Intelligent regulation and control method for air source heat pump based on cost-benefit analysis

Abstract

The invention discloses an air source heat pump intelligent regulation and control method based on cost benefit analysis, and relates to the technical field of air source heat pumps, and the method comprises the following steps: collecting operation data by using an air source heat pump and monitoring equipment, and establishing a characteristic parameter set which comprises an environment heat demand characteristic, a time-of-use electricity price time sequence characteristic and a heat pump operation efficiency characteristic; based on the characteristic parameter set, a multi-level decision-making model is established with the purpose of minimizing the energy cost, and the output strategy of the air source heat pump is determined; and on the basis of the multi-level decision model, constraint conditions are set, an adjusting strategy of the air source heat pump is determined, and operation parameters of the air source heat pump are regulated and controlled in real time according to the adjusting strategy. According to the method, the time period with high efficiency can be recognized and utilized for heat energy supply, meanwhile, the energy storage characteristic of the building is considered, output is increased for energy storage in the electricity price valley period, output is reduced in the peak period, stored heat is utilized for heat supply, and the economical efficiency of the air source heat pump is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air source heat pumps, and in particular to an intelligent control method for air source heat pumps based on cost-benefit analysis. Background Art

[0002] As an efficient and environmentally friendly heating and cooling equipment, air source heat pump plays an increasingly important role in the field of building energy conservation and renewable energy utilization. This technology achieves the dual functions of winter heating and summer cooling by absorbing heat from the air. It has the advantages of high energy efficiency, low operating cost, and environmental friendliness. With the development of intelligent technology, the control system of air source heat pump is also constantly upgrading, from simple temperature control to complex control strategies based on multiple factors. Modern air source heat pump systems usually combine multiple technologies such as environmental parameter monitoring, load forecasting, and energy management to achieve more efficient and economical operation. At the same time, considering the thermal inertia and energy storage characteristics of buildings, many advanced systems also adopt time-of-use electricity price strategies and energy storage technologies to further optimize the system's operating costs and energy efficiency.

[0003] However, there are still some shortcomings in the existing air source heat pump control methods. First, most control strategies are too simplistic and fail to fully consider the changes in equipment performance over time and the dynamic impact of the external environment, resulting in a decrease in long-term operating efficiency; second, existing methods often only focus on a single goal, such as energy efficiency or cost, ignoring long-term factors such as equipment life, which may lead to premature aging of equipment or increased maintenance costs. Moreover, many existing technologies lack adaptive capabilities and cannot dynamically adjust strategies according to actual operating conditions and user needs, making it difficult to cope with complex and changing usage environments; in addition, existing technologies usually regard heat pump systems as independent units, failing to fully utilize the energy storage characteristics of buildings and the time-of-use electricity price policy of the power grid, missing the opportunity to further optimize operating costs; finally, most methods lack a comprehensive cost-benefit analysis, and cannot achieve the best balance between multiple goals such as energy efficiency, operating costs, and equipment life, making it difficult to achieve long-term optimal operation of the system. These problems seriously restrict the overall performance and economic benefits of air source heat pump systems, and a more intelligent and comprehensive control method is urgently needed to solve them.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In response to the problems in the related art, the present invention proposes an intelligent control method for air source heat pumps based on cost-benefit analysis, which is capable of identifying and utilizing time periods with higher efficiency for heat supply, and taking into account the energy storage characteristics of the building, increasing the output for energy storage during periods of low electricity prices, and reducing the output during peak periods to utilize stored heat for heating, thereby improving the economic advantages of the air source heat pump, and thereby solving the problem in the prior art of treating the heat pump system as an independent unit, failing to fully utilize the energy storage characteristics of the building and the time-of-use electricity price policy of the power grid, and missing the opportunity to further optimize operating costs.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] An intelligent control method for an air source heat pump based on cost-benefit analysis, the intelligent control method for an air source heat pump based on cost-benefit analysis comprises the following steps:

[0008] S1. Using air source heat pumps and monitoring equipment to collect operating data and establish a characteristic parameter set, wherein the characteristic parameter set includes environmental heat demand characteristics, time-of-use electricity price timing characteristics, and heat pump operating efficiency characteristics;

[0009] S2. Based on the characteristic parameter set and aiming at minimizing energy cost, a multi-level decision-making model is established to determine the output strategy of the air source heat pump;

[0010] S3. Based on the multi-level decision-making model, set constraints, determine the adjustment strategy of the air source heat pump, and adjust the operating parameters of the air source heat pump in real time according to the adjustment strategy.

[0011] Furthermore, using the air source heat pump and monitoring equipment to collect operating data, establishing a characteristic parameter set includes the following steps:

[0012] S11. Collect outdoor environment data, indoor environment data and user historical energy consumption data to establish environmental heat demand characteristics;

[0013] S12. Based on the time-of-use electricity price data, determine the electricity price levels in different time periods of the day, record the start and end time of the electricity price change period, and establish the electricity price time series characteristics;

[0014] S13. Collect the heating capacity and input power of the air source heat pump under different weather conditions, calculate the energy efficiency ratio, and combine it with the outdoor environmental data to establish the heat pump operation efficiency characteristics.

[0015] Furthermore, collecting outdoor environment data, indoor environment data and user historical energy consumption data, and establishing the environmental heat demand characteristics includes the following steps:

[0016] S111, using a weather station to collect outdoor temperature data and outdoor humidity data, and using a temperature and humidity sensor to obtain indoor temperature data and indoor humidity data;

[0017] S112. Based on the historical energy consumption data of users, the users are divided into different types, and time series analysis is performed to identify the changing trend of heat demand of different user types;

[0018] S113. Based on the collected data, establish environmental heat demand characteristics, which include outdoor temperature, outdoor humidity, indoor temperature, indoor humidity at different time periods, and heat demands of different user types.

[0019] Furthermore, based on the characteristic parameter set and with the goal of minimizing energy costs, a multi-level decision model is established to determine the output strategy of the air source heat pump, including the following steps:

[0020] S21. Based on the characteristics of environmental heat demand, predict the user's heat demand and set the target water outlet temperature to minimize the energy cost and determine the primary output strategy of the air source heat pump;

[0021] S22. Combined with the time series characteristics of electricity prices, set a mid-level output strategy, take advantage of the energy storage characteristics of buildings, increase output when electricity prices are low, and reduce output when electricity prices are high, so as to reduce overall operating costs;

[0022] S23. Based on the operating efficiency characteristics of the heat pump, analyze the efficiency changes under different weather conditions, optimize the intermediate output strategy, and obtain the advanced output strategy.

[0023] Furthermore, based on the characteristics of environmental heat demand, the user heat demand is predicted, and the target water outlet temperature is set to minimize the energy cost. The primary output strategy of the air source heat pump is determined, including the following steps:

[0024] S211, based on the environmental heat demand characteristics, extract key environmental characteristics that affect the user's heat demand using principal component analysis;

[0025] S212, based on the extracted key environmental features and combined with the user's electricity consumption behavior pattern, the user's heat demand is predicted using time series analysis;

[0026] S213. According to the user's heat demand prediction results, combined with the performance curve and operation efficiency of the air source heat pump, the target water outlet temperature is set, and the primary output strategy is determined through linear programming.

[0027] Furthermore, in combination with the time series characteristics of electricity prices, an intermediate output strategy is set to utilize the energy storage characteristics of buildings to increase output when electricity prices are low and reduce output when electricity prices are high, so as to reduce the overall operating cost. The following steps are included:

[0028] S221. According to the time series characteristics of electricity prices, the daily electricity consumption is divided into peak electricity price period, parity electricity price period and valley electricity price period, and based on the preset output adjustment threshold, the output is increased in the valley electricity price period and the output is reduced in the peak electricity price period;

[0029] S222. Based on the heat capacity and thermal conductivity of the building, an energy balance equation is established to analyze the energy storage capacity of the building during different electricity price periods;

[0030] The expression of the energy balance equation is:

[0031]

[0032] In the formula, Q in Represents the energy provided by the air source heat pump to the building; Q out represents the energy loss of the building to the environment; m represents the mass of the building; c represents the specific heat capacity; It represents the rate of change of building temperature over time;

[0033] S223. Based on the primary output strategy and the energy storage capacity of the building during different electricity price periods, an intermediate output strategy is set to increase the output during the low electricity price period and reduce the output during the peak electricity price period.

[0034] Furthermore, based on the heat pump operating efficiency characteristics, the efficiency changes under different weather conditions are analyzed, and the intermediate output strategy is optimized. The advanced output strategy includes the following steps:

[0035] S231. According to the operating efficiency characteristics of the heat pump, a linear regression equation is established to analyze the relationship between the energy efficiency ratio of the air source heat pump and the outdoor temperature under different weather conditions, and to predict the changing trend of the air source heat pump efficiency;

[0036] S232. Based on the prediction results of air source heat pump efficiency, formulate the optimization rules of intermediate output strategy:

[0037] S233. Based on the optimization rules of the intermediate output strategy, the output of the air source heat pump is adjusted to obtain an advanced output strategy.

[0038] Furthermore, based on the multi-level decision model, setting constraints, determining the regulation strategy of the air source heat pump, and regulating the operating parameters of the air source heat pump in real time according to the regulation strategy include the following steps:

[0039] S31. Perform statistical analysis based on the historical operating data of the air source heat pump, extract key features, establish equipment performance curves, and set dynamic operating thresholds;

[0040] S32. Add advanced constraints of multi-level decision-making models based on equipment performance curves, including performance degradation rules and capacity limits of building energy storage;

[0041] S33. Use artificial intelligence algorithms to perform multi-objective optimization, formulate adaptive adjustment strategies, and dynamically adjust the operating parameters of air source heat pumps.

[0042] Furthermore, statistical analysis is performed based on the historical operation data of the air source heat pump to extract key features, establish equipment performance curves, and set dynamic operation thresholds, including the following steps:

[0043] S311, reading and cleaning the historical operation data of the air source heat pump, the historical operation data including the operation time, maintenance records and parts replacement information of the air source heat pump;

[0044] S312. Extract key features using statistical analysis methods, where the key features include maximum operating time, maintenance cycle, and replacement threshold;

[0045] S313. Establish equipment performance curve based on the extracted key features and set dynamic operation thresholds.

[0046] Furthermore, using artificial intelligence algorithms to perform multi-objective optimization, formulate adaptive adjustment strategies, and dynamically adjust the operating parameters of air source heat pumps include the following steps:

[0047] S331. Define optimization objectives, establish a multi-objective optimization problem for air source heat pumps, and use a weighted method to balance different objectives. The optimization objectives include energy efficiency, operating cost and equipment life of air source heat pumps.

[0048] S332. Use genetic algorithm to solve the multi-objective optimization problem of air source heat pump;

[0049] S333. Based on the solution results of the multi-objective optimization problem of the air source heat pump, an adaptive adjustment strategy is formulated, and the operating parameters of the air source heat pump are adjusted in real time. The adaptive adjustment strategy includes adjusting the output, operating time and maintenance cycle of the air source heat pump.

[0050] The beneficial effects of the present invention are:

[0051] (1) Cost and operation optimization: By establishing a characteristic parameter set and a multi-level decision-making model, the present invention can accurately predict user heat demand and formulate an operation strategy based on time-of-use electricity price data to meet the same heat supply demand at the lowest cost. At the same time, the output strategy of the air source heat pump is intelligently adjusted by using weather forecast and electricity price information. By analyzing the operating efficiency characteristics of the air source heat pump under different weather conditions, it can identify and utilize time periods with higher efficiency for heat supply, thereby reducing the overall operating cost. In addition, the present invention takes into account the energy storage characteristics of the building, increases the output for energy storage during the low electricity price period, and reduces the output during the peak period to utilize the stored heat, thereby improving the economy of the air source heat pump.

[0052] (2) Real-time adjustment and economy: Based on real-time data and dynamic adjustment strategies, the present invention can quickly respond to environmental changes and user needs, ensure stable heat supply at the lowest cost, and significantly reduce electricity expenses, so that users can enjoy the same service level while greatly reducing their economic burden. Secondly, the present invention also establishes efficiency attenuation functions and equipment performance curves to adjust the operation strategy in real time, effectively extending the life of the equipment and reducing long-term maintenance costs. By regularly updating the optimal solution and dynamically adjusting the operating parameters of the air source heat pump, the best operating state can be continuously maintained to achieve long-term economic benefits.

[0053] (3) Efficiency and intelligence: The present invention not only focuses on cost control, but also takes into account multiple objectives such as energy efficiency and equipment life. Through the multi-objective optimization algorithm, the control strategy is more comprehensive, efficient, easy to implement and maintain. In addition, by using artificial intelligence algorithms, the present invention realizes the intelligence and automation of operation strategies, reduces dependence on manual intervention, and improves the intelligence level of the system. At the same time, through in-depth analysis of historical operation data and extraction of key features, it can provide reliable data support for equipment maintenance and update decisions, further improving the overall performance and economic benefits of the air source heat pump system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 It is a flow chart of an intelligent control method of an air source heat pump based on cost-benefit analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0057] According to an embodiment of the present invention, a method for intelligently controlling an air source heat pump based on cost-benefit analysis is provided.

[0058] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1As shown, according to one embodiment of the present invention, a method for intelligently controlling an air source heat pump based on cost-benefit analysis is provided. The method for intelligently controlling an air source heat pump based on cost-benefit analysis comprises the following steps:

[0059] S1. Using air source heat pumps and monitoring equipment to collect operating data and establish a characteristic parameter set, wherein the characteristic parameter set includes environmental heat demand characteristics, time-of-use electricity price timing characteristics, and heat pump operating efficiency characteristics;

[0060] S2. Based on the characteristic parameter set and aiming at minimizing energy cost, a multi-level decision-making model is established to determine the output strategy of the air source heat pump;

[0061] S3. Based on the multi-level decision-making model, set constraints, determine the adjustment strategy of the air source heat pump, and adjust the operating parameters of the air source heat pump in real time according to the adjustment strategy.

[0062] In one embodiment, using an air source heat pump and a monitoring device to collect operating data, establishing a characteristic parameter set includes the following steps:

[0063] S11. Collect outdoor environment data, indoor environment data and user historical energy consumption data to establish environmental heat demand characteristics;

[0064] S12. Based on the time-of-use electricity price data, determine the electricity price levels in different time periods of the day, record the start and end time of the electricity price change period, and establish the electricity price time series characteristics;

[0065] Specifically, first collect time-of-use electricity price data. In this embodiment, collect the time-of-use electricity price of a certain area in the format of time period and electricity price (yuan / kWh). In this embodiment, the results are as follows:

[0066] 00:00-06:00, 0.3;

[0067] 06:00-08:00, 0.5;

[0068] 08:00-11:00, 0.8;

[0069] 11:00-13:00, 1.0;

[0070] 13:00-15:00, 0.8;

[0071] 15:00-18:00, 0.5;

[0072] 18:00-21:00, 1.0;

[0073] 21:00-24:00, 0.5;

[0074] Then determine the electricity price level, and obtain the valley electricity price is 0.3 yuan / kWh, the flat electricity price is 0.5 yuan / kWh, the peak electricity price is 0.8 yuan / kWh, and the peak electricity price is 1.0 yuan / kWh.

[0075] Specifically, the electricity price change period is then recorded in the format of the change time, the electricity price before the change, and the electricity price after the change. In this embodiment, the results are as follows:

[0076] 06:00, 0.3, 0.5;

[0077] 08:00, 0.5, 0.8;

[0078] 11:00, 0.8, 1.0;

[0079] 13:00, 1.0, 0.8;

[0080] 15:00, 0.8, 0.5;

[0081] 18:00, 0.5, 1.0;

[0082] 21:00, 1.0, 0.5;

[0083] 00:00, 0.5, 0.3;

[0084] Finally, the electricity price time series characteristics are established. In the above embodiment, a 24-hour electricity price time series array is created, and the electricity price is recorded once every hour, and the result is: [0.3, 0.3, 0.3, 0.3, 0.3, 0.5, 0.5, 0.8, 0.8, 0.8, 1.0, 1.0, 0.8, 0.8, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 0.5, 0.5, 0.5].

[0085] S13. Collect the heating capacity and input power of the air source heat pump under different weather conditions, calculate the energy efficiency ratio, and combine it with the outdoor environmental data to establish the heat pump operation efficiency characteristics.

[0086] Specifically, the operating data of the air source heat pump under different weather conditions are collected, including date, time, outdoor temperature (degrees Celsius), outdoor humidity (percentage), heating capacity (kW), and input power (kW). In this embodiment, the results are as follows:

[0087] 2024-01-15, 08:00, -5, 60, 10.5, 3.5;

[0088] 2024-01-15, 12:00, 0, 55, 12.0, 3.6;

[0089] 2024-01-15,16:00,-2,58,11.2,3.5...

[0090] Next, the energy efficiency ratio (energy efficiency coefficient) is calculated. The calculation formula is energy efficiency ratio = heating capacity / input power. The calculation result format is date, time, outdoor temperature (degrees Celsius), outdoor humidity (percentage), energy efficiency ratio. In this embodiment, the result is as follows:

[0091] 2024-01-15, 08:00, -5, 60, 3.00;

[0092] 2024-01-15, 12:00, 0, 55, 3.33;

[0093] 2024-01-15, 16:00, -2, 58, 3.20;

[0094] 2024-02-01, 08:00, 5, 70, 3.68;

[0095] 2024-02-01, 12:00, 10, 65, 3.97;

[0096] 2024-02-01,16:00,8,68,3.89...

[0097] Finally, the operating efficiency characteristics of the heat pump are established, including a) the relationship between temperature and energy efficiency ratio. Linear regression is used to analyze the relationship between temperature and energy efficiency ratio, and the energy efficiency ratio is obtained as 3.2+0.07*outdoor temperature; b) humidity influence. By calculating the average energy efficiency ratio in different humidity ranges, it is found that when the humidity is 50-60%, the average energy efficiency ratio is 3.27, and when the humidity is 60-70%, the average energy efficiency ratio is 3.64; c) time influence. The average energy efficiency ratio in different time periods is calculated, and the average energy efficiency ratio in the morning (6:00-10:00) is 3.34, the average energy efficiency ratio at noon (10:00-14:00) is 3.65, and the average energy efficiency ratio in the afternoon (14:00-18:00) is 3.55.

[0098] Specifically, a multivariate linear regression equation is established: energy efficiency ratio = 2.8 + 0.06 * outdoor temperature + 0.005 * outdoor humidity + 0.1 * time coefficient. In this embodiment, the time coefficient is set to morning = 1, noon = 2, and afternoon = 1.5.

[0099] In one embodiment, collecting outdoor environment data, indoor environment data and user historical energy consumption data to establish an environment heat demand characteristic includes the following steps:

[0100] S111, using a weather station to collect outdoor temperature data and outdoor humidity data, and using a temperature and humidity sensor to obtain indoor temperature data and indoor humidity data;

[0101] Specifically, the weather station is used to collect outdoor temperature and humidity data every hour, and the storage format is time, outdoor temperature (°C), outdoor relative humidity (%). In this embodiment, the results are as follows:

[0102] 2024-05-01 00:00, 15.2, 68;

[0103] 2024-05-01 01:00, 14.8, 70...

[0104] Specifically, a temperature and humidity sensor is installed indoors, and data is collected every 15 minutes. The storage format is time, indoor temperature (°C), and indoor relative humidity (%). In this embodiment, the results are as follows:

[0105] 2024-05-01 00:00, 22.5, 55;

[0106] 2024-05-01 00:15, 22.4, 56...

[0107] S112. Based on the historical energy consumption data of users, the users are divided into different types, and time series analysis is performed to identify the changing trend of heat demand of different user types;

[0108] Specifically, the user's historical energy consumption data is collected in the format of user ID, date, hour, and energy consumption (kWh). In this embodiment, the results are as follows:

[0109] 001, 2024-05-01, 00, 1.2;

[0110] 001, 2024-05-01, 01, 1.1...

[0111] Specifically, user types are divided based on their energy consumption patterns, and the average energy consumption of each user in different time periods is calculated; first, two time periods are defined: working time (9:00-18:00) and non-working time (other time), and then the proportion of energy consumption during working time is calculated = total energy consumption during working time / total energy consumption for the whole day, and finally, a rule for dividing user types is formulated: if the proportion of energy consumption during working time is >0.7, it is classified as a commercial office user, otherwise, it is classified as a residential user.

[0112] Specifically, a time series model is used (in this embodiment, a seasonal autoregressive moving average model, i.e., a SARIMA model, is used, which is an open source statistical model for analyzing and predicting time series data with seasonal characteristics) to perform time series analysis on each type of user: a. For residential users, daily and weekly periodicities are considered to analyze the user's daily energy consumption patterns, including morning and evening peak hours and weekend energy consumption characteristics; b. For commercial office users, a SARIMA model with a working day / non-working day distinction is used to analyze the energy consumption differences between working days and non-working days, and identify energy consumption peaks during working hours and troughs during non-working hours.

[0113] S113. Based on the collected data, establish environmental heat demand characteristics, which include outdoor temperature, outdoor humidity, indoor temperature, indoor humidity at different time periods, and heat demands of different user types.

[0114] Specifically, based on the collected data, an environmental heat demand characteristic matrix is ​​established, the format of which is time, outdoor temperature, outdoor humidity, indoor temperature, indoor humidity, residential user heat demand, and commercial office user heat demand. In this embodiment, the results are as follows:

[0115] 2024-05-01 00:00, 15.2, 68, 22.5, 55, 1.2, 0.3;

[0116] 2024-05-01 01:00, 14.8, 70, 22.3, 56, 1.1, 0.2;

[0117] 2024-05-01 09:00, 18.5, 65, 23.0, 52, 0.9, 1.5...

[0118] This matrix includes indoor and outdoor environmental parameters at different time periods and the thermal requirements of different user types, which can be used for subsequent analysis and decision-making.

[0119] In one embodiment, based on the characteristic parameter set and with the goal of minimizing energy cost, a multi-level decision model is established to determine the output strategy of the air source heat pump, including the following steps:

[0120] S21. Based on the characteristics of environmental heat demand, predict the user's heat demand and set the target water outlet temperature to minimize the energy cost and determine the primary output strategy of the air source heat pump;

[0121] S22. Combined with the time series characteristics of electricity prices, set a mid-level output strategy, take advantage of the energy storage characteristics of buildings, increase output when electricity prices are low, and reduce output when electricity prices are high, so as to reduce overall operating costs;

[0122] S23. Based on the operating efficiency characteristics of the heat pump, analyze the efficiency changes under different weather conditions, optimize the intermediate output strategy, and obtain the advanced output strategy.

[0123] In one embodiment, based on the environmental heat demand characteristics, the user heat demand is predicted, and the target water outlet temperature is set to minimize the energy cost. The primary output strategy of the air source heat pump is determined, including the following steps:

[0124] S211, based on the environmental heat demand characteristics, extract key environmental characteristics that affect the user's heat demand using principal component analysis;

[0125] S212, based on the extracted key environmental features and combined with the user's electricity consumption behavior pattern, the user's heat demand is predicted using time series analysis;

[0126] S213. According to the user's heat demand prediction results, combined with the performance curve and operation efficiency of the air source heat pump, the target water outlet temperature is set, and the primary output strategy is determined through linear programming.

[0127] Specifically, according to the environmental heat demand characteristics, principal component analysis is used to extract key environmental characteristics that affect the user's heat demand. In this embodiment, it is concluded through analysis that outdoor temperature and relative humidity are two main factors that affect the user's heat demand.

[0128] Specifically, based on the extracted key environmental features and combined with the user's electricity consumption behavior pattern, time series analysis is used to predict the user's heat demand. In this embodiment, the SARIMA model is used for prediction. The prediction results show that the user has a peak heat demand from 7 to 9 in the morning and from 6 to 8 in the evening on weekdays, and has a higher heat demand from 10 in the morning to 2 in the afternoon on weekends.

[0129] Specifically, according to the user's heat demand prediction results, combined with the performance curve and operation efficiency of the air source heat pump, the target outlet water temperature is set, and the primary output strategy is determined through linear programming. In this embodiment, the target outlet water temperature is set to 50°C from 7:00 to 9:00 in the morning on weekdays, and is set to 48°C from 6:00 to 8:00 in the evening, and is set to 45°C in other periods; the target outlet water temperature is set to 49°C from 10:00 a.m. to 2:00 p.m. on weekends, and is set to 45°C in other periods. The final primary output strategy is that during peak demand periods, the heat pump operates at 90% of the rated power; in other periods, the heat pump operates at 60% of the rated power.

[0130] In one embodiment, combining the time series characteristics of electricity prices, setting a mid-level output strategy, utilizing the energy storage characteristics of buildings, increasing output when electricity prices are low, and reducing output when electricity prices are high, so as to reduce the overall operating cost includes the following steps:

[0131] S221. According to the time series characteristics of electricity prices, the daily electricity consumption is divided into peak electricity price period, parity electricity price period and valley electricity price period, and based on the preset output adjustment threshold, the output is increased in the valley electricity price period and the output is reduced in the peak electricity price period;

[0132] Specifically, check the time-of-use electricity price policy of the local power company in the electricity price time series characteristics to obtain electricity price data for different periods of the day; according to the electricity price data, divide the day into peak electricity price period, parity electricity price period and valley electricity price period; for valley electricity price period, plan the heat pump to increase output during these periods to take advantage of the low electricity price to store heat in the building; for peak electricity price period, reduce the heat pump output to reduce operating costs, and set the heat pump to operate at a lower power.

[0133] Specifically, in this embodiment, the off-peak electricity price period is from 10 pm to 6 am, and the corresponding electricity price is 0.5 yuan / kWh; the parity electricity price period is from 6 am to 8 am and from 10 pm to midnight, and the corresponding electricity price is 0.8 yuan / kWh; the peak electricity price period is from 8 am to 10 pm, and the corresponding electricity price is 1.2 yuan / kWh.

[0134] Specifically, in the above embodiment, during the off-peak electricity price period, the air source heat pump output increases to 70% of the maximum capacity to fully utilize the low electricity price for energy storage; during the parity electricity price period, the air source heat pump maintains a medium output of 50% of the maximum capacity to meet basic heating or cooling needs; during the peak electricity price period, the air source heat pump output is reduced to 30% of the maximum capacity to reduce energy consumption and operating costs.

[0135] S222. Based on the heat capacity and thermal conductivity of the building, an energy balance equation is established to analyze the energy storage capacity of the building during different electricity price periods;

[0136] The expression of the energy balance equation is:

[0137]

[0138] In the formula, Q in Represents the energy provided by the air source heat pump to the building; Q out represents the energy loss of the building to the environment; m represents the mass of the building; c represents the specific heat capacity; It represents the rate of change of building temperature over time;

[0139] Specifically, according to different electricity price periods, the building's energy storage and release capabilities are calculated; the maximum amount of energy that can be stored during off-peak electricity price periods and the amount of energy that can be released during peak electricity price periods are determined.

[0140] S223. Based on the primary output strategy and the energy storage capacity of the building during different electricity price periods, an intermediate output strategy is set to increase the output during the low electricity price period and reduce the output during the peak electricity price period.

[0141] Specifically, the intermediate output strategy is formulated by combining the in-depth analysis of the electricity price time series characteristics in step S221 and the evaluation of the building energy storage characteristics in step S222. During the low electricity price period, the energy storage characteristics of the building are used to store heat by increasing the heat pump output; during the peak electricity price period, the heat pump output is reduced and the stored heat is used to meet the heat demand.

[0142] Specifically, in the above embodiment, the low electricity price period is from 10 pm to 6 am, and the peak period is from 6 am to 10 pm. According to the energy storage characteristics of the building and the electricity price data, an intermediate output strategy is set, with an output of 120% during the low period and 80% during the peak period.

[0143] In one embodiment, based on the heat pump operating efficiency characteristics, analyzing the efficiency changes under different weather conditions, and optimizing the intermediate output strategy, obtaining the advanced output strategy includes the following steps:

[0144] S231. According to the operating efficiency characteristics of the heat pump, a linear regression equation is established to analyze the relationship between the energy efficiency ratio of the air source heat pump and the outdoor temperature under different weather conditions, and to predict the changing trend of the air source heat pump efficiency;

[0145] S232. Based on the prediction results of air source heat pump efficiency, formulate the optimization rules of intermediate output strategy:

[0146] S233. Based on the optimization rules of the intermediate output strategy, the output of the air source heat pump is adjusted to obtain an advanced output strategy.

[0147] Specifically, based on the operating efficiency characteristics of the heat pump, the relationship between temperature and energy efficiency ratio is analyzed. In this embodiment, it is found through analysis that when the outdoor temperature decreases by 1°C, the energy efficiency ratio of the heat pump decreases by an average of 2%; when the relative humidity increases by 10%, the energy efficiency ratio of the heat pump decreases by an average of 1%.

[0148] Specifically, the intermediate output strategy is optimized by combining the analysis results of the heat pump operating efficiency characteristics and the equipment performance degradation analysis results. In the above embodiment, the final advanced output strategy is as follows: when the outdoor temperature is lower than 0°C, on the basis of the original intermediate strategy, the output during the off-peak period is increased to 130%, and the output during the peak period is increased to 85%; when the outdoor temperature is between 0°C and 10°C, the intermediate strategy is kept unchanged; when the outdoor temperature is higher than 10°C, the output during the off-peak period is reduced to 110%, and the output during the peak period is reduced to 75%.

[0149] In one embodiment, based on a multi-level decision model, setting constraints, determining an adjustment strategy for an air source heat pump, and regulating the operating parameters of the air source heat pump in real time according to the adjustment strategy include the following steps:

[0150] S31. Perform statistical analysis based on the historical operating data of the air source heat pump, extract key features, establish equipment performance curves, and set dynamic operating thresholds;

[0151] S32. Add advanced constraints of multi-level decision-making models based on equipment performance curves, including performance degradation rules and capacity limits of building energy storage;

[0152] Specifically, based on the equipment performance curve, adding advanced constraints of the multi-level decision model, including performance degradation rules and capacity limitations of building energy storage, includes the following steps:

[0153] S321. According to the equipment performance curve, use the exponential decay function to simulate the change of equipment efficiency with the running time, and formulate performance degradation rules to obtain the first constraint condition;

[0154] S322. Calculate the energy storage capacity of the building using a thermodynamic model to obtain a second constraint condition;

[0155] S323. Based on the first constraint condition and the second constraint condition, an efficiency attenuation function and a heat capacity limitation function are established and integrated into a multi-level decision model.

[0156] Specifically, based on the equipment performance curve established in step S313, an exponential decay function is used to further simulate the change of equipment efficiency over time. First, the exponential decay function is set to: COP(t) = COP0*e^(-λ*t), where COP0 is the initial COP value (set to 4.5 in this embodiment), t is the cumulative operating time (hours), and λ is the decay coefficient; then, by fitting historical data, λ = 0.00005 is obtained; then, performance degradation rules are formulated, including a. When COP drops to 90% of the initial value, a mild warning is triggered; b. When COP drops to 80% of the initial value, a moderate warning is triggered; c. When COP drops to 70% of the initial value, a serious warning is triggered, and maintenance or replacement is recommended; the first constraint condition finally obtained is expressed as: COP(t) ≥ 0.7*COP0, that is, COP(t) ≥ 3.15.

[0157] Specifically, the building in this embodiment is a standard office building with a construction area of ​​10,000 square meters and a floor height of 3 meters. The expression for calculating the energy storage capacity using the thermodynamic model is Q=m*c*ΔT, where Q is the storage energy (J), m is the mass of the building (kg), c is the specific heat capacity of the building (J / kg·K), and ΔT is the allowable temperature variation range (K).

[0158] Specifically, the average density of the building in the above embodiment is 300 kg / m 3 , the specific heat capacity is 1000 J / kg·K, the allowed temperature range is 2K, m = 10000*3*300 = 9000000 kg, Q = 9000000*1000*2 = 5000 kWh, so the maximum energy storage capacity of the building is 5000 kWh, and the second constraint condition is expressed as: 0≤Q s ≤5000kWh, where Q s Energy stored for current buildings.

[0159] Specifically, the efficiency decay function and the heat capacity limit function are integrated into the multi-level decision model, including: efficiency decay function COP(t) = 4.5*e^(-0.00005t), the first constraint condition COP(t) ≥ 3.15, and the heat capacity limit function Q s (t) = Q s (t-1)+Q in (t)-Q out (t), the second constraint 0≤Q s (t)≤5000kWh, where Q in (t) is the energy input by the heat pump at time t, Q out (t) is the energy consumed by the building at time t.

[0160] Specifically, the above two functions are integrated into a multi-level decision-making model. At the primary output strategy layer, the efficiency decay function is considered to adjust the maximum output of the heat pump. In this embodiment, when the COP drops to 4.05 (90% of the initial value), the maximum output is limited to 95% of the rated power; at the intermediate output strategy layer, the thermal capacity limitation function is used to optimize the energy storage strategy during the low electricity price period. In this embodiment, during the low electricity price period, the output of the heat pump must not only meet the current demand, but also consider energy storage, but the storage capacity must not exceed 5000kWh; at the advanced output strategy layer, the efficiency decay and thermal capacity limitation are comprehensively considered to dynamically adjust the operation strategy of the heat pump. In this embodiment, when the COP drops to 3.6 (80% of the initial value), the stored energy is used first to reduce the operation time of the heat pump to delay efficiency decay.

[0161] S33. Use artificial intelligence algorithms to perform multi-objective optimization, formulate adaptive adjustment strategies, and dynamically adjust the operating parameters of air source heat pumps.

[0162] In one embodiment, statistical analysis is performed based on historical operation data of the air source heat pump to extract key features, establish a device performance curve, and set a dynamic operation threshold, including the following steps:

[0163] S311, reading and cleaning the historical operation data of the air source heat pump, the historical operation data including the operation time, maintenance records and parts replacement information of the air source heat pump;

[0164] S312. Extract key features using statistical analysis methods, where the key features include maximum operating time, maintenance cycle, and replacement threshold;

[0165] S313. Establish equipment performance curve based on the extracted key features and set dynamic operation thresholds.

[0166] Specifically, the operating data of the past five years are exported from the data management system of the air source heat pump, including daily operating time, monthly maintenance records and replacement information of major components (compressors, fans and valves), and the data is cleaned. In this embodiment, records with daily operating time exceeding 24 hours are marked as abnormal, and missing maintenance records are supplemented by interpolation method. The cleaned data set contains 1825 days of valid operating records, 60 maintenance records and 15 major component replacement records.

[0167] Specifically, through statistical analysis (Python's scipy library statistics are used in this embodiment), it is calculated that the average daily operating time of the air source heat pump is 14.5 hours, and the standard deviation is 2.3 hours. Then, by analyzing the maintenance records, it is determined that the average maintenance cycle is 30 days, and the standard deviation is 5 days. For the main components, the analysis shows that the average service life of the compressor is 15,000 hours, the fan is 20,000 hours, and the valve is 25,000 hours.

[0168] Specifically, in the above embodiment, the maximum operating time is set to the average daily operating time plus two standard deviations, that is, 19.1 hours, the maintenance cycle is set to the average cycle minus one standard deviation, that is, 25 days, and the replacement threshold is set to 90% of the average service life, that is, 13,500 hours for the compressor, 18,000 hours for the fan, and 22,500 hours for the valve.

[0169] Specifically, first, a polynomial regression model is established. In this embodiment, the cumulative operating time is used as the independent variable, and the energy efficiency ratio (COP) of the air source heat pump is used as the dependent variable. The equipment performance curve is fitted to obtain the performance curve equation: COP = 4.5-0.0001*t-0.000000005*t^2, where t is the cumulative operating time (hours); then, based on this performance curve, the dynamic operating threshold is set, including when the COP drops to 85% of the initial value (i.e., 3.825), a maintenance warning is triggered; when the COP drops to 70% of the initial value (i.e., 3.15), a replacement warning is triggered.

[0170] Specifically, finally, according to the features extracted in step S312, the following dynamic operation thresholds are set: ① When the daily operation time exceeds 19.1 hours, an overload warning is triggered; ② When the time from the last maintenance exceeds 25 days, a maintenance reminder is triggered; ③ When the cumulative operation time of the compressor reaches 13,500 hours, the fan reaches 18,000 hours, or the valve reaches 22,500 hours, the corresponding replacement reminder is triggered.

[0171] In one embodiment, using artificial intelligence algorithms to perform multi-objective optimization, formulate adaptive adjustment strategies, and dynamically adjust the operating parameters of air source heat pumps include the following steps:

[0172] S331. Define optimization objectives, establish a multi-objective optimization problem for air source heat pumps, and use a weighted method to balance different objectives. The optimization objectives include energy efficiency, operating cost and equipment life of air source heat pumps.

[0173] S332. Use genetic algorithm to solve the multi-objective optimization problem of air source heat pump;

[0174] S333. Based on the solution results of the multi-objective optimization problem of the air source heat pump, an adaptive adjustment strategy is formulated, and the operating parameters of the air source heat pump are adjusted in real time. The adaptive adjustment strategy includes adjusting the output, operating time and maintenance cycle of the air source heat pump.

[0175] Specifically, three optimization objective functions are defined, including:

[0176] Energy efficiency target f1(x) = 1 / COP(x), where COP(x) is the energy efficiency coefficient under the current operating parameters;

[0177] Operation cost target f2(x) = Σ(P(t)*E(t)), where P(t) is the electricity price at time t and E(t) is the electricity consumption at time t;

[0178] Equipment life target f3(x) = T / L(x), where T is the expected life of the air source heat pump and L(x) is the estimated life under the current operating parameters.

[0179] Specifically, a weighted method is used to construct a comprehensive objective function, and the expression is F(x)=w1f1(x)+w2f2(x)+w3*f3(x), where w1, w2, w3 are the first, second, and third weight coefficients, respectively, and w1+w2+w3=1. In this embodiment, w1=0.4, w2=0.4, w3=0.2 are set to balance the three goals of energy efficiency, cost, and life.

[0180] Specifically, a genetic algorithm (implemented in this embodiment using Python's DEAP library) is used to solve the multi-objective optimization problem. The algorithm parameter settings include: population size (set to 100 in this embodiment); chromosome encoding, which is a real number encoding, and includes three decision variables in this embodiment: heat pump output, operating time, and maintenance cycle; crossover probability (set to 0.8 in this embodiment); mutation probability (set to 0.2 in this embodiment); number of iterations (set to 200 in this embodiment). The fitness function of the genetic algorithm is the comprehensive objective function F(x) defined above. In each iteration, the individual with the highest fitness is selected for crossover and mutation operations to generate a new population. After 200 iterations, the optimal solution x is obtained. * , including optimal output, optimal operating time and optimal maintenance cycle.

[0181] Specifically, the optimal solution x obtained by genetic algorithm is * , formulate three adaptive adjustment strategies, including:

[0182] 1) Output adjustment strategy: if the current COP is lower than 95% of the optimal COP, the output is adjusted to 90% of the optimal output; if the current COP is higher than 105% of the optimal COP, the output is adjusted to 110% of the optimal output, but not exceeding the rated power; in other cases, the optimal output remains unchanged.

[0183] 2) Operation time adjustment strategy: during the period of low electricity prices, if the building's energy storage has not reached the upper limit, the operation time will be extended to 120% of the optimal operation time; during the period of peak electricity prices, if the building's energy storage is sufficient, the operation time will be shortened to 80% of the optimal operation time; in other cases, the optimal operation time will remain unchanged.

[0184] 3) Maintenance cycle adjustment strategy: if the air source heat pump energy efficiency ratio decreases faster than expected (in this embodiment, the COP decrease rate is set to exceed 0.1 / month), the maintenance cycle will be shortened to 90% of the optimal maintenance cycle; if the equipment efficiency decreases slower than expected (in this embodiment, the COP decrease rate is set to be lower than 0.05 / month), the maintenance cycle will be extended to 110% of the optimal maintenance cycle; in other cases, the optimal maintenance cycle remains unchanged.

[0185] Specifically, the real-time adjustment process first collects operating data every hour, including COP, power consumption, and operating time; then, based on the collected data, calculates the current energy efficiency, operating cost, and estimated life; then, compares the calculated results with the optimal solution, and adjusts the operating parameters according to the above-mentioned adaptive adjustment strategy; finally, the adjusted parameters are applied to the control system of the air source heat pump; in addition, the genetic algorithm is re-run once a week to update the optimal solution x *, to adapt to possible environmental changes or equipment performance changes.

[0186] Through the above embodiments, technicians can achieve multi-objective optimization and adaptive adjustment of air source heat pumps. The method proposed in the present invention can strike a balance between energy efficiency, operating costs and equipment life, and dynamically adjust the strategy according to the actual operating conditions, thereby realizing intelligent operation and maintenance and optimized control of air source heat pumps.

[0187] In summary, with the help of the above-mentioned technical scheme of the present invention, by establishing a characteristic parameter set and a multi-level decision-making model, it is possible to accurately predict the user's heat demand, and formulate an operation strategy based on time-of-use electricity price data to meet the same heat supply demand at the lowest cost; by using weather forecast and electricity price information, the output strategy of the air source heat pump is intelligently adjusted, and by analyzing the operating efficiency characteristics of the air source heat pump under different weather conditions, it is possible to identify and utilize time periods with higher efficiency for heat supply, thereby reducing the overall operating cost; the present invention takes into account the energy storage characteristics of the building, increases the output for energy storage during the low electricity price period, and reduces the output during peak hours to utilize stored heat, further optimizing the economy of the system; secondly, based on real-time data and dynamic adjustment strategies, the present invention can quickly respond to environmental changes and user needs, ensure a stable heat supply at the lowest cost, significantly reduce electricity bills, and enable users to enjoy the same While improving the service level, the economic burden is greatly reduced; the present invention also establishes an efficiency decay function and equipment performance curve to adjust the operation strategy in real time, effectively prolonging the equipment life and reducing the long-term maintenance cost. By regularly updating the optimal solution and dynamically adjusting the operating parameters of the air source heat pump, the best operating state can be continuously maintained to achieve long-term economic benefits; in addition, the present invention not only focuses on cost control, but also takes into account multiple goals such as energy efficiency and equipment life. Through the multi-objective optimization algorithm, the control strategy is made more comprehensive, efficient, easy to implement and maintain; using artificial intelligence algorithms, the present invention realizes the intelligence and automation of the operation strategy, reduces the dependence on manual intervention, and improves the intelligence level of the system; at the same time, through in-depth analysis of historical operation data and extraction of key features, it can provide reliable data support for equipment maintenance and update decisions, further improving the overall performance and economic benefits of the air source heat pump system.

[0188] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The air source heat pump intelligent control method based on cost-benefit analysis includes the following steps: S1. Using air source heat pumps and monitoring equipment to collect operating data and establish a characteristic parameter set, wherein the characteristic parameter set includes environmental heat demand characteristics, time-of-use electricity price timing characteristics, and heat pump operating efficiency characteristics; S2. Based on the characteristic parameter set and aiming at minimizing energy cost, a multi-level decision-making model is established to determine the output strategy of the air source heat pump; S3. Based on the multi-level decision-making model, set constraints, determine the adjustment strategy of the air source heat pump, and adjust the operating parameters of the air source heat pump in real time according to the adjustment strategy.

2. According to claim 1, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The method of collecting operation data using air source heat pump and monitoring equipment and establishing characteristic parameter set comprises the following steps: S11. Collect outdoor environment data, indoor environment data and user historical energy consumption data to establish environmental heat demand characteristics; S12. Based on the time-of-use electricity price data, determine the electricity price levels in different time periods of the day, record the start and end time of the electricity price change period, and establish the electricity price time series characteristics; S13. Collect the heating capacity and input power of the air source heat pump under different weather conditions, calculate the energy efficiency ratio, and combine it with the outdoor environmental data to establish the heat pump operation efficiency characteristics.

3. According to claim 2, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The collection of outdoor environment data, indoor environment data and user historical energy consumption data to establish the environmental heat demand characteristics includes the following steps: S111, using a weather station to collect outdoor temperature data and outdoor humidity data, and using a temperature and humidity sensor to obtain indoor temperature data and indoor humidity data; S112. Based on the historical energy consumption data of users, the users are divided into different types, and time series analysis is performed to identify the changing trend of heat demand of different user types; S113. Based on the collected data, establish environmental heat demand characteristics, where the environmental heat demand characteristics include outdoor temperature, outdoor humidity, indoor temperature, indoor humidity at different time periods, and heat demands of different user types.

4. According to claim 1, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The method of establishing a multi-level decision-making model based on a characteristic parameter set and aiming at minimizing energy costs to determine the output strategy of an air source heat pump includes the following steps: S21. Based on the characteristics of environmental heat demand, predict the user's heat demand and set the target water outlet temperature to minimize the energy cost and determine the primary output strategy of the air source heat pump; S22. Combined with the time series characteristics of electricity prices, set a mid-level output strategy, take advantage of the energy storage characteristics of buildings, increase output when electricity prices are low, and reduce output when electricity prices are high, so as to reduce overall operating costs; S23. Based on the operating efficiency characteristics of the heat pump, analyze the efficiency changes under different weather conditions, optimize the intermediate output strategy, and obtain the advanced output strategy.

5. According to claim 4, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The method of predicting user heat demand based on environmental heat demand characteristics and setting a target water outlet temperature to minimize energy costs and determine the primary output strategy of the air source heat pump includes the following steps: S211, based on the environmental heat demand characteristics, extract key environmental characteristics that affect the user's heat demand using principal component analysis; S212, based on the extracted key environmental features and combined with the user's electricity consumption behavior pattern, the user's heat demand is predicted using time series analysis; S213. According to the user's heat demand prediction results, combined with the performance curve and operation efficiency of the air source heat pump, the target water outlet temperature is set, and the primary output strategy is determined through linear programming.

6. According to claim 4, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The method of setting a mid-level output strategy in combination with the time series characteristics of electricity prices, utilizing the energy storage characteristics of buildings, increasing output when electricity prices are low, and reducing output when electricity prices are high, so as to reduce overall operating costs includes the following steps: S221. According to the time series characteristics of electricity prices, the daily electricity consumption is divided into peak electricity price period, parity electricity price period and valley electricity price period, and based on the preset output adjustment threshold, the output is increased in the valley electricity price period and the output is reduced in the peak electricity price period; S222. Based on the heat capacity and thermal conductivity of the building, an energy balance equation is established to analyze the energy storage capacity of the building during different electricity price periods; The expression of the energy balance equation is: In the formula, Q in Represents the energy provided by the air source heat pump to the building; Q out represents the energy loss of the building to the environment; m represents the mass of the building; c represents the specific heat capacity; It represents the rate of change of building temperature over time; S223. Based on the primary output strategy and the energy storage capacity of the building during different electricity price periods, an intermediate output strategy is set to increase the output during the low electricity price period and reduce the output during the peak electricity price period.

7. According to claim 4, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The method of analyzing the efficiency changes under different weather conditions based on the heat pump operation efficiency characteristics and optimizing the intermediate output strategy to obtain the advanced output strategy includes the following steps: S231. According to the operating efficiency characteristics of the heat pump, a linear regression equation is established to analyze the relationship between the energy efficiency ratio of the air source heat pump and the outdoor temperature under different weather conditions, and to predict the changing trend of the air source heat pump efficiency; S232. Based on the prediction results of air source heat pump efficiency, formulate the optimization rules of intermediate output strategy: S233. Based on the optimization rules of the intermediate output strategy, the output of the air source heat pump is adjusted to obtain an advanced output strategy.

8. According to claim 1, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The method of setting constraint conditions based on a multi-level decision model, determining an adjustment strategy for an air source heat pump, and regulating the operating parameters of the air source heat pump in real time according to the adjustment strategy includes the following steps: S31. Perform statistical analysis based on the historical operation data of the air source heat pump, extract key features, establish equipment performance curves, and set dynamic operation thresholds; S32. Add advanced constraints of multi-level decision-making models based on equipment performance curves, including performance degradation rules and capacity limits of building energy storage; S33. Use artificial intelligence algorithms to perform multi-objective optimization, formulate adaptive adjustment strategies, and dynamically adjust the operating parameters of air source heat pumps.

9. According to claim 8, an intelligent control method for air source heat pump based on cost-benefit analysis, It is characterized in that The method of performing statistical analysis based on the historical operation data of the air source heat pump, extracting key features, establishing the equipment performance curve, and setting the dynamic operation threshold value includes the following steps: S311, reading and cleaning the historical operation data of the air source heat pump, wherein the historical operation data includes the operation time, maintenance records and parts replacement information of the air source heat pump; S312, extracting key features using a statistical analysis method, wherein the key features include a maximum operating time, a maintenance cycle, and a replacement threshold; S313. Establish equipment performance curve based on the extracted key features and set dynamic operation thresholds.

10. The intelligent control method of air source heat pump based on cost-benefit analysis according to claim 8, It is characterized in that The method of using artificial intelligence algorithm to perform multi-objective optimization, formulate adaptive adjustment strategy, and dynamically adjust the operating parameters of the air source heat pump includes the following steps: S331. Define optimization objectives, establish a multi-objective optimization problem for an air source heat pump, and use a weighted method to balance different objectives, wherein the optimization objectives include energy efficiency, operating cost, and equipment life of the air source heat pump; S332. Use genetic algorithm to solve the multi-objective optimization problem of air source heat pump; S333. According to the solution results of the multi-objective optimization problem of the air source heat pump, an adaptive adjustment strategy is formulated, and the operating parameters of the air source heat pump are adjusted in real time. The adaptive adjustment strategy includes adjusting the output, operating time and maintenance cycle of the air source heat pump.

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