A photothermal coupling air source heat pump heating control method

Abstract

This invention belongs to the field of heating control technology, and particularly relates to a heating control method for a solar-thermal coupled air source heat pump. The method includes: acquiring real-time temperature data and solar radiation intensity data from environmental sensors; analyzing these data to determine the current low-temperature environment level and the fluctuation trend of solar energy collection, thus obtaining environmental change parameters; based on equipment power adjustment requirements, acquiring the current output data of the solar energy collection module and the real-time heat distribution status of the main heating equipment, determining whether the two are in an unbalanced state, and obtaining a dynamic coordination index; based on the dynamic coordination index, using a support vector machine algorithm to process historical energy consumption records and operating load data, determining an optimized heat distribution scheme, and obtaining a path to improve energy efficiency; based on the heating efficiency correction value, using a random forest algorithm to analyze the risk of insufficient heat in a low-temperature environment, determining the final energy consumption reduction configuration, and obtaining the overall system operating status.

Description

Technical Field

[0001] This invention belongs to the field of heating control technology, and particularly relates to a heating control method for a photothermal coupled air source heat pump. Background Technology

[0002] In the field of energy efficiency and heating system optimization, improving the operating efficiency of heating systems and reducing energy consumption are important research directions for addressing energy shortages, alleviating environmental pollution, and promoting green and low-carbon development. They are also directly related to the living comfort of residents in cold regions and the sustainable use of energy resources.

[0003] Currently, most mainstream heating methods in cold regions employ single-energy supply or simple energy combination modes, which suffer from numerous technical defects in actual operation, making it difficult to meet the demands for efficient, stable, and energy-saving heating. Specifically, existing heating systems generally lack the ability to dynamically respond to external environmental parameters (such as ambient temperature and solar radiation intensity), and cannot adjust the system's operating status in real time according to environmental changes, easily leading to energy waste and substandard heating effects.

[0004] Crucially, existing systems lack the capacity for comprehensive utilization of multiple energy forms, especially in coupled solar thermal and air source heat pump heating systems. As a clean and renewable energy source, solar energy collection fluctuates significantly due to factors such as weather and time of day. If dynamic coordinated control of solar energy collection and air source heat pump operation cannot be achieved, energy distribution imbalance is highly likely. When there is sufficient sunshine, the amount of solar energy collected is excessive but cannot be effectively utilized, resulting in idle energy. When the sunshine intensity is insufficient (such as on cloudy days or at night) or in extreme low temperature environments, the amount of solar energy collected drops sharply, and the air source heat pump needs to operate at high power to make up for the heat gap. This not only significantly increases energy consumption but also exacerbates equipment wear and shortens equipment lifespan.

[0005] In summary, existing solar-thermal coupled air source heat pump heating systems suffer from poor energy synergy, weak environmental adaptability, low operating efficiency, and high energy consumption. They cannot achieve efficient synergistic operation of solar energy and air source heat pumps under complex and variable operating conditions, and it is difficult to balance heating stability and energy saving. Therefore, developing a heating control method that can dynamically coordinate energy distribution and adapt to different operating conditions has become an urgent technical challenge to be solved in this field. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a heating control method for a photothermal coupled air source heat pump, which solves the technical problems of poor energy synergy, weak low-temperature adaptability, low heating efficiency, and high energy consumption and equipment loss in existing photothermal coupled air source heat pump systems, thereby improving overall energy efficiency and heating reliability.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: A method for controlling the heating operation of a photothermal coupled air source heat pump, the method comprising the following steps: Step S1: Collect real-time ambient temperature data and solar radiation intensity data through environmental sensors, analyze the real-time ambient temperature data and solar radiation intensity data, determine the current low temperature environment level and the fluctuation trend of solar energy collection, and then generate dynamic parameters of environmental change. Step S2: Based on the dynamic parameters of environmental changes, determine whether the solar energy collection is lower than the preset normal level according to the preset threshold; if the solar energy collection is lower than the preset normal level, retrieve historical operating load data from the main heating equipment database, and determine the power adjustment requirements of the main heating equipment by analyzing the historical operating load data. Step S3: Based on the power adjustment requirements of the main heating equipment, collect the current output parameters of the solar energy acquisition module and the real-time heat distribution status of the main heating equipment, determine whether the current output parameters of the solar energy acquisition module and the real-time heat distribution status of the main heating equipment are out of balance, and generate dynamic coordination indicators. Step S4: Based on the dynamic coordination index, the support vector machine algorithm is used to process the historical energy consumption data and operating load data to construct a heat distribution optimization scheme, thereby determining the path to improve energy efficiency. Step S5: Along the energy-saving level improvement path, retrieve the indoor temperature maintenance record from the user experience feedback system and determine whether the indoor temperature corresponding to the indoor temperature maintenance record is within the preset comfort range; if it is not within the preset comfort range, adjust the power of the main heating equipment and the solar energy collection rhythm to obtain the heating efficiency correction value. Step S6: Based on the heating efficiency correction value, the random forest algorithm is used to analyze the risk of insufficient heat supply in low temperature environment, determine the final energy consumption reduction configuration parameters, and then obtain the overall system operating status. Step S7: Based on the overall system operating status, collect real-time monitoring data of equipment loss and determine whether the dynamic coordination between the solar energy acquisition module and the main heating equipment has reached a stable state; if a stable state has been reached, update the heat distribution model and output the user experience optimization results.

[0008] Furthermore, step S1 specifically includes: Real-time ambient temperature and solar radiation intensity data are continuously collected by environmental sensors. Data is collected according to a preset sampling frequency to generate an original environmental dataset. The original environmental dataset is cleaned to remove outliers and noise interference, resulting in purified temperature and solar radiation intensity data. Based on the purified temperature data and combined with the preset temperature threshold range, if the purified temperature data is lower than the lower limit of the preset temperature threshold range, the current environment is determined to be a low-temperature environment, and the classification of the low-temperature environment level is completed to obtain the low-temperature environment level classification result. By combining the low-temperature environment classification results with the purified solar intensity data, the variation law of solar intensity over time is analyzed to determine the fluctuation trend of solar energy collection. Time series analysis is used to extract features from the fluctuation trend of solar energy collection, obtain fluctuation period and fluctuation amplitude characteristic parameters, and generate dynamic parameters of environmental change. Based on the aforementioned dynamic parameters of environmental change, combined with the low temperature environment level and the fluctuation trend of solar energy collection, a comprehensive assessment of the current environmental state is conducted to determine the degree of impact of environmental changes on solar energy collection efficiency. Based on the degree of impact of environmental changes on solar energy collection efficiency, an environmental adaptation strategy is generated, and the key directions and monitoring parameters for subsequent environmental monitoring are determined.

[0009] Furthermore, step S2 specifically includes: Based on the dynamic parameters of environmental changes and combined with the preset solar energy collection threshold range, the real-time collection volume of the solar energy collection module is dynamically monitored to determine whether it is lower than the preset normal level. If the solar energy collection is lower than the preset normal level, the historical operating load data and operating parameters of the main heating unit are retrieved from the main heating equipment database, and the time series features of the historical operating load data and operating parameters are extracted. The historical operating load data and operating parameters of the main heating engine are classified and processed using the support vector machine algorithm to construct a classification model of the main heating engine's operating mode, thereby obtaining a preliminary basis for power adjustment. Based on the preliminary basis for the power adjustment, and combined with real-time monitoring data of environmental changes, the current operating status of the main heating unit is dynamically matched to determine the specific range of the main heating unit power adjustment. Based on the specific range of the power adjustment and in conjunction with the operating parameter setting requirements of the main heating equipment, the control parameters of the main heating unit are updated to generate the adjusted operating configuration parameters. Based on the adjusted operating configuration parameters, the actual operating status of the main heat engine is continuously tracked and monitored to determine whether its operating status meets the preset power adjustment target. If the actual operating status of the main heating unit does not meet the preset power adjustment target, then historical operating load data for a longer period will be retrieved from the main heating equipment database, and a new power adjustment direction will be determined through data analysis.

[0010] Furthermore, step S3 specifically includes: By using a pre-defined data acquisition process, the current output parameters are extracted from the solar energy acquisition module, and the real-time heat distribution status parameters are retrieved from the operation record of the main heating engine to build a basic data comparison set between the two. Based on the preset balance judgment criteria, the current output parameters of the solar energy acquisition module in the basic data comparison set are compared and analyzed with the real-time heat distribution status parameters of the main heat engine to determine whether the two are in a matching state and obtain a preliminary balance judgment result. If the preliminary balance judgment result is that the current output parameters of the solar energy acquisition module do not match the real-time heat distribution status parameters of the main heat generator, then the degree of deviation between the two is quantified by the dynamic index calculation tool to obtain specific dynamic coordination indicators. Based on the aforementioned dynamic coordination index, the real-time operating status data of the main heat engine is retrieved, and combined with the preset power adjustment demand logic, the priority of heat distribution is sorted to determine the reference basis for the direction of parameter adjustment. Based on the reference criteria for the adjustment direction of the parameters, and combined with the current power load of the main heating equipment, the heat distribution parameters of the main heating unit are dynamically updated to generate a new operating configuration scheme. Based on the new operating configuration scheme, the real-time operating status of the main heating engine and the changes in the output parameters of the solar energy acquisition module are continuously monitored to determine whether the two are gradually approaching a state of equilibrium, and to obtain the final coordination result.

[0011] Furthermore, step S4 specifically includes: Based on the aforementioned dynamic coordination indicators, key data are extracted from historical energy consumption records and combined with real-time operating load data to perform data processing and normalization, thereby constructing a basic dataset for heat distribution. The support vector machine algorithm is used to classify and model the basic dataset of heat allocation, analyze the correlation between energy consumption and operating load, and construct an initial framework for heat allocation optimization. The system retrieves the current actual heat allocation status data and compares it with the initial framework of the heat allocation optimization configuration and historical heat allocation schemes to determine whether the deviation between the current heat allocation status and the optimization framework exceeds a preset threshold. If the deviation exceeds the preset threshold, the heat allocation schemes are prioritized and an adjusted heat allocation configuration strategy is generated. Based on the adjusted heat allocation configuration strategy and in conjunction with the goal of improving energy efficiency, the feasibility of the heat allocation optimization path is analyzed, and a specific implementation plan for heat allocation is derived. Retrieve the latest fluctuation data of the operating load and determine whether the latest fluctuation data of the operating load matches the dynamic coordination indicators; if they do not match, use data analysis tools to make local corrections to the specific implementation plan of heat allocation and determine the final implementation details of heat allocation. Based on the aforementioned final heat allocation implementation details, continuously monitor the trend of energy consumption changes, combine dynamic coordination and real-time feedback data, optimize and determine a stable path to improve energy efficiency. Regularly update the data analysis results and, in conjunction with the long-term goal of improving energy efficiency, determine the direction for continuous optimization of the heat distribution model.

[0012] Furthermore, step S5 specifically includes: Along the energy-saving level improvement path, retrieve indoor temperature maintenance records from the user experience feedback system, classify and organize the indoor temperature maintenance records by time period, and extract the indoor temperature change distribution pattern. The indoor temperature change distribution pattern is compared with the preset indoor temperature comfort range standard. If the temperature change distribution pattern deviates from the preset comfort range, the abnormal temperature time period is marked and a list of time periods that need to be adjusted is generated. For the aforementioned list of time periods requiring adjustment, retrieve the current power operation status data of the main heating equipment, analyze the matching degree between the power configuration and indoor temperature changes, and generate a preliminary power adjustment plan; Based on the aforementioned preliminary power adjustment scheme, and combined with solar energy acquisition frequency data, the influence weight of solar energy acquisition frequency on heating efficiency is analyzed, and the optimization direction of solar energy acquisition frequency is determined. Based on the aforementioned solar energy acquisition frequency optimization direction, a joint control strategy for the power of the main heating equipment and the solar energy acquisition frequency is generated to obtain a heating efficiency correction value. The heating efficiency correction value is imported into the system control unit, the operating system control command is updated, the real-time feedback data of indoor temperature after the command is executed is collected, and it is determined whether the indoor temperature has reached the preset comfort range. Based on the real-time indoor temperature feedback data, the indoor temperature maintenance record is continuously monitored to evaluate the actual implementation effect of the energy-saving improvement path.

[0013] Furthermore, step S6 specifically includes: Using the heating efficiency correction value as input parameter, the random forest algorithm is used to conduct a stratified assessment of the risk of insufficient heat supply in low-temperature environments, and the high-risk periods in the assessment results are marked to generate a risk distribution priority ranking result. Based on the risk distribution priority ranking results, retrieve the energy consumption records under the system operation status, analyze the correspondence between the energy consumption peak during high-risk periods and the heating efficiency trough, and determine the initial adjustment direction of energy configuration. Environmental impact factor parameters are extracted from historical operational data, the mechanism of low temperature environment on insufficient heat supply is analyzed, and a reference basis for environmental adaptive adjustment is generated. Based on the aforementioned environmental adaptability adjustment reference and real-time feedback data of system operation status, an energy consumption reduction plan for high-risk periods is generated, and specific parameter values ​​for energy configuration optimization are determined. The specific parameter values ​​for energy configuration optimization are imported into the system, the system operating status is dynamically updated, the updated heating efficiency analysis data is collected, and it is determined whether the preset heat supply standard is met. If the heating efficiency analysis data does not meet the preset heat supply standard, additional environmental impact data will be retrieved from the historical operation records and combined with the current energy configuration adjustment plan for secondary optimization to obtain the final system operation configuration parameters. Based on the final system operation configuration parameters, the energy consumption reduction effect is continuously monitored, the mitigation of the risk of insufficient heat supply is analyzed, and the stability indicators of the overall system operation status are determined.

[0014] Furthermore, step S7 specifically includes: A comprehensive scan of the overall system operation status is performed, real-time monitoring data of equipment wear is collected, the wear trend of each equipment component is analyzed, and the current operational stability indicators of the system are determined. Based on the wear and tear trend of the equipment components, a preset stability threshold is used to evaluate the dynamic coordination state and determine whether the dynamic coordination has reached a stable state. If the stable state has not been reached, the coordination deviation value is recorded and a dynamic coordination state evaluation report is generated. Extract the equipment module data corresponding to the deviation values ​​from the dynamic coordination status assessment report, analyze the distribution characteristics of abnormal points in the equipment loss data collection, and determine the list of key equipment modules that need to be adjusted. Retrieve historical operating status monitoring records of the key equipment modules, analyze the correlation between historical operating status and equipment wear data, determine whether there is a persistent deviation, and generate a priority ranking result for adjusting the key equipment modules; Based on the priority ranking results of the key equipment modules, a heat distribution model update scheme is generated. Combined with the input of system operation status monitoring data, the direction of correction of heat distribution model parameters is determined. According to the correction direction of the heat distribution model parameters, execute the heat distribution model update process, analyze the dynamic coordination state evaluation results after the model update, and output the final result of user experience optimization. Based on the final results of the user experience optimization, real-time data on the system's operating status will be continuously collected, the matching degree between the optimization results and the actual operating status will be analyzed, and the key directions and monitoring parameters for subsequent system monitoring will be determined.

[0015] The technical effects and advantages of this invention are as follows: 1. This application provides a solar thermal coupled air source heat pump heating control method, which addresses the problem of fluctuations in solar energy collection and imbalance in heat distribution of heating equipment in low-temperature environments. By acquiring ambient temperature and solar radiation intensity data in real time, analyzing environmental change parameters, determining whether the solar energy collection is below normal levels, and combining historical load data of the main heating equipment, the power adjustment requirements are determined.

[0016] 2. The solar-thermal coupled air source heat pump heating control method provided in this application optimizes the heat distribution scheme by dynamically coordinating indicators, using support vector machine and random forest algorithms, assessing heating efficiency and energy consumption risks, and finally realizing dynamic adjustment of equipment power and solar energy collection rhythm, updating the heat distribution model, and ensuring indoor temperature comfort and system operation stability.

[0017] 3. The photothermal coupling air source heat pump heating control method provided in this application realizes dynamic coordination and energy-saving optimization of the photothermal coupling system through intelligent algorithms, effectively reducing energy consumption, improving user experience, and achieving the best balance between heating efficiency and environmental adaptability. Attached Figure Description

[0018] Figure 1 This is a flowchart of the photothermal coupled air source heat pump heating control method of the present invention; Figure 2 This is a schematic diagram of the implementation process of step S1 of the present invention; Figure 3 This is a schematic diagram of the implementation process of step S5 of the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the embodiments given in the accompanying drawings.

[0020] like Figures 1-3 As shown in the figure, the specific implementation steps of the solar-thermal coupled air source heat pump heating control method disclosed in this embodiment are as follows: Step S1: By deploying an outdoor environmental sensing module, the ambient temperature and solar radiation intensity parameters are collected in real time. The ambient temperature and solar radiation intensity parameters are filtered, purified, feature extracted, and trend analyzed to determine the current low temperature environment level and the fluctuation characteristics of solar energy collection. Then, environmental change parameters are integrated to provide data support for subsequent heat distribution and power coordination control.

[0021] For example, the environmental sensing module continuously collects raw outdoor ambient temperature data and raw solar radiation intensity data at a preset sampling frequency of 10 seconds per sampling, constructing a raw environmental dataset, which can be represented as:

[0022] in, (i=1,2,…,n) represents the raw ambient temperature data collected in the i-th time, in °C; (i=1,2,…,n) represents the raw solar radiation intensity data collected in the i-th time, in W / m²; n represents the total number of collections.

[0023] Due to external electromagnetic interference, momentary obstruction, and other interference factors, the original environmental dataset is prone to abnormal and sudden data changes (for example, the ambient temperature jumps instantaneously from 3°C to 75°C at a certain collection moment and then quickly returns to the normal range). To ensure data accuracy, this embodiment uses a median filtering algorithm to clean the original environmental dataset, removing outliers and noise interference to obtain purified ambient temperature data. Compared with purified solar radiation intensity data The core calculation formula of the median filtering algorithm is:

[0024] In the formula, The data is the result of the kth purification cycle (which can be ambient temperature or solar radiation intensity). to Let be the original data sequence centered at the k-th original data and with a window size of 2m+1, where med{} represents taking the median of the data sequence.

[0025] Based on the purified ambient temperature data The system has a preset lower limit for low temperature thresholds. When the ambient temperature data after purification stabilizes at 2℃ for a consecutive preset number of frames (i.e., When the current environment is determined to be a low-temperature environment, it is classified as a Level 2 low-temperature environment based on the preset low-temperature environment classification standard, and the low-temperature environment level classification result is output.

[0026] Combining the aforementioned Level II low-temperature environment level and the purified solar radiation intensity data The time series analysis method was used to monitor the change trend of solar radiation intensity over time in real time. The solar radiation intensity data after purification during the monitoring period met the requirements. Furthermore, there is no stable upward or downward trend, which indicates that the amount of solar energy collected exhibits a high-frequency and drastic fluctuation trend.

[0027] Furthermore, time series analysis was used to extract features from the fluctuation trend of the solar energy collection to obtain the fluctuation period. Average fluctuation range The formula for calculating the fluctuation range is:

[0028] In the formula, To monitor the average value of solar radiation intensity data after purification during the monitoring period, Let represent the solar radiation intensity data after the i-th purification, and n represent the total number of data collections during the monitoring period.

[0029] Based on the above fluctuation characteristics, dynamic parameters of environmental change are generated, including fluctuation period, average fluctuation amplitude, and instantaneous rate of change. Its expression is:

[0030] In the formula, This represents the instantaneous rate of change of solar radiation intensity, reflecting the real-time fluctuation rate of solar radiation intensity.

[0031] Based on the dynamic parameters of environmental change Based on the characteristics of the secondary low-temperature environment and the high-frequency and drastic fluctuations in solar energy collection, the system uses a comprehensive evaluation model to determine that the impact of the current environmental changes on solar energy collection efficiency is high, which may easily lead to unstable heat supply and sudden increases and decreases in output power of the solar energy collection module.

[0032] In response to the aforementioned impact levels, the system generates an environmental adaptability adjustment strategy: increasing the sampling frequency of solar radiation intensity from 10 seconds / time to 2 seconds / time to enhance the monitoring density of radiation fluctuations; simultaneously optimizing the response speed of the solar collector tilt adjustment mechanism to achieve rapid adaptation to varying radiation conditions. Ultimately, the core focus of subsequent environmental monitoring is determined to be the instantaneous fluctuation of solar radiation intensity and the trend of sudden changes in ambient temperature. The low-temperature environment level, dynamic parameters of environmental changes, environmental adaptability adjustment strategy, and monitoring priorities are integrated to form environmental change parameters. The environmental change parameters Used for subsequent control processes such as power adjustment of main heating equipment and heat distribution coordination.

[0033] Step S2: Based on the environmental change parameters Based on a preset solar energy collection threshold, it is determined whether the real-time collection volume of the solar energy collection module is lower than the preset normal level. If the solar energy collection volume is lower than the preset normal level, historical operating load data is retrieved from the main heating equipment database. By extracting features and analyzing patterns from the historical operating load data, the power adjustment requirements of the main heating equipment are determined, providing a basis for subsequent power regulation.

[0034] For example, the system is based on the aforementioned environmental change parameters The included characteristics of a secondary low-temperature environment, high-frequency and drastic fluctuations in solar radiation intensity, and a high level of impact on solar energy collection efficiency, combined with the preset normal threshold for solar energy collection, are considered. The real-time power acquisition of the solar energy acquisition module is dynamically monitored. The real-time acquisition power of the solar energy acquisition module is then monitored. ,satisfy If the solar energy supply is deemed insufficient, the power adjustment process of the main heating equipment needs to be initiated.

[0035] The system retrieves historical operating load data and operating parameters for the past 72 hours from the main heating equipment database. These operating parameters include compressor operating frequency, input power, heating capacity, and ambient temperature-related data. A time series feature extraction algorithm is used to extract the feature parameters of the above data as a function of time, thereby obtaining the load variation pattern of the main heating equipment under different low temperature levels and different solar radiation intensities.

[0036] The historical operating load data and operating parameters were classified using the Support Vector Machine (SVM) algorithm, with ambient temperature as the primary criterion. Solar energy gap Heat load demand As a feature vector, a classification model for the operation mode of the main heating equipment is constructed, and the expression of the feature vector is:

[0037] Among them, the solar energy gap The formula for calculation is:

[0038] The operating mode classification model categorizes the operating modes of the main heating equipment into four types: low load mode, medium load mode, high load mode, and extreme cold compensation load mode. Based on model matching, the current operating condition corresponds to the high load mode, requiring an increase in the output power of the main heating equipment (air source heat pump unit). This serves as the initial basis for power adjustment.

[0039] Based on this preliminary evidence, combined with real-time environmental change data (current ambient temperature) (Due to high-frequency and drastic fluctuations in solar radiation intensity), dynamic matching was performed on the current operating status of the main heating equipment, and it was determined that the current operating power of the main heating equipment is its rated power. 45% of This power output is insufficient to make up for the current solar energy deficit. The specific range of power adjustment is calculated and determined, and the operating power of the main heating equipment is increased to 70% of the rated power, i.e., the target power. The formula for calculating the power adjustment range is:

[0040] Based on the power increase and the preset setting requirements of the main heating equipment operating parameters, the core control parameters such as compressor operating frequency, electronic expansion valve opening, and fan speed are updated to generate the adjusted operating configuration parameters: compressor operating frequency is 55Hz, operating power is 70% of the rated value, and outlet water temperature is set to 45℃.

[0041] Based on the updated operating configuration parameters, the system continuously tracks and monitors the actual output power, heating capacity, and outlet water temperature of the main heating equipment through the data acquisition module to determine whether the actual operating status has reached the preset target. The preset target is: the operating power reaches 70% of the rated power, and the heating power reaches [missing information]. .

[0042] If monitoring reveals that the main heating equipment is limited by low-temperature frosting conditions and operating environment, and its actual operating power is only stable at 58% of the rated power, then... If the preset 70% adjustment target is not achieved, the system will retrieve historical operating load data for a longer period of nearly 180 days from the main heating equipment database. It will focus on extracting operating characteristic parameters under similar extreme cold, weak sunlight, and high-frequency fluctuations in sunlight intensity. Through data analysis, the power adjustment direction will be redefined: maintain the operating power of the main heating equipment at 58% of the rated power, while increasing the defrosting frequency and increasing the heat medium circulation flow rate. This will replace the simple power increase with heat compensation to ensure that the heat load demand is met.

[0043] Step S3: Based on the power adjustment requirements of the main heating equipment, collect the current output parameters of the solar energy acquisition module and the real-time heat distribution status parameters of the main heating equipment. Through a preset balance judgment standard, determine whether the current output parameters of the solar energy acquisition module and the real-time heat distribution status of the main heating equipment are in an unbalanced state. Quantify the imbalance deviation and generate dynamic coordination indicators to provide a quantitative basis for subsequent heat distribution optimization.

[0044] For example, the system extracts the current output parameters from the solar energy acquisition module in real time through a preset data acquisition process. The current output parameters are the instantaneous effective heating output. Simultaneously, real-time heat distribution status parameters are retrieved from the main heating equipment operation record. These real-time heat distribution status parameters represent the current total heating load demand. To construct a basic data comparison set of solar energy output and heat load demand.

[0045] Based on a preset balance judgment standard: when the ratio of solar heating output to total heat load demand is ≥80%, it is determined to be a supply-demand matching state. The expression for this judgment standard is: ; A comparative analysis is performed on the aforementioned basic data set to calculate the ratio of current solar heating output to total heat load demand: ,because The analysis determined that the solar energy output and heat distribution were mismatched, and the preliminary balance assessment indicated a supply-demand imbalance.

[0046] The system quantifies the degree of supply-demand imbalance using dynamic index calculation tools, first calculating the heat load gap. The formula for its calculation is: Substituting the data, we get: Define dynamic coordination indicators It is used to quantify the degree of supply and demand imbalance, and its calculation formula is: Substituting the data, the specific dynamic coordination index values ​​are calculated as follows: ; Based on this dynamic coordination index value The system retrieves real-time operating status data of the main heating equipment and, in conjunction with the preset heating zone priority logic (bedroom, living room > bathroom > balcony, storage room), sorts the heat allocation priority of each heating zone. It then determines the parameter adjustment direction for prioritizing the heating needs of high-priority areas and reducing the heating flow of low-priority areas, serving as a reference for subsequent heat allocation parameter updates.

[0047] Based on the above adjustment direction and considering the current operating status of the main heating equipment at a load rate of 65%, the heat distribution parameters are dynamically updated: the high-priority area maintains the original heat medium flow rate and water supply temperature; the low-priority area reduces the heat medium valve opening from 80% to 45%, and the heat medium flow rate is reduced accordingly, generating a new heat distribution operation configuration scheme. Under the premise that the total heating capacity of the main heating equipment remains unchanged, the heat gap in the high-priority area is prioritized to be supplemented.

[0048] Based on the new operating configuration, the system continuously monitors the output parameters of the solar energy acquisition module and the changes in heat distribution of the main heating equipment. As the external cloud layer moves, the instantaneous effective heating output of the solar energy acquisition module gradually recovers. The heat distribution of the main heating equipment tends to be reasonable, and the supply-demand deviation gradually narrows to within 5%. It is determined that the solar energy output and heat distribution status are gradually approaching equilibrium, and the final coordination result is coordinated and stable.

[0049] Step S4: Using the aforementioned dynamic coordination index Based on this core principle, the support vector machine algorithm is used to classify, model, and analyze historical energy consumption data and operating load data, construct an optimized heat distribution scheme, and then determine the path to improve the system's energy-saving level, thereby achieving a balance between energy saving and heating demand.

[0050] For example, the system uses the dynamic coordination index Based on this as the core basis, key operational data for the past 30 days were extracted from the historical energy consumption database. This key operational data includes: solar energy collection at different low-temperature levels, power consumption of the main heating equipment, heat medium flow rate in each heating zone, outlet water temperature, and energy consumption variation curves; this is combined with real-time data on the current system operating load (total heating load demand). The above data is then organized and normalized to construct a standardized basic dataset for heat distribution. The normalization formula is as follows:

[0051] In the formula, For the normalized data, This is the original data. The maximum value in the original data. This is the minimum value in the original data.

[0052] The support vector machine algorithm is used to classify and model the basic dataset of heat distribution, taking ambient temperature as the basis. Solar power output Using load structure and energy intensity as feature vectors, this study analyzes the correlation between energy consumption and operating load, and constructs an initial framework for optimizing heat allocation. This initial framework divides the day into four typical operating period periods and sets a baseline heat allocation ratio for each period, as follows: During periods of high temperature fluctuations: the solar energy collection module accounts for 40% of heating, and the main heating equipment accounts for 60%, i.e. ; During periods of stable low temperatures: the solar energy collection module accounts for 60% of the heating output, and the main heating equipment accounts for 40%. ; During the mid-temperature period: the solar energy collection module accounts for 70% of the heating output, and the main heating equipment accounts for 30%. .

[0053] The system retrieves current actual heat distribution data: output from the solar energy acquisition module. Output of main heating equipment Calculate the actual heating ratio of the current solar energy collection module. The formula for its calculation is: Substituting the data, we get: ; The current actual heating ratio is compared with the baseline allocation ratio (40%) during periods of strong low-temperature fluctuations, and the deviation value is calculated. : Substituting the data, we get: Since the deviation value exceeds the preset ±10% deviation threshold, it is determined that the current heat distribution state deviation exceeds the limit and needs to be optimized and adjusted.

[0054] Based on the deviation exceeding limits, the system generates an adjusted heat distribution configuration strategy according to the priority logic of heating areas: prioritizing the increase of the heat medium allocation ratio in high-priority areas; moderately reducing the heat medium flow in low-priority areas; and dynamically adjusting the solar thermal energy utilization ratio to improve the solar energy absorption rate. Based on this configuration strategy and combined with the system's energy-saving level improvement target, the system analyzes the feasibility of the heat distribution optimization path. Under the premise of maintaining the total heating power unchanged, by optimizing the output ratio of the solar energy acquisition module and the main heating equipment, the system energy consumption can be reduced by 5%–8%. Based on this, a specific implementation plan for heat distribution is derived: High priority areas: Maintain the original heat transfer fluid flow rate or slightly increase the heat transfer fluid flow rate; Low priority areas: Heat medium flow rate reduced by 30%; Solar energy harvesting module output target: Increase to ; Main heating equipment output target: reduced to .

[0055] The system retrieves the latest fluctuation data of the operating load (such as a sudden increase in short-term heat load). ), determine its correlation with dynamic coordination indicators The matching was found to be inadequate. Comparison revealed that the current load fluctuation exceeded the prediction range of the dynamic coordination index. Therefore, a partial correction was made to the specific heat distribution implementation plan using data analysis tools. The corrected plan is as follows: the output target of the solar energy acquisition module is adjusted to... Adjust the output target of the main heating equipment to Maintain the heat transfer fluid flow rate in high-priority areas unchanged, and determine the final implementation details for heat allocation.

[0056] Based on the final heat allocation implementation details, the system continuously monitors the trend of energy consumption changes, and combines dynamic coordination with real-time feedback data to optimize and determine a stable path for improving energy efficiency: after 30 minutes of operation and monitoring, the system energy consumption decreased by 6.2% compared with before optimization, the solar energy utilization rate increased by 12%, and the indoor temperature fluctuation was controlled within ±0.5℃, forming a stable energy-saving optimization path.

[0057] The system regularly (e.g., every 7 days) updates the data analysis results and, in conjunction with the long-term goal of improving energy efficiency (reducing annual energy consumption by 8%), determines the direction for continuous optimization of the heat distribution model: further refine the division of working conditions into time periods; introduce a solar energy prediction model to predict changes in solar radiation intensity in advance; dynamically update the priority weights of heating areas, and finally form an adaptive and sustainable heat distribution control strategy to ensure that the system balances energy efficiency and heating comfort in complex environments.

[0058] Step S5: Along the energy-saving level improvement and stabilization path, retrieve the indoor temperature maintenance record from the user experience feedback system, compare the indoor temperature corresponding to the indoor temperature maintenance record with the preset comfort temperature range, and determine whether it is within the preset comfort range; if it is not within the preset comfort range, jointly regulate the power of the main heating equipment and the solar energy collection rhythm, calculate the heating efficiency correction value, and achieve dual optimization of indoor comfort and energy-saving performance.

[0059] For example, following a preset energy-saving level improvement path, the system retrieves nearly 72 hours of indoor temperature maintenance records from the user experience feedback system, categorizes and organizes the indoor temperature maintenance records into 2-hour periods, and extracts the indoor temperature change distribution pattern: the indoor temperature fluctuates between 18-19℃ from 2:00 AM to 6:00 AM, between 22-23℃ from 8:00 AM to 12:00 PM, between 2:00 PM to 6:00 PM, between 23-24℃, and between 8:00 PM to 10:00 PM, between 21-22℃.

[0060] The preset indoor temperature comfort range standard is The indoor temperature variation distribution pattern was compared with the comfort range standard. It was found that the indoor temperature during the period from 2:00 am to 6:00 am was consistently lower than the lower limit of the comfort range, deviating from the preset standard. Therefore, this period was marked as an abnormal temperature period, and a list of periods that need to be adjusted was generated (only including the period from 2:00 am to 6:00 am).

[0061] Regarding the list of time periods requiring adjustment, the system retrieved the power operation status data of the main heating equipment (air source heat pump unit) during the period of 2:00 AM to 6:00 AM. It was found that during this period, the main heating equipment was in a low-power standby state, with its operating power only at 30% of its rated power. Analysis revealed a mismatch between the current power configuration and the persistently low indoor temperature trend. A preliminary power adjustment plan was proposed: increase the operating power of the main heating equipment to 50% of its rated power during the 2:00 AM to 6:00 AM period. .

[0062] Based on this preliminary power adjustment plan, and combined with solar energy collection frequency data (currently, the solar energy collection frequency during the early morning period is 10 minutes / time), data analysis shows that for every 1 time / 5 minutes increase in the solar energy collection frequency during the early morning period, the system heating efficiency can be improved by 4%. Therefore, the optimization direction for the solar energy collection frequency is determined to be: to increase the solar energy collection frequency from 10 minutes / time to 5 minutes / time during the period from 2:00 to 6:00 in the early morning, so as to accurately capture the weak solar heat energy during this period and improve the utilization rate of solar energy.

[0063] Based on the optimization direction of the solar energy collection frequency, a joint control strategy for the power of the main heating equipment and the solar energy collection frequency is generated: during the period from 2:00 to 6:00 in the morning, the power of the main heating equipment is maintained at 50% of the rated power, the solar energy collection frequency is adjusted to 5 minutes / time, and the circulation speed of the solar thermal medium is optimized simultaneously to reduce heat loss.

[0064] Define heating efficiency correction value This is used to quantify the improvement in heating efficiency after regulation, and its calculation formula is:

[0065] In the formula, The heating efficiency before adjustment. The adjusted heating efficiency is shown below. Through joint regulation, the corrected heating efficiency value is obtained. (That is, heating efficiency is improved by 8%).

[0066] The heating efficiency correction value Import the system control unit, update the system operation control instructions. After the instructions are executed, the system continuously collects real-time feedback data on indoor temperature through the indoor temperature sensing module. It is found that the indoor temperature gradually rises to 20-21℃ between 2:00 AM and 6:00 AM, reaching the preset comfort range standard.

[0067] Based on the real-time indoor temperature feedback data, the system continuously monitored the indoor temperature records from 2:00 AM to 6:00 AM for the next three days, confirming that the indoor temperature remained stable within the range of 20-21℃ during this period. The assessment concluded that the actual implementation effect of the energy-saving improvement path was good—it met the indoor heating comfort requirements without increasing additional energy consumption, achieving a balance between energy-saving performance and indoor comfort.

[0068] Step S6: Based on the heating efficiency correction value The random forest algorithm is used to conduct multi-feature hierarchical assessment of the risk of insufficient heat supply in the system under low temperature environment. The assessment results are combined to determine the final energy consumption reduction configuration parameters, thereby obtaining the overall system operating status and ensuring the stability and reliability of the system operation.

[0069] For example, the system uses the heating efficiency correction value. Using the random forest algorithm as input parameters, a multi-feature hierarchical assessment of the risk of insufficient heat supply in low-temperature environments is performed, with ambient temperature selected as the input parameter. Sunlight intensity Sunlight fluctuation range Heating efficiency Duration of historical heat deficiency As feature variables, a risk assessment model is constructed, and the expression for the set of feature variables is:

[0070] The risk assessment model is used to classify the risk of insufficient heat supply throughout the 24 hours of the day. According to the model calculation, the period from 2:00 am to 6:00 am is identified as an extremely high-risk period, the period from 6:00 am to 8:00 am is identified as a medium-risk period, and the remaining periods are identified as low-risk periods. The high-risk periods are marked and a risk distribution priority ranking is generated: 2:00 am to 6:00 am > 6:00 am to 8:00 am > other periods.

[0071] Based on the above risk priority ranking, the system retrieves the system's energy consumption records for the past 7 days. Through data analysis, it is found that: high-risk periods correspond to peak system energy consumption, while heating efficiency is at its lowest. Furthermore, the lower the ambient temperature and the greater the heat deficit, the higher the system energy consumption. Therefore, the initial direction for adjusting energy configuration is determined to be: during high-risk periods, moderately increase the output of the main heating equipment (heat pump), optimize the defrosting logic, reduce ineffective energy consumption, and improve energy utilization efficiency.

[0072] The system extracts environmental impact factor parameters from historical operating data, including ambient temperature, ambient humidity, wind speed, outdoor unit frost level, and light attenuation rate. It analyzes the mechanism by which low-temperature environments affect insufficient heat supply: when the ambient temperature... At this time, the outdoor unit of the air source heat pump is prone to frost formation, which leads to a decrease in the heat exchange coefficient and a reduction in heating efficiency of 12% to 18%. Coupled with the extremely low solar energy collection during this period, there is a risk of insufficient heat supply. This can be used as a reference for environmental adaptability adjustments.

[0073] Based on the aforementioned environmental adaptability adjustment reference and real-time system operation feedback data, the system generates an energy consumption reduction plan for high-risk periods and determines specific parameter values ​​for energy configuration optimization: Compressor operating frequency: increased from 45Hz to 55Hz; Defrosting cycle: extended from 60 minutes to 80 minutes; Heat pump outlet water temperature: set to 44℃; Heat allocation ratio for low-priority areas: reduced by 15%.

[0074] The above optimized parameters were imported into the system control unit to dynamically update the system operating status and collect the updated heating efficiency data: the actual heating efficiency increased by 7.9%, and the total heating supply met the preset standard. The system is determined to have met the heat supply requirements.

[0075] If the actual heating efficiency of the system only increases by 4.2% after the update, and the total heating capacity is... If the preset standard is not met, the system retrieves additional environmental impact data such as extreme low temperature, continuous cloudy days, and strong winds from historical operation records, and performs secondary optimization in combination with the current energy configuration scheme: further increasing the compressor operating frequency to 58Hz, increasing the heat pump outlet water temperature to 46℃, shortening the interval of solar collector heat medium circulation, reducing heat loss, and obtaining the final system operation configuration parameters.

[0076] Based on the final operating configuration parameters, the system continuously monitors changes in energy consumption and heating supply: the risk of insufficient heating supply during high-risk periods decreased by 72%, the system energy consumption decreased by 9.3% compared to before optimization, the heating supply was stable and met the standards, and the indoor temperature fluctuation was ≤±0.6℃. Based on this, the overall system operating status stability index was determined to be excellent, and the dynamic coordination control was reliable.

[0077] Step S7: Based on the overall system operating status, collect real-time monitoring data of the losses of each core equipment component through the equipment loss monitoring module, and determine whether the dynamic coordination between the solar energy acquisition module and the main heating equipment has reached a preset stable state; if the preset stable state has been reached, update and optimize the heat distribution model, output user experience optimization results, and ensure long-term stable operation of the system.

[0078] For example, the system performs a comprehensive scan of the overall operating status of the photothermal coupled air source heat pump through the control unit, and simultaneously collects real-time monitoring data on the losses of each core equipment component, including: compressor friction loss. Circulating pump power loss Heat exchange loss of solar collectors Electronic expansion valve operating loss By analyzing the trend of loss of each component using trend analysis tools, the compressor friction loss has shown a continuous upward trend in the past 3 days (from 5% to 8%), the circulating pump power loss has remained stable within the range of 3% ± 0.5%, the solar collector heat exchange loss has remained at 2%, and the electronic expansion valve action loss has not changed significantly. Based on the preset loss evaluation criteria, the current operating stability index of the system is determined to be "good" (not reaching the "excellent" level standard).

[0079] Based on the loss trends of each equipment component, the system uses preset stability thresholds to evaluate the dynamic coordination status between the solar energy acquisition module and the main heating equipment. These preset stability thresholds are: daily equipment loss fluctuation ≤ ±1% / day, and dynamic coordination deviation ≤ 5%. The formula for calculation is:

[0080] In the formula, To coordinate indicator values ​​in real time, The target value for the preset dynamic coordination indicator is set.

[0081] Testing revealed that the compressor's friction loss fluctuated by 1% per day, indicating a dynamic coordination deviation. All values ​​exceeded the preset stability threshold, indicating that the dynamic coordination between the solar energy acquisition module and the main heating equipment had not reached the preset stable state.

[0082] For the unstable state of dynamic coordination, the system records the specific coordination deviation values: dynamic coordination deviation 6.8%, compressor friction loss deviation 3%, heat distribution deviation 4.2%, and generates a "Dynamic Coordination Status Assessment Report" to clarify the deviation type, deviation magnitude and corresponding impact range.

[0083] Extract the equipment module data corresponding to the deviation values ​​from the aforementioned "Dynamic Coordination Status Assessment Report", and focus on analyzing the distribution characteristics of abnormal points in the equipment loss data collection. The abnormal points are mainly concentrated during the compressor operation period (the extremely high-risk period from 2:00 AM to 6:00 AM), which manifests as a sudden increase in instantaneous loss and a mismatch with load fluctuations. Based on this, a list of key equipment modules that need to be adjusted is determined: compressor module and compressor frequency conversion control module.

[0084] The system retrieved the historical operating status monitoring records of the above-mentioned key equipment modules for the past 30 days and analyzed the correlation between the historical operating status and equipment loss data. It was found that the compressor loss was consistently high under high load (operating power ≥ 70% of rated power) and low temperature frosting conditions, and there was a significant and continuous deviation from the ambient temperature and operating load (the deviation was consistently greater than 2%). Based on the importance of the equipment modules and the degree of impact of loss, the adjustment priority ranking result of the key equipment modules was generated: compressor frequency conversion control module > compressor module.

[0085] Based on this priority ranking result, the system generates an updated heat distribution model scheme, combined with current system operating status monitoring data (ambient temperature). Solar power output Total heat load Input the following parameters to determine the direction of correction for the heat distribution model: 1. Optimize the compressor frequency conversion control curve to reduce compressor losses during high-load periods; 2. Adjust the heat distribution weights of each heating zone to reduce the compressor's high-load running time; 3. Link compressor loss data to dynamically adjust the upper limit of heat pump output to avoid excessive losses.

[0086] Following the aforementioned model parameter correction direction, the system executes a heat distribution model update process: updating compressor inverter control parameters (lowering the upper frequency limit during high-load periods from 55Hz to 52Hz), adjusting heat distribution weights (increasing the heating ratio of high-priority areas by 5%), and adding loss feedback adjustment logic to achieve closed-loop control of losses and power. After the update, the dynamic coordination state is re-evaluated, and the evaluation results are as follows: dynamic coordination deviation is reduced to 3.2%, daily equipment loss fluctuation is ≤0.8% / day, reaching the preset stability threshold, and the final result of user experience optimization is output: indoor temperature is stable at 20-21℃, system energy consumption is reduced by 7.5%, equipment operating noise is reduced by 3dB, and heating comfort meets the standards.

[0087] Based on the final results of this user experience optimization, the system continuously collects real-time data on system operation status (collection frequency of 5 minutes / time) and analyzes the matching degree between the optimized results after model update and the actual operation status. After 24 hours of continuous monitoring, the matching degree between the optimized results and the actual operation status reached 92%, with no significant deviation. Based on this, the key areas and parameters for subsequent system monitoring were determined: the compressor module (monitoring parameters include compressor loss value, operating frequency, and exhaust temperature) and the compressor frequency converter control module (monitoring parameters include control accuracy and response speed) will be the focus, with the monitoring frequency increased to 2 minutes / time to ensure long-term stable system operation.

[0088] This embodiment provides a heating control method for a photothermal coupled air source heat pump, which solves the technical problems of poor energy synergy, weak low-temperature adaptability, low heating efficiency, and high energy consumption and equipment loss in existing photothermal coupled air source heat pump systems, thereby improving the overall energy efficiency and heating reliability.

[0089] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A method for controlling the heating of a photothermal coupled air source heat pump, characterized in that, The method includes the following steps: Step S1: Collect real-time ambient temperature data and solar radiation intensity data through environmental sensors, analyze the real-time ambient temperature data and solar radiation intensity data, determine the current low temperature environment level and the fluctuation trend of solar energy collection, and then generate dynamic parameters of environmental change. Step S2: Based on the dynamic parameters of environmental changes, determine whether the solar energy collection is lower than the preset normal level according to the preset threshold; if the solar energy collection is lower than the preset normal level, retrieve historical operating load data from the main heating equipment database, and determine the power adjustment requirements of the main heating equipment by analyzing the historical operating load data. Step S3: Based on the power adjustment requirements of the main heating equipment, collect the current output parameters of the solar energy acquisition module and the real-time heat distribution status of the main heating equipment, determine whether the current output parameters of the solar energy acquisition module and the real-time heat distribution status of the main heating equipment are out of balance, and generate dynamic coordination indicators. Step S4: Based on the dynamic coordination index, the support vector machine algorithm is used to process the historical energy consumption data and operating load data to construct a heat distribution optimization scheme, thereby determining the path to improve energy efficiency. Step S5: Along the energy-saving level improvement path, retrieve the indoor temperature maintenance record from the user experience feedback system and determine whether the indoor temperature corresponding to the indoor temperature maintenance record is within the preset comfort range; if it is not within the preset comfort range, adjust the power of the main heating equipment and the solar energy collection rhythm to obtain the heating efficiency correction value. Step S6: Based on the heating efficiency correction value, the random forest algorithm is used to analyze the risk of insufficient heat supply in low temperature environment, determine the final energy consumption reduction configuration parameters, and then obtain the overall system operating status. Step S7: Based on the overall system operating status, collect real-time monitoring data of equipment loss and determine whether the dynamic coordination between the solar energy acquisition module and the main heating equipment has reached a stable state; if a stable state has been reached, update the heat distribution model and output the user experience optimization results.

2. The heating control method for a photothermal coupled air source heat pump according to claim 1, characterized in that, Step S1 specifically includes: Real-time ambient temperature and solar radiation intensity data are continuously collected by environmental sensors. Data is collected according to a preset sampling frequency to generate an original environmental dataset. The original environmental dataset is cleaned to remove outliers and noise interference, resulting in purified temperature and solar radiation intensity data. Based on the purified temperature data and combined with the preset temperature threshold range, if the purified temperature data is lower than the lower limit of the preset temperature threshold range, the current environment is determined to be a low-temperature environment, and the classification of the low-temperature environment level is completed to obtain the low-temperature environment level classification result. By combining the low-temperature environment classification results with the purified solar intensity data, the variation law of solar intensity over time is analyzed to determine the fluctuation trend of solar energy collection. Time series analysis is used to extract features from the fluctuation trend of solar energy collection, obtain fluctuation period and fluctuation amplitude characteristic parameters, and generate dynamic parameters of environmental change. Based on the aforementioned dynamic parameters of environmental change, combined with the low temperature environment level and the fluctuation trend of solar energy collection, a comprehensive assessment of the current environmental state is conducted to determine the degree of impact of environmental changes on solar energy collection efficiency. Based on the degree of impact of environmental changes on solar energy collection efficiency, an environmental adaptation strategy is generated, and the key directions and monitoring parameters for subsequent environmental monitoring are determined.

3. The heating control method for a photothermal coupled air source heat pump according to claim 1, characterized in that, Step S2 specifically includes: Based on the dynamic parameters of environmental changes and combined with the preset solar energy collection threshold range, the real-time collection volume of the solar energy collection module is dynamically monitored to determine whether it is lower than the preset normal level. If the solar energy collection is lower than the preset normal level, the historical operating load data and operating parameters of the main heating unit are retrieved from the main heating equipment database, and the time series features of the historical operating load data and operating parameters are extracted. The historical operating load data and operating parameters of the main heating engine are classified and processed using the support vector machine algorithm to construct a classification model of the main heating engine's operating mode, thereby obtaining a preliminary basis for power adjustment. Based on the preliminary basis for the power adjustment, and combined with real-time monitoring data of environmental changes, the current operating status of the main heating unit is dynamically matched to determine the specific range of the main heating unit power adjustment. Based on the specific range of the power adjustment and in conjunction with the operating parameter setting requirements of the main heating equipment, the control parameters of the main heating unit are updated to generate the adjusted operating configuration parameters. Based on the adjusted operating configuration parameters, the actual operating status of the main heat engine is continuously tracked and monitored to determine whether its operating status meets the preset power adjustment target. If the actual operating status of the main heating unit does not meet the preset power adjustment target, then historical operating load data for a longer period will be retrieved from the main heating equipment database, and a new power adjustment direction will be determined through data analysis.

4. The heating control method for a photothermal coupled air source heat pump according to claim 1, characterized in that, Step S3 specifically includes: By using a pre-defined data acquisition process, the current output parameters are extracted from the solar energy acquisition module, and the real-time heat distribution status parameters are retrieved from the operation record of the main heating engine to build a basic data comparison set between the two. Based on the preset balance judgment criteria, the current output parameters of the solar energy acquisition module in the basic data comparison set are compared and analyzed with the real-time heat distribution status parameters of the main heat engine to determine whether the two are in a matching state and obtain a preliminary balance judgment result. If the preliminary balance judgment result is that the current output parameters of the solar energy acquisition module do not match the real-time heat distribution status parameters of the main heat generator, then the degree of deviation between the two is quantified by the dynamic index calculation tool to obtain specific dynamic coordination indicators. Based on the aforementioned dynamic coordination index, the real-time operating status data of the main heat engine is retrieved, and combined with the preset power adjustment demand logic, the priority of heat distribution is sorted to determine the reference basis for the direction of parameter adjustment. Based on the reference criteria for the adjustment direction of the parameters, and combined with the current power load of the main heating equipment, the heat distribution parameters of the main heating unit are dynamically updated to generate a new operating configuration scheme. Based on the new operating configuration scheme, the real-time operating status of the main heating engine and the changes in the output parameters of the solar energy acquisition module are continuously monitored to determine whether the two are gradually approaching a state of equilibrium, and to obtain the final coordination result.

5. The heating control method for a photothermal coupled air source heat pump according to claim 1, characterized in that, Step S4 specifically includes: Based on the aforementioned dynamic coordination indicators, key data are extracted from historical energy consumption records and combined with real-time operating load data to perform data processing and normalization, thereby constructing a basic dataset for heat distribution. The support vector machine algorithm is used to classify and model the basic dataset of heat allocation, analyze the correlation between energy consumption and operating load, and construct an initial framework for heat allocation optimization. The system retrieves the current actual heat allocation status data and compares it with the initial framework of the heat allocation optimization configuration and historical heat allocation schemes to determine whether the deviation between the current heat allocation status and the optimization framework exceeds a preset threshold. If the deviation exceeds the preset threshold, the heat allocation schemes are prioritized and an adjusted heat allocation configuration strategy is generated. Based on the adjusted heat allocation configuration strategy and in conjunction with the goal of improving energy efficiency, the feasibility of the heat allocation optimization path is analyzed, and a specific implementation plan for heat allocation is derived. Retrieve the latest fluctuation data of the operating load and determine whether the latest fluctuation data of the operating load matches the dynamic coordination indicators; if they do not match, use data analysis tools to make local corrections to the specific implementation plan of heat allocation and determine the final implementation details of heat allocation. Based on the aforementioned final heat allocation implementation details, continuously monitor the trend of energy consumption changes, combine dynamic coordination and real-time feedback data, optimize and determine a stable path to improve energy efficiency. Regularly update the data analysis results and, in conjunction with the long-term goal of improving energy efficiency, determine the direction for continuous optimization of the heat distribution model.

6. The heating control method for a photothermal coupled air source heat pump according to claim 1, characterized in that, Step S5 specifically includes: Along the energy-saving level improvement path, retrieve indoor temperature maintenance records from the user experience feedback system, classify and organize the indoor temperature maintenance records by time period, and extract the indoor temperature change distribution pattern. The indoor temperature change distribution pattern is compared with the preset indoor temperature comfort range standard. If the temperature change distribution pattern deviates from the preset comfort range, the abnormal temperature time period is marked and a list of time periods that need to be adjusted is generated. For the aforementioned list of time periods requiring adjustment, retrieve the current power operation status data of the main heating equipment, analyze the matching degree between the power configuration and indoor temperature changes, and generate a preliminary power adjustment plan; Based on the aforementioned preliminary power adjustment scheme, and combined with solar energy acquisition frequency data, the influence weight of solar energy acquisition frequency on heating efficiency is analyzed, and the optimization direction of solar energy acquisition frequency is determined. Based on the aforementioned solar energy acquisition frequency optimization direction, a joint control strategy for the power of the main heating equipment and the solar energy acquisition frequency is generated to obtain a heating efficiency correction value. The heating efficiency correction value is imported into the system control unit, the operating system control command is updated, the real-time feedback data of indoor temperature after the command is executed is collected, and it is determined whether the indoor temperature has reached the preset comfort range. Based on the real-time indoor temperature feedback data, the indoor temperature maintenance record is continuously monitored to evaluate the actual implementation effect of the energy-saving improvement path.

7. The heating control method for a photothermal coupled air source heat pump according to claim 1, characterized in that, Step S6 specifically includes: Using the heating efficiency correction value as input parameter, the random forest algorithm is used to conduct a stratified assessment of the risk of insufficient heat supply in low-temperature environments, and the high-risk periods in the assessment results are marked to generate a risk distribution priority ranking result. Based on the risk distribution priority ranking results, retrieve the energy consumption records under the system operation status, analyze the correspondence between the energy consumption peak during high-risk periods and the heating efficiency trough, and determine the initial adjustment direction of energy configuration. Environmental impact factor parameters are extracted from historical operational data, the mechanism of low temperature environment on insufficient heat supply is analyzed, and a reference basis for environmental adaptive adjustment is generated. Based on the aforementioned environmental adaptability adjustment reference and real-time feedback data of system operation status, an energy consumption reduction plan for high-risk periods is generated, and specific parameter values ​​for energy configuration optimization are determined. The specific parameter values ​​for energy configuration optimization are imported into the system, the system operating status is dynamically updated, the updated heating efficiency analysis data is collected, and it is determined whether the preset heat supply standard is met. If the heating efficiency analysis data does not meet the preset heat supply standard, additional environmental impact data will be retrieved from the historical operation records and combined with the current energy configuration adjustment plan for secondary optimization to obtain the final system operation configuration parameters. Based on the final system operation configuration parameters, the energy consumption reduction effect is continuously monitored, the mitigation of the risk of insufficient heat supply is analyzed, and the stability indicators of the overall system operation status are determined.

8. The heating control method for a photothermal coupled air source heat pump according to claim 1, characterized in that, Step S7 specifically includes: A comprehensive scan of the overall system operation status is performed, real-time monitoring data of equipment wear is collected, the wear trend of each equipment component is analyzed, and the current operational stability indicators of the system are determined. Based on the wear and tear trend of the equipment components, a preset stability threshold is used to evaluate the dynamic coordination state and determine whether the dynamic coordination has reached a stable state. If the stable state has not been reached, the coordination deviation value is recorded and a dynamic coordination state evaluation report is generated. Extract the equipment module data corresponding to the deviation values ​​from the dynamic coordination status assessment report, analyze the distribution characteristics of abnormal points in the equipment loss data collection, and determine the list of key equipment modules that need to be adjusted. Retrieve historical operating status monitoring records of the key equipment modules, analyze the correlation between historical operating status and equipment wear data, determine whether there is a persistent deviation, and generate a priority ranking result for adjusting the key equipment modules; Based on the priority ranking results of the key equipment modules, a heat distribution model update scheme is generated. Combined with the input of system operation status monitoring data, the direction of correction of heat distribution model parameters is determined. According to the correction direction of the heat distribution model parameters, execute the heat distribution model update process, analyze the dynamic coordination state evaluation results after the model update, and output the final result of user experience optimization. Based on the final results of the user experience optimization, real-time data on the system's operating status will be continuously collected, the matching degree between the optimization results and the actual operating status will be analyzed, and the key directions and monitoring parameters for subsequent system monitoring will be determined.

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