing zero-electric heating control on the air source heat pump according to the heating control parameters includes:

Performing sensitivity analysis on air source heat pump system parameters according to the demand-side control strategy to obtain a parameter sensitivity matrix, wherein the parameter sensitivity matrix represents the degree of influence of each control parameter on system performance;

Performing principal component analysis on the parameter sensitivity matrix to obtain a key control parameter set, wherein the key control parameter set includes a parameter subset that has the most significant impact on system performance;

Performing multi-objective optimization processing on the air source heat pump system according to the key control parameter set to obtain a Pareto optimal solution set, wherein the Pareto optimal solution set includes optimal parameter combinations under different optimization objectives;

The optimal parameter combination is selected and processed according to the Pareto optimal solution set and the current system state to obtain the heating control parameters, and the heat source distribution, water supply temperature, circulating water flow rate and terminal equipment of the air source heat pump are coordinated and controlled according to the heating control parameters to achieve zero-electricity heating.

Optionally, in a sixth implementation of the first aspect of the present invention, after performing parameter coordination optimization control processing on the air source heat pump according to the demand-side control strategy to obtain heating control parameters, and performing zero-electric heating control on the air source heat pump according to the heating control parameters, it also includes:

The actual operation data of the air source heat pump is collected and processed to obtain an operation status data set, wherein the operation status data set includes the actual output of each non-electric heat source, the system energy efficiency ratio and the user comfort index;

Evaluate and process the zero-electricity heating control effect according to the operating status data set to obtain a control effect evaluation report;

Performing deviation analysis on the control effect evaluation report to obtain control deviation data, wherein the control deviation data represents the difference between the actual control effect and the expected target;

The control strategy is adaptively adjusted according to the control deviation data to obtain optimized control parameters, and the optimized control parameters are used for zero-electricity heating control in the next control cycle.

A second aspect of the present invention provides an air source heat pump zero-electricity heating control device, the air source heat pump zero-electricity heating control device comprising:

The heat source evaluation module is used to collect and evaluate the heat energy of multiple non-electric heat sources of the air source heat pump to obtain the dynamic weight coefficient of the heat source;

A scheduling module, used for performing intelligent scheduling and control processing on multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain a dynamic scheduling plan of the heat source;

An adaptive control module, used to perform adaptive control processing on the heat pump working mode according to the heat source dynamic scheduling plan to obtain the heat pump working parameters;

A strategy management module, used to dynamically predict the user's heat load and perform demand-side management processing according to the heat pump operating parameters to obtain a demand-side control strategy;

The heating control module is used to perform parameter coordination optimization control processing on the air source heat pump according to the demand-side control strategy, obtain heating control parameters, and perform zero-electricity heating control on the air source heat pump according to the heating control parameters.

The third aspect of the present invention provides an air source heat pump zero-electricity heating control device, comprising: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected through lines; the at least one processor calls the instructions in the memory to enable the air source heat pump zero-electricity heating control device to perform the steps of the above-mentioned air source heat pump zero-electricity heating control method.

A fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the steps of the above-mentioned air source heat pump zero-electric heating control method.

The above-mentioned air source heat pump zero-electric heating control method, device, equipment and storage medium obtain the dynamic weight coefficient of the heat source by collecting and evaluating the heat energy of multiple non-electric heat sources; perform intelligent scheduling and control on multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain the dynamic scheduling plan of the heat source; perform adaptive control on the heat pump working mode according to the dynamic scheduling plan of the heat source to obtain the heat pump working parameters; perform dynamic prediction and demand-side management on the user's heat load according to the heat pump working parameters to obtain the demand-side control strategy; perform parameter coordination and optimization control on the air source heat pump according to the demand-side control strategy to obtain the heating control parameters, and perform zero-electric heating control. This method maximizes the use of non-electric heat sources through intelligent scheduling and adaptive control, realizes zero-electric heating of air source heat pumps, and improves the energy utilization efficiency and operational reliability of the system.

Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a first embodiment of a zero-electricity heating control method for an air source heat pump according to an embodiment of the present invention;

FIG2 is a schematic diagram of an embodiment of an air source heat pump zero-electricity heating control device according to an embodiment of the present invention;

FIG3 is a schematic diagram of an embodiment of an air source heat pump zero-electricity heating control device in an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. 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.

The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

To facilitate understanding of this embodiment, a method for controlling zero-electricity heating of an air source heat pump disclosed in an embodiment of the present invention is first introduced in detail. As shown in FIG1 , this method includes the following steps:

101. Collect and evaluate the heat energy of multiple non-electric heat sources of the air source heat pump to obtain the dynamic weight coefficient of the heat source;

In one embodiment of the present invention, the heat energy collection and evaluation processing of multiple non-electric heat sources of the air source heat pump to obtain the dynamic weight coefficient of the heat source includes: performing thermal energy characteristic detection processing on the multiple non-electric heat sources of the air source heat pump to obtain heat source characteristic data, and the heat source characteristic data includes heat source temperature, thermal stability and heat size; performing thermal energy availability analysis processing on the multiple non-electric heat sources according to the heat source characteristic data to obtain a heat source availability index, and the heat source availability index includes available heat and thermal efficiency of the heat source; performing dynamic modeling processing on the multiple non-electric heat sources according to the heat source availability index to obtain a heat source dynamic model, and the heat source dynamic model includes a prediction function of heat source performance changing over time; performing weight calculation processing on the multiple non-electric heat sources according to the heat source dynamic model to obtain a heat source dynamic weight coefficient.

Specifically, during the process of detecting the thermal energy characteristics of multiple non-electric heat sources of the air source heat pump, the system continuously collects the temperature, flow and heat data of the heat source through the temperature sensors, flow meters and calorimeters installed on each non-electric heat source. The temperature data is collected by precise thermocouples or thermal resistor sensors, the flow data is obtained by turbine flowmeters or ultrasonic flowmeters, and the heat data is calculated by the heat calculation method based on the temperature difference and flow. These raw data are preliminarily processed by the data acquisition module, including filtering, calibration and digital conversion, to form a preliminary heat source characteristic data set. Next, the system performs further statistical analysis and processing on these preliminary data. The average, maximum, minimum and fluctuation range of the heat source temperature are calculated to characterize the temperature characteristics of the heat source. The thermal stability is quantified by calculating the standard deviation and coefficient of variation of the heat output, reflecting the degree of fluctuation of the heat source output. The heat size is represented by the accumulated heat and the average thermal power. These processed data constitute the complete heat source characteristic data, including the heat source temperature, thermal stability and heat size. Subsequently, the system performs thermal energy availability analysis and processing on multiple non-electric heat sources based on the heat source characteristic data. This step involves the application of thermodynamic principles. The system calculates the theoretical maximum thermal efficiency of each heat source based on the temperature level of the heat source and the Carnot efficiency formula. At the same time, considering the irreversible loss of the actual system, a correction factor is introduced to estimate the actual thermal efficiency. The calculation of available heat is based on the heat size of the heat source and the operating time of the system. The cumulative available heat in a certain period of time is obtained by integral calculation. These calculation results form the heat source availability index, including the available heat and thermal efficiency of the heat source. After the heat source availability index is generated, the system enters the dynamic modeling stage. This step uses machine learning algorithms such as support vector regression (SVR) or long short-term memory network (LSTM) to model the performance of each heat source over time. The input of the model includes historical heat source characteristic data, environmental parameters (such as outdoor temperature and humidity), and time series information (such as timestamps and seasonal factors). Through training, the model learns the time dependence of heat source performance and the influence of environmental factors, and finally generates a function that can predict the performance changes of heat sources in the future. This function constitutes a dynamic model of the heat source, which can predict the performance of the heat source at different time points and environmental conditions. Finally, the system performs weighted calculations on multiple non-electric heat sources based on the dynamic model of the heat source. This process takes into account multiple factors, including the predicted performance of the heat source, the current needs of the system, and the long-term optimization goals. Specifically, the system first predicts the performance curve of each heat source in the future (such as the next 24 hours) based on the dynamic model. Then, these performance curves are matched with the predicted system demand curve to calculate the contribution potential of each heat source in different time periods. At the same time, considering long-term optimization goals, such as balanced energy utilization or priority use of specific heat sources, the system introduces priority coefficients. Finally, by comprehensively considering short-term matching and long-term priority, the dynamic weight coefficient of each heat source is calculated using weighted average or more complex multi-criteria decision-making methods.

102. Perform intelligent scheduling and control processing on multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain a dynamic scheduling plan for the heat source;

In one embodiment of the present invention, the intelligent scheduling and control processing of multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain a heat source dynamic scheduling plan includes: performing availability prediction processing on multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain heat source availability prediction data, the heat source availability prediction data including the estimated available heat and thermal efficiency of each non-electric heat source in a future time period; analyzing and processing the user's historical heat usage data to obtain a user heating demand prediction model, the user heating demand prediction model is used to predict the user's thermal load changes in a future time period; optimizing the configuration of multiple non-electric heat sources according to the heat source availability prediction data and the user heating demand prediction model to obtain an initial heat source scheduling plan, the initial heat source scheduling plan including the usage ratio and duration of each non-electric heat source in different time periods; evaluating and processing the system thermal inertia and the heat source switching cost according to the initial heat source scheduling plan to obtain a heat source dynamic scheduling plan.

Specifically, the availability of multiple non-electric heat sources is predicted based on the dynamic weight coefficient of the heat source. In this step, the system uses the dynamic weight coefficient of the heat source obtained in the previous stage as input, combines historical data and current environmental parameters, and uses time series prediction algorithms (such as ARIMA or Prophet) to predict the performance of each non-electric heat source in the future time period. The prediction process takes into account the periodic changes, seasonal trends, and possible abnormalities of the heat source. The system models and predicts each heat source separately to obtain a series of time series data, including expected available heat and thermal efficiency. These data constitute the heat source availability prediction data, which provides a basis for subsequent scheduling optimization. Next, the system analyzes and processes the user's historical heat data to establish a user heating demand prediction model. This process involves the application of big data analysis and machine learning technology. The system first cleans and preprocesses the historical heat data to remove outliers and noise. Then, through the time series decomposition technology, the heat data is decomposed into trend, seasonal and residual components. These components are analyzed separately to extract key factors affecting heat demand, such as temperature, humidity, date type (weekday/weekend), etc. Based on these factors, the system builds a composite prediction model, which can use algorithms such as multi-layer perceptron (MLP) or random forest. This model can predict the changes in user heat load in the future time period based on the input environmental parameters and time information. Subsequently, the system optimizes the configuration of multiple non-electric heat sources based on the heat source availability prediction data and the user heating demand prediction model. This step uses complex optimization algorithms, such as genetic algorithms or particle swarm optimization. The objective function of the optimization process considers multiple factors, including meeting user heating needs, maximizing the utilization of non-electric heat sources, and minimizing energy waste. The optimization algorithm tries different heat source combination schemes through iterative calculations and evaluates the performance indicators of each scheme. In each iteration, the algorithm adjusts the use ratio and duration of the heat source according to the feedback of the objective function, and gradually converges to a better solution. The result of this optimization process is the initial heat source scheduling plan, which contains the specific use arrangements of each non-electric heat source in different time periods. Finally, the system evaluates the system thermal inertia and heat source switching cost based on the initial heat source scheduling plan. This step takes into account the physical constraints and economic factors in the actual system operation. First, the system established a thermodynamic model to simulate the thermal inertia characteristics of the entire heating system. This model takes into account the thermal capacity of the building, the heat loss of the pipe network, and the start-stop characteristics of each heat source. Through this model, the system can predict the difference between the actual heating effect and the ideal situation when executing the initial scheduling plan. At the same time, the system also established a cost model to calculate the relevant costs of heat source switching, including start-stop energy consumption, equipment wear and other factors. Based on these two models, the system corrected and optimized the initial scheduling plan. The optimization process uses a dynamic programming algorithm to balance the impact of thermal inertia and switching costs while meeting user needs, and obtain the final dynamic scheduling plan for the heat source. This plan not only includes the use of the heat source, but also includes specific control strategies after considering the dynamic characteristics of the system, such as preheating time, switching sequence, etc.

103. Performing adaptive control processing on the heat pump working mode according to the heat source dynamic scheduling plan to obtain heat pump working parameters;

In one embodiment of the present invention, the adaptive control processing of the heat pump working mode according to the heat source dynamic scheduling plan to obtain the heat pump working parameters includes: identifying the available working modes of the heat pump according to the heat source dynamic scheduling plan to obtain a heat pump mode set, the heat pump mode set including direct heating mode, heat pump warming mode, cascade working mode and heat storage mode; performing performance evaluation processing on each working mode in the heat pump mode set to obtain a mode performance index, the mode performance index includes the energy efficiency ratio, heating capacity and response speed under each working mode; performing applicability calculation processing on each working mode according to the mode performance index to obtain a mode suitability matrix, the mode suitability matrix represents the applicability of each working mode under different operating conditions; dynamically selecting the heat pump working mode according to the mode suitability matrix to obtain the heat pump working parameters.

Specifically, in the process of identifying and processing the available working modes of the heat pump according to the dynamic scheduling plan of the heat source, the system analyzes various parameters in the dynamic scheduling plan of the heat source, including the expected output temperature, heat and time distribution of each non-electric heat source. Based on this information, the system uses a preset decision tree algorithm to judge the available working modes of the heat pump. The nodes of the decision tree include heat source temperature threshold, heat demand threshold and time period characteristics. By traversing the decision tree, the system identifies the feasible working modes of the heat pump under given conditions. These modes constitute a set of heat pump modes, including direct heating mode, heat pump warming mode, cascade working mode and heat storage mode. The direct heating mode is suitable for situations with high heat source temperature, the heat pump warming mode is used to improve the quality of low-temperature heat sources, the cascade working mode is suitable for situations with large temperature differences, and the heat storage mode is used when the heat source supply is sufficient but the demand is low. Next, the system performs performance evaluation on each working mode in the heat pump mode set. This step involves the comprehensive application of thermodynamic models and experimental data. The system first establishes a detailed thermodynamic model of the heat pump, including the performance characteristics of core components such as compressors, evaporators, condensers and expansion valves. For each working mode, the system simulates and calculates the thermodynamic model to obtain theoretical performance data. At the same time, the system also uses historical operating data to calibrate and correct the model to ensure the accuracy of the model. The performance evaluation takes into account multiple key indicators, including energy efficiency ratio (COP), heating capacity and response speed. The energy efficiency ratio is calculated by the ratio of output heat to input power, the heating capacity is directly extracted from the model output, and the response speed is quantified by the time required for the simulation system to switch from one state to another. The results of these calculations and analyses constitute the mode performance indicators, which provide basic data for subsequent applicability evaluation. Subsequently, the system calculates the applicability of each working mode based on the mode performance indicators. This step uses fuzzy logic control theory and multi-criteria decision-making methods. First, the system defines a series of fuzzy rules that map performance indicators to applicability. For example, a rule such as "if the energy efficiency ratio is high and the heating capacity is sufficient, the applicability is high". Then, the system inputs specific performance indicator data into these fuzzy rules and obtains the applicability score of each working mode under different conditions through fuzzy reasoning. This process takes into account multiple factors, including current heat source conditions, user needs, ambient temperature, etc. By combining the suitability scores under different conditions, the system generates a mode suitability matrix. This matrix is a multidimensional data structure in which each element represents the degree of suitability of a specific operating mode under a specific combination of conditions. The dimensions of the matrix include key factors such as operating mode, heat source temperature range, load level, and ambient temperature. Finally, the system dynamically selects the heat pump operating mode based on the mode suitability matrix. This process uses a dynamic programming algorithm, the goal of which is to maximize the overall efficiency of the system while meeting the heating demand. The inputs to the algorithm include the current system state, the predicted future state changes, the mode suitability matrix, and various constraints (such as minimum operating time, switching number limit, etc.). The dynamic programming algorithm uses backtracking calculations to find an optimal mode switching sequence that can achieve the best overall performance over the entire forecast time period. Based on this optimal sequence, the system determines the most suitable operating mode at the current moment and calculates the corresponding heat pump operating parameters. These parameters include specific control quantities such as compressor speed, expansion valve opening, and circulating water flow. At the same time, the system also generates a short-term work plan, including the expected mode switching time points and the corresponding parameter adjustment strategies, so as to smoothly carry out the mode transition in the future.

104. Perform dynamic prediction and demand-side management processing on user heat load according to the heat pump operating parameters to obtain a demand-side control strategy;

In one embodiment of the present invention, the dynamic prediction and demand-side management of the user's heat load according to the heat pump working parameters to obtain the demand-side control strategy includes: extracting and processing the user's heat load characteristics according to the heat pump working parameters and historical heat usage data to obtain a heat load characteristic vector, the heat load characteristic vector includes daytime load distribution, periodic fluctuations and abnormal heat usage patterns; performing correlation analysis on the heat load characteristic vector and environmental factor data, predicting and processing the user's heat load in future time periods, and obtaining a heat load prediction sequence, the heat load prediction sequence includes heat load prediction values on different time scales; optimizing the demand-side management measures according to the heat load prediction sequence to obtain a demand-side control strategy, the demand-side control strategy includes a preheating control scheme, a peak-valley regulation strategy, a regional differentiated heating scheme and a user participation incentive mechanism. Extracting and processing the user's heat load characteristics according to the heat pump working parameters and historical heat usage data. In this process, the system integrates the real-time working parameters of the heat pump, such as water supply temperature, flow rate and heat pump operation mode, with the user's historical heat usage data. The system uses time series analysis techniques, such as wavelet transform and Fourier analysis, to process these data. Through wavelet transform, the system can capture the multi-scale characteristics of heat usage patterns, including short-term fluctuations and long-term trends. Fourier analysis is used to identify periodic patterns, such as daily cycles, weekly cycles, and seasonal changes. In addition, the system also uses clustering algorithms, such as K-means or DBSCAN, to identify abnormal heat usage patterns. The results of these processes are summarized to form a heat load feature vector, which contains the statistical characteristics of the daytime load distribution (such as peak time, load curve shape), the frequency and amplitude information of periodic fluctuations, and the type and frequency of abnormal heat usage patterns. Next, the system performs correlation analysis on the heat load feature vector and environmental factor data, and predicts the user heat load in the future period. This step involves the application of machine learning and statistical modeling techniques. First, the system collects and organizes environmental factor data, including outdoor temperature, humidity, wind speed, sunlight intensity, etc. Then, through correlation analysis and principal component analysis (PCA) and other methods, the environmental factors most related to the heat load are identified. Based on these analysis results, the system constructs a composite prediction model. This model can use deep learning techniques, such as long short-term memory networks (LSTM) or temporal convolutional networks (TCN), to simultaneously process time series data and multi-dimensional environmental factors. The model training process uses historical data sets and continuously adjusts model parameters through the back-propagation algorithm until the preset accuracy requirements are met. After training, the system inputs the current heat load feature vector and future environmental factor forecast data to generate a heat load forecast sequence. This forecast sequence includes forecast values at multiple time scales, from short-term (such as hourly forecasts for the next 24 hours) to medium- and long-term (such as daily average forecasts for the next week). Finally, the system optimizes demand-side management measures based on the heat load forecast sequence. This process uses multi-objective optimization algorithms, such as NSGA-II (non-dominated sorting genetic algorithm II). The optimization goals include minimizing energy consumption, balancing load curves, improving user comfort, and reducing operating costs. The system first defines a series of adjustable parameters, such as the heating temperature setpoints for each area, the start and stop time of the heat pump, and the charging and discharging strategies of the heat storage equipment. Then, based on the heat load forecast sequence, the system simulates the heating effect and energy consumption under different parameter combinations. Through iterative optimization, the system generates a set of Pareto optimal solutions, each of which represents a possible demand-side control strategy. From these solutions, the system selects the most suitable strategy as the final demand-side control strategy based on the current operating conditions and management preferences. This demand-side control strategy includes several key components. The preheating control scheme uses the thermal inertia of the building to increase the temperature in advance before the peak of heat demand arrives to reduce the load pressure during peak hours. The peak-valley regulation strategy achieves load shifting and peak-shaving and valley-filling by adjusting the heating intensity and time. The regional differentiated heating scheme takes into account the heat characteristics and needs of different regions and formulates personalized heating plans for each region. The user participation incentive mechanism encourages users to actively participate in demand-side management by designing a reasonable price mechanism and feedback system, such as using hot water or adjusting the indoor temperature set point during non-peak hours.

105. Perform parameter coordination optimization control processing on the air source heat pump according to the demand-side control strategy to obtain heating control parameters, and perform zero-electricity heating control on the air source heat pump according to the heating control parameters.

In one embodiment of the present invention, the parameter coordination optimization control processing of the air source heat pump according to the demand-side control strategy to obtain the heating control parameters, and the zero-electricity heating control of the air source heat pump according to the heating control parameters includes: performing sensitivity analysis processing on the air source heat pump system parameters according to the demand-side control strategy to obtain a parameter sensitivity matrix, wherein the parameter sensitivity matrix characterizes the degree of influence of each control parameter on the system performance; performing principal component analysis processing on the parameter sensitivity matrix to obtain a key control parameter set, wherein the key control parameter set includes a parameter subset with the most significant influence on the system performance; performing multi-objective optimization processing on the air source heat pump system according to the key control parameter set to obtain a Pareto optimal solution set, wherein the Pareto optimal solution set contains the optimal parameter combination under different optimization objectives; selecting the optimal parameter combination according to the Pareto optimal solution set and the current system state to obtain the heating control parameters, and coordinating the heat source distribution, water supply temperature, circulating water flow rate and terminal equipment of the air source heat pump according to the heating control parameters to achieve zero-electricity heating.

Specifically, in the process of sensitivity analysis of air source heat pump system parameters, the system uses a local sensitivity analysis method to make a small disturbance to each control parameter, and then observes the changes in system performance indicators. Control parameters include compressor frequency, expansion valve opening, fan speed, etc., while performance indicators include heating capacity, energy efficiency ratio (COP), system stability, etc. By calculating the performance change rate caused by each parameter disturbance, the system generates a parameter sensitivity matrix. This matrix is a two-dimensional array, with rows representing different control parameters, columns representing different performance indicators, and matrix elements representing the sensitivity of the parameters to the indicators. Next, the system performs principal component analysis on the parameter sensitivity matrix. This step uses the principal component analysis (PCA) algorithm to reduce the dimension of the parameter space and find the most critical control parameters. PCA converts the original parameters to a new orthogonal basis by calculating the eigenvalues and eigenvectors of the sensitivity matrix. On the new basis, the parameters are ranked according to their impact on system performance. The system selects the principal components whose cumulative contribution rate exceeds the preset threshold (such as 90%), and determines the original parameters corresponding to these principal components as the key control parameter set. This parameter set usually includes several parameters that have the most significant impact on system performance, such as compressor frequency, water supply temperature set point, etc. Subsequently, the system performs multi-objective optimization on the air source heat pump system based on the key control parameter set. This step uses a multi-objective optimization algorithm, such as NSGA-II (non-dominated sorting genetic algorithm II) or MOEA/D (multi-objective evolutionary algorithm based on decomposition). The optimization objective functions include maximizing system COP, meeting user heat demand, minimizing power consumption, etc. The algorithm first generates a set of initial solutions within the feasible domain of key control parameters, and then generates new solutions through genetic operations (such as crossover and mutation). Each solution is evaluated and sorted by non-dominated sorting and congestion calculation. After multiple iterations, the algorithm converges to a set of Pareto optimal solution sets. Each solution in this solution set represents a set of optimal parameter combinations under different optimization objectives. Finally, the system selects the optimal parameter combination based on the Pareto optimal solution set and the current system status. This process takes into account the real-time system operating status, environmental conditions and user needs. The system uses decision support algorithms, such as TOPSIS (Topic to Ideal Solution Ranking) or AHP (Analytic Hierarchy Process), to evaluate and rank the solutions in the Pareto optimal solution set. The evaluation criteria include proximity to the current operating state, switching cost, expected performance improvement, etc. Through this process, the system selects the most suitable parameter combination for the current situation as the heating control parameter. Based on the obtained heating control parameters, the system coordinates and controls the various subsystems of the air source heat pump to achieve zero-electric heating. Specifically, the heat source allocation control adjusts the usage ratio and switching timing of each non-electric heat source according to the heating control parameters to ensure the maximum utilization of renewable energy. The water supply temperature control accurately controls the outlet water temperature by adjusting the operating parameters of the heat pump (such as the compressor frequency), which not only meets user needs but also avoids overheating. The circulating water flow control adjusts the system flow through the variable frequency water pump to minimize the pump power consumption while ensuring heat transfer. The terminal equipment control adjusts the fan coil speed or the floor heating pipeline valve opening according to the needs of different areas to achieve refined indoor temperature control.

Furthermore, after the air source heat pump is subjected to parameter coordination optimization control processing according to the demand-side control strategy to obtain heating control parameters, and the air source heat pump is subjected to zero-electricity heating control according to the heating control parameters, it also includes: collecting and processing actual operating data of the air source heat pump to obtain an operating status data set, wherein the operating status data set includes the actual output of each non-electric heat source, the system energy efficiency ratio and the user comfort index; evaluating and processing the zero-electricity heating control effect according to the operating status data set to obtain a control effect evaluation report; performing deviation analysis processing on the control effect evaluation report to obtain control deviation data, wherein the control deviation data characterizes the difference between the actual control effect and the expected target; and adaptively adjusting the control strategy according to the control deviation data to obtain optimized control parameters, wherein the optimized control parameters are used for zero-electricity heating control in the next control cycle.

Specifically, in the process of collecting and processing the actual operation data of the air source heat pump, the system continuously collects operation data through a sensor network distributed in key parts of the heat pump. These sensors include temperature sensors, flow meters, power meters, and pressure sensors, which are installed at the heat source output end, system pipelines, compressors, and terminal equipment. The collected raw data is preliminarily processed by the data acquisition unit, including signal filtering to remove noise, outlier detection and elimination, and data standardization. The processed data is integrated into the operation status data set, which contains the actual output of each non-electric heat source (such as the heat output of the solar collector, the heat exchange of the ground source heat pump), the overall energy efficiency ratio (COP) of the system, and indicators reflecting user comfort (such as indoor temperature deviation and temperature fluctuation amplitude). These data are recorded at fixed time intervals (such as every 5 minutes) to form a time series database. Next, the system evaluates and processes the zero-electric heating control effect based on the operation status data set. The evaluation process adopts a multi-dimensional analysis method. First, the statistical characteristics of each performance indicator are calculated, such as the mean, standard deviation, maximum, and minimum. Then, the trend and periodic changes of performance indicators are identified through time series analysis techniques, such as moving average and exponential smoothing. The system also uses data visualization technology to generate time series graphs and correlation heat maps of various performance indicators to intuitively display the system operation status. In addition, the system compares the actual operation data with the predicted results of the theoretical model and calculates the deviation between the prediction error and the actual effect. Based on these analyses, the system generates a detailed control effect evaluation report, which includes the quantitative analysis results of various performance indicators, the stability evaluation of system operation, the analysis of energy utilization efficiency, and the feedback of user comfort. Subsequently, the system performs deviation analysis on the control effect evaluation report. This step uses statistical analysis and machine learning techniques to first define a series of key performance indicators (KPIs), such as energy utilization rate, heating stability index, and user satisfaction. Then, for each KPI, the deviation between the actual value and the expected target value is calculated. Through principal component analysis (PCA) or factor analysis, the system identifies the main factors causing the deviation. At the same time, the system uses decision trees or random forest algorithms to establish a mapping relationship between the deviation and each control parameter to understand the degree of influence of different parameters on system performance. These analysis results are integrated into control deviation data, which includes not only the deviation values of each KPI, but also the time characteristics, spatial distribution and main influencing factors of the deviation. Finally, the system adaptively adjusts the control strategy according to the control deviation data. This process uses reinforcement learning algorithms such as Q-learning or deep deterministic policy gradient (DDPG). The system uses control deviation data as part of the environment state and the adjustment of control parameters as the action space. Through continuous interaction and learning with the environment, the system gradually optimizes its control strategy. Specifically, the system first explores possible parameter adjustment schemes in the action space based on the current control deviation. Each adjustment scheme is evaluated for its potential effect through the simulation model and the corresponding reward value is calculated. Through multiple iterations, the system continuously updates its value function or policy network and gradually finds the parameter adjustment scheme that can minimize the control deviation. The optimized control parameters include not only direct control variables (such as compressor frequency, valve opening, etc.), but also meta-parameters of the control strategy (such as the gain coefficient of the PID controller, the prediction time domain of the model predictive control, etc.). These parameters are used for zero-electric heating control in the next control cycle to form a closed-loop adaptive control system. Through this continuous monitoring, evaluation and optimization process, the system can continuously improve its control performance and adapt to the changing operating environment and user needs, thereby achieving more efficient and stable zero-electricity heating control.

In this embodiment, the heat source dynamic weight coefficient is obtained by collecting and evaluating the heat energy of multiple non-electric heat sources; the multiple non-electric heat sources are intelligently dispatched and controlled according to the dynamic weight coefficient of the heat source to obtain the dynamic dispatch plan of the heat source; the heat pump working mode is adaptively controlled according to the dynamic dispatch plan of the heat source to obtain the heat pump working parameters; the user's heat load is dynamically predicted and the demand side management is processed according to the heat pump working parameters to obtain the demand side control strategy; the air source heat pump is parameter coordinated and optimized according to the demand side control strategy to obtain the heating control parameters, and zero-electric heating control is performed. This method maximizes the use of non-electric heat sources through intelligent dispatching and adaptive control, realizes zero-electric heating of air source heat pumps, and improves the energy efficiency and operation reliability of the system.

The above describes the zero-electricity heating control method of the air source heat pump in the embodiment of the present invention. The following describes the zero-electricity heating control device of the air source heat pump in the embodiment of the present invention. Please refer to Figure 2. An embodiment of the zero-electricity heating control device of the air source heat pump in the embodiment of the present invention includes:

The heat source evaluation module 201 is used to collect and evaluate the heat energy of multiple non-electric heat sources of the air source heat pump to obtain the dynamic weight coefficient of the heat source;

The scheduling module 202 is used to perform intelligent scheduling control processing on multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain a dynamic scheduling plan of the heat source;

The adaptive control module 203 is used to perform adaptive control processing on the heat pump working mode according to the heat source dynamic scheduling plan to obtain the heat pump working parameters;

A strategy management module 204 is used to dynamically predict the user's heat load and perform demand-side management processing according to the heat pump operating parameters to obtain a demand-side control strategy;

The heating control module 205 is used to perform parameter coordination optimization control processing on the air source heat pump according to the demand-side control strategy, obtain heating control parameters, and perform zero-electricity heating control on the air source heat pump according to the heating control parameters.

In an embodiment of the present invention, the air source heat pump zero-electricity heating control device runs the above-mentioned air source heat pump zero-electricity heating control method, and the air source heat pump zero-electricity heating control device collects and evaluates heat energy from multiple non-electric heat sources to obtain a dynamic weight coefficient of the heat source; performs intelligent scheduling and control on multiple non-electric heat sources according to the dynamic weight coefficient of the heat source to obtain a dynamic scheduling plan for the heat source; performs adaptive control on the heat pump working mode according to the dynamic scheduling plan for the heat source to obtain the heat pump working parameters; performs dynamic prediction and demand-side management on the user's heat load according to the heat pump working parameters to obtain a demand-side control strategy; performs parameter coordination and optimization control on the air source heat pump according to the demand-side control strategy to obtain heating control parameters, and performs zero-electricity heating control. This method maximizes the use of non-electric heat sources through intelligent scheduling and adaptive control, realizes zero-electricity heating of air source heat pumps, and improves the energy utilization efficiency and operational reliability of the system.

FIG2 above describes in detail the zero-electricity heating control device of the air source heat pump in the embodiment of the present invention from the perspective of modular functional entity. The following describes in detail the zero-electricity heating control device of the air source heat pump in the embodiment of the present invention from the perspective of hardware processing.

FIG3 is a schematic diagram of the structure of an air source heat pump zero-electric heating control device provided by an embodiment of the present invention. The air source heat pump zero-electric heating control device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the air source heat pump zero-electric heating control device 300. Furthermore, the processor 310 can be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the air source heat pump zero-electric heating control device 300 to implement the steps of the above-mentioned air source heat pump zero-electric heating control method.

The air source heat pump zero-electricity heating control device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and/or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that the air source heat pump zero-electricity heating control device structure shown in FIG3 does not constitute a limitation on the air source heat pump zero-electricity heating control device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the air source heat pump zero-electric heating control method.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device or unit described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. The zero-electricity heat supply control method for the air source heat pump is characterized by comprising the following steps of:

Performing heat energy collection and evaluation treatment on a plurality of non-electric heat sources of the air source heat pump to obtain a heat source dynamic weight coefficient;

performing intelligent scheduling control processing on a plurality of non-electric heat sources according to the heat source dynamic weight coefficient to obtain a heat source dynamic scheduling plan;

Performing self-adaptive control processing on a heat pump working mode according to the heat source dynamic scheduling plan to obtain heat pump working parameters;

carrying out dynamic prediction and demand side management processing on the heat load of a user according to the heat pump working parameters to obtain a demand side control strategy;

And carrying out parameter coordination optimization control processing on the air source heat pump according to the demand side control strategy to obtain a heat supply control parameter, and carrying out zero-electricity heat supply control on the air source heat pump according to the heat supply control parameter.

2. The method for zero-electricity heat supply control of an air source heat pump according to claim 1, wherein the performing heat energy collection and evaluation processing on the plurality of non-electric heat sources of the air source heat pump to obtain a heat source dynamic weight coefficient comprises:

Performing thermal energy characteristic detection processing on a plurality of non-electric heat sources of the air source heat pump to obtain heat source characteristic data, wherein the heat source characteristic data comprises heat source temperature, heat stability and heat magnitude;

Performing heat energy availability analysis processing on a plurality of non-electric heat sources according to the heat source characteristic data to obtain heat source availability indexes, wherein the heat source availability indexes comprise available heat and heat efficiency of the heat sources;

carrying out dynamic modeling processing on a plurality of non-electric heat sources according to the heat source availability index to obtain a heat source dynamic model, wherein the heat source dynamic model comprises a prediction function of heat source performance changing along with time;

and carrying out weight calculation processing on a plurality of non-electric heat sources according to the heat source dynamic model to obtain a heat source dynamic weight coefficient.

3. The method for controlling zero electric heating of an air source heat pump according to claim 1, wherein the performing intelligent scheduling control processing on the plurality of non-electric heat sources according to the heat source dynamic weight coefficient to obtain a heat source dynamic scheduling plan comprises:

carrying out availability prediction processing on a plurality of non-electric heat sources according to the heat source dynamic weight coefficient to obtain heat source availability prediction data, wherein the heat source availability prediction data comprises predicted available heat and heat efficiency of each non-electric heat source in a future time period;

analyzing and processing the historical heat consumption data of the user to obtain a user heat supply demand prediction model, wherein the user heat supply demand prediction model is used for predicting the user heat load change in a future time period;

Performing optimal configuration processing on a plurality of non-electric heat sources according to the heat source availability prediction data and the user heat supply demand prediction model to obtain an initial heat source scheduling scheme, wherein the initial heat source scheduling scheme comprises the use proportion and the use duration of each non-electric heat source in different time periods;

and evaluating the thermal inertia of the system and the heat source switching cost according to the initial heat source scheduling scheme to obtain a heat source dynamic scheduling plan.

4. The method for controlling zero electric heating of an air source heat pump according to claim 1, wherein the adaptively controlling the heat pump working mode according to the heat source dynamic scheduling plan, and obtaining the heat pump working parameters comprises:

Identifying and processing the available working modes of the heat pump according to the heat source dynamic scheduling plan to obtain a heat pump mode set, wherein the heat pump mode set comprises a direct heat supply mode, a heat pump heating mode, a cascade working mode and a heat storage mode;

Performing performance evaluation processing on each working mode in the heat pump mode set to obtain a mode performance index, wherein the mode performance index comprises an energy efficiency ratio, heating capacity and response speed under each working mode;

performing applicability calculation processing on each working mode according to the mode performance index to obtain a mode applicability matrix, wherein the mode applicability matrix represents the applicability of each working mode under different running conditions;

And dynamically selecting and processing the heat pump working mode according to the mode suitability matrix to obtain heat pump working parameters.

5. The method for zero-electricity heat supply control of an air source heat pump according to claim 1, wherein the dynamically predicting the heat load of the user and managing the heat load on the demand side according to the heat pump working parameters, and obtaining the control strategy on the demand side comprises:

Extracting and processing the heat load characteristics of a user according to the heat pump working parameters and the historical heat data to obtain heat load characteristic vectors, wherein the heat load characteristic vectors comprise daytime load distribution, periodic fluctuation and abnormal heat mode;

Performing association analysis processing on the thermal load characteristic vector and the environmental factor data, and performing prediction processing on the thermal load of the user in a future period to obtain a thermal load prediction sequence, wherein the thermal load prediction sequence comprises thermal load prediction values on different time scales;

And optimizing the management measures of the demand side according to the thermal load prediction sequence to obtain a control strategy of the demand side, wherein the control strategy of the demand side comprises a preheating control scheme, a peak-valley regulation strategy, a regional differentiated heating scheme and a user participation excitation mechanism.

6. The zero-electricity heat supply control method of an air source heat pump according to claim 1, wherein the performing parameter coordination optimization control processing on the air source heat pump according to the demand side control strategy to obtain a heat supply control parameter, and performing zero-electricity heat supply control on the air source heat pump according to the heat supply control parameter comprises:

Performing sensitivity analysis processing on the parameters of the air source heat pump system according to the demand side control strategy to obtain a parameter sensitivity matrix, wherein the parameter sensitivity matrix characterizes the influence degree of each control parameter on the system performance;

Performing principal component analysis processing on the parameter sensitivity matrix to obtain a key control parameter set, wherein the key control parameter set comprises a parameter subset with the most obvious influence on system performance;

performing multi-objective optimization processing on the air source heat pump system according to the key control parameter set to obtain a Pareto optimal solution set, wherein the Pareto optimal solution set comprises optimal parameter combinations under different optimization targets;

And selecting and processing the optimal parameter combination according to the Pareto optimal solution set and the current system state to obtain a heat supply control parameter, and carrying out coordinated control on heat source distribution, water supply temperature, circulating water flow and terminal equipment of the air source heat pump according to the heat supply control parameter to realize zero-electricity heat supply.

7. The zero-electricity heat supply control method of an air source heat pump according to claim 1, wherein after performing parameter coordination optimization control processing on the air source heat pump according to the demand side control strategy to obtain a heat supply control parameter, and performing zero-electricity heat supply control on the air source heat pump according to the heat supply control parameter, the method further comprises:

Acquiring and processing actual operation data of the air source heat pump to obtain an operation state data set, wherein the operation state data set comprises actual output of each non-electric power heat source, system energy efficiency ratio and user comfort index;

performing evaluation processing on the zero electric heating control effect according to the running state data set to obtain a control effect evaluation report;

Performing deviation analysis processing on the control effect evaluation report to obtain control deviation data, wherein the control deviation data represents the difference between the actual control effect and an expected target;

and carrying out self-adaptive adjustment processing on the control strategy according to the control deviation data to obtain optimized control parameters, wherein the optimized control parameters are used for zero-electricity heat supply control of the next control period.

8. An air source heat pump zero-electricity heat supply control device, which is characterized by comprising:

The heat source evaluation module is used for carrying out heat energy acquisition and evaluation treatment on a plurality of non-electric heat sources of the air source heat pump to obtain a heat source dynamic weight coefficient;

The scheduling module is used for performing intelligent scheduling control processing on a plurality of non-electric heat sources according to the heat source dynamic weight coefficient to obtain a heat source dynamic scheduling plan;

the self-adaptive control module is used for carrying out self-adaptive control processing on the heat pump working mode according to the heat source dynamic scheduling plan to obtain heat pump working parameters;

the strategy management module is used for carrying out dynamic prediction and demand side management processing on the heat load of the user according to the heat pump working parameters to obtain a demand side control strategy;

And the heat supply control module is used for carrying out parameter coordination optimization control processing on the air source heat pump according to the demand side control strategy to obtain heat supply control parameters, and carrying out zero-electricity heat supply control on the air source heat pump according to the heat supply control parameters.

9. An air source heat pump zero-power heat supply control device, characterized in that the air source heat pump zero-power heat supply control device comprises: a memory and at least one processor, the memory having instructions stored therein;

The at least one processor invoking the instructions in the memory to cause the air source heat pump zero electric heating control device to perform the steps of the air source heat pump zero electric heating control method of any of claims 1-7.

10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the steps of the air source heat pump zero electric heating control method according to any one of claims 1-7.