A method for high-efficiency and energy-saving cooling and heating of central air conditioner

Abstract

The present application relates to a method for efficient and energy-saving cooling and heating of a central air conditioner. The method comprises: obtaining user expectation information, air conditioning equipment information, and environmental parameter information; judging the effectiveness of a first central air conditioner operation strategy based on the user expectation information, air conditioning equipment information, and environmental parameter information; if the first central air conditioner operation strategy is judged to be invalid, inputting the user expectation information and environmental parameter information into an edge strategy generation model to generate a second central air conditioner operation strategy; updating the first central air conditioner operation strategy based on the second central air conditioner operation strategy, and adjusting the central air conditioner's refrigeration equipment information and / or heating equipment information based on the updated first central air conditioner operation strategy. The use of this method can improve user experience, improve operational efficiency, reduce energy consumption, enhance system adaptability, and simplify management processes.

Description

Technical Field

[0001] The present invention belongs to the field of central air-conditioning systems, and in particular relates to a method for high-efficiency and energy-saving cooling and heating of a central air-conditioning system. Background Art

[0002] With the development of modern architecture, central air conditioning systems are widely used in various places, such as commercial buildings, office buildings, and large residential buildings. However, traditional central air conditioning systems generally adopt a relatively fixed operating mode, which makes it difficult to flexibly and accurately adjust according to actual needs. As a result, in some cases, central air conditioning cannot dynamically adjust to real-time load conditions and environmental changes, resulting in excessive energy consumption and low energy efficiency.

[0003] Traditionally, some existing central air conditioning energy-saving control systems can achieve a certain degree of energy savings. However, most systems rely solely on environmental data. Energy-saving strategies developed without fully considering user behavior often fail to achieve optimal energy savings and user experience. Furthermore, existing technologies often adopt a single optimization objective when formulating energy-saving strategies, failing to balance energy conservation with user comfort. Furthermore, existing central air conditioning cooling and heating source centralized control systems have a low level of integration and lack effective coordination and integration between modules, making system maintenance and upgrades difficult. Summary of the Invention

[0004] Based on this, it is necessary to provide a method for efficient and energy-saving cooling and heating of central air conditioning in response to the above technical problems, which can improve the comprehensive performance of the central air conditioning system in terms of operating efficiency, energy utilization, user comfort, adaptability and management convenience.

[0005] The present application provides a method for high-efficiency and energy-saving cooling and heating of a central air conditioner, comprising:

[0006] Acquire user expectation information, air conditioning equipment information, and environmental parameter information, where the air conditioning equipment information includes refrigeration equipment information and heating equipment information;

[0007] Determining the effectiveness of a first central air-conditioning operation strategy based on user expectation information, air-conditioning equipment information, and environmental parameter information, where the first central air-conditioning operation strategy is used to represent a real-time operating status of the central air-conditioning equipment;

[0008] If the first central air-conditioning operation strategy is determined to be invalid, the user expectation information and the environmental parameter information are input into the edge strategy generation model to generate a second central air-conditioning operation strategy, which is used to represent the expected operating state of the central air-conditioning air conditioning equipment;

[0009] The first central air-conditioning operation strategy is updated based on the second central air-conditioning operation strategy, and the refrigeration equipment information and / or heating equipment information of the central air-conditioning is adjusted based on the updated first central air-conditioning operation strategy.

[0010] The above-mentioned method of efficient and energy-saving cooling and heating of central air conditioning can better understand the user's personalized needs by obtaining user expectation information, thereby providing a more accurate and comfortable indoor environment; through the edge policy generation model, the system can generate a more optimized operation strategy based on user expectation information and environmental parameter information, thereby reducing unnecessary equipment startup and shutdown, avoiding frequent equipment switching, and thus improving the equipment's operating efficiency; through the edge policy generation model, the system can generate a more optimized operation strategy to enable the equipment to operate in the most energy-saving mode; by automatically judging the effectiveness of the operation strategy and automatically generating a new operation strategy when the strategy fails, it can reduce manual intervention and make the management of the central air-conditioning system more automated and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 A schematic diagram of an application environment for a method for high-efficiency and energy-saving cooling and heating of a central air conditioner provided in one embodiment of the present application;

[0013] Figure 2 A flow chart of a method for efficient and energy-saving cooling and heating of a central air conditioner provided in one embodiment of the present application;

[0014] Figure 3 A schematic diagram of a central air-conditioning system for efficient and energy-saving cooling and heating according to an embodiment of the present application;

[0015] Figure 4 A flow chart of another method for efficient and energy-saving cooling and heating of a central air conditioner provided in one embodiment of the present application;

[0016] Figure 5 A schematic diagram of a flow chart of control strategy prediction and control model optimization provided in one embodiment of the present application;

[0017] Figure 6 A schematic diagram of a flow chart for regulating air conditioning equipment based on a control strategy provided in one embodiment of the present application;

[0018] Figure 7A flow chart of another method for efficient and energy-saving cooling and heating of a central air conditioner provided in one embodiment of the present application;

[0019] Figure 8 A schematic structural diagram of a central air-conditioning system with high efficiency and energy saving for cooling and heating is provided in accordance with one embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0021] The central air-conditioning high-efficiency energy-saving cooling and heating method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, edge computing device 101 communicates with working device 103 and sensor device 102 via a communication channel. A data storage system can store data that edge computing device 101 needs to process. The data storage system can be integrated with edge computing device 101 or placed on a cloud or other network server. Edge computing device 101 can generate control information based on the sensor data acquired by sensor device 102. Edge computing device 101 can control the operating status of working device 103 based on the generated control information. Edge computing device 101 can include, but is not limited to, various microcontrollers, personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, etc. Portable wearable devices can include smart watches, smart bracelets, head-mounted devices, etc. Sensor devices 102 can include, but are not limited to, temperature sensors, humidity sensors, infrared sensors, radio frequency sensors, Bluetooth sensors, air quality sensors, flow sensors, and light sensors. The working equipment 103 may include, but is not limited to, a compressor, a condenser, an evaporator, a throttle, a fan coil, a cooling tower, a refrigeration pump, a fresh air unit, a boiler, a heat pump, a solar collector, an electric heater, and a heat recovery device.

[0022] In an exemplary embodiment, Figure 2 As shown, a method for high-efficiency and energy-saving cooling and heating of central air conditioner is provided. Figure 1 Taking the edge computing device 101 in FIG. 1 as an example, the following steps S201 to S204 are included. Among them:

[0023] Step S201: Acquire user expectation information, air conditioning equipment information, and environmental parameter information.

[0024] Specifically, the edge computing device 101 can obtain user expectation information, air conditioning equipment information and environmental parameter information. The air conditioning equipment information may include refrigeration equipment information and heating equipment information.

[0025] Optionally, the user expected information may include but is not limited to expected temperature setting, expected humidity control range, expected switch setting, expected scene mode and expected partition control.

[0026] Optionally, the air conditioning equipment information may include but is not limited to equipment type information, operation mode information, energy efficiency ratio information, operation load ratio information and fault information.

[0027] Optionally, the environmental parameter information may include, but is not limited to, indoor and outdoor environment difference information, expected environment information, and real-time work zone environment information. The indoor and outdoor environment difference information may include, but is not limited to, air pollution index difference information, temperature difference information, humidity difference information, solar radiation information, wind field information, and seasonal information.

[0028] Step S202 : determining the effectiveness of the first central air-conditioning operation strategy based on user expectation information, air conditioning equipment information, and environmental parameter information.

[0029] Specifically, the edge computing device 101 can judge the effectiveness of the first central air-conditioning operation strategy that has been deployed and is being implemented based on the acquired user expectation information, air-conditioning equipment information and environmental parameter information. The first central air-conditioning operation strategy can be used to characterize the real-time operating status of the central air-conditioning air-conditioning equipment.

[0030] Illustratively, the edge computing device 101 can evaluate the effectiveness of the first central air conditioning operation strategy based on the acquired user expectation information, air conditioning equipment information, and environmental parameter information from three dimensions: user demand satisfaction, equipment operating efficiency, and environmental adaptability. The user demand satisfaction dimension may include, but is not limited to, determining whether the set user expectation information has been met after a preset response cycle has elapsed; the equipment operating efficiency dimension may include, but is not limited to, determining the degree of deviation between the equipment operating status and the designed performance and whether the load distribution of each device in a multi-unit system is reasonable; and environmental adaptability may include, but is not limited to, determining whether the environmental parameters exceed the equipment design operating conditions and determining whether the air conditioning equipment information matches the user expectation information under the environmental conditions corresponding to the environmental parameter information.

[0031] Optionally, the expression of the first central air-conditioning operation strategy effectiveness evaluation model can be:

[0032] ;

[0033] Where, This is the effectiveness evaluation model for the first central air-conditioning operation strategy. and are user expectation coefficient and device status coefficient respectively, 、 and They are user expectation item, environment adaptation item and device status item respectively. is the user's expected fuzzy function, for The actual value of the environmental parameters at the moment, for The user's expected environmental parameter value at the moment, is the total number of environmental parameters, For the The weight coefficient of each environmental parameter, For the The environmental adaptation fuzzy function of the environmental parameters, 、 and Respectively The outdoor value, indoor value and reference value of each environmental parameter, is the total number of air conditioning equipment, 、 、 and Respectively The weight coefficient, actual energy consumption, set energy consumption and performance coefficient of each air conditioning equipment, is the total physical properties of the air regulated by the central air conditioner, and They are respectively the first The weight parameters and allowable error parameters of the physical properties of air, and They are The moment The measured and set values ​​of the physical properties of air.

[0034] In step S203 , if the first central air-conditioning operation strategy is determined to be invalid, the user expectation information and the environmental parameter information are input into the edge strategy generation model to generate a second central air-conditioning operation strategy.

[0035] For details, please refer to Figure 3 If the first central air-conditioning operation strategy is judged to be invalid, the edge computing device 101 can input the user expectation information and environmental parameter information into the edge strategy generation model mounted on the edge computing device 101 to generate a second central air-conditioning operation strategy. The second central air-conditioning operation strategy can be used to characterize the expected operating status of the central air-conditioning air conditioning equipment.

[0036] Optionally, the expected operating state of the air-conditioning equipment may include, but is not limited to, specific parameters such as the start-up time, operating power, and adjustment mode of the refrigeration equipment and / or heating equipment.

[0037] Optionally, edge policy generation models may include, but are not limited to, BP neural network models, fuzzy logic models, deep learning models optimized using the TinyML algorithm, and reinforcement learning models. Deep learning models optimized using the TinyML algorithm include, but are not limited to, convolutional neural network models and recurrent neural networks (RNNs) and their variants (LSTM, GRU). Reinforcement learning models optimized using the TinyML algorithm include, but are not limited to, deep Q-network models.

[0038] Step S204: updating the first central air-conditioning operation strategy based on the second central air-conditioning operation strategy, and adjusting the refrigeration equipment information and / or heating equipment information of the central air-conditioning based on the updated first central air-conditioning operation strategy.

[0039] Specifically, the edge computing device 101 can update the current first central air-conditioning operation strategy based on the second central air-conditioning operation strategy generated by the edge strategy generation model mounted on the edge computing device 101, and adjust the cooling equipment information and / or heating equipment information of the central air-conditioning based on the updated first central air-conditioning operation strategy.

[0040] Illustratively, when in a specific scenario that requires simultaneous cooling and heating, the edge computing device 101 can adjust the cooling equipment information and heating equipment information of the central air conditioner based on the updated first central air conditioner operation strategy; when only cooling is required, the edge computing device 101 can adjust the cooling equipment information of the central air conditioner based on the updated first central air conditioner operation strategy; when only heating is required, the edge computing device 101 can adjust the heating equipment information of the central air conditioner based on the updated first central air conditioner operation strategy.

[0041] In the above-mentioned method of efficient and energy-saving cooling and heating of central air conditioners, by real-time collection of user expectations, equipment status and environmental parameters, combined with edge computing to generate the optimal operating strategy, it is possible to avoid the energy waste caused by traditional fixed modes; by immediately generating a new strategy when it is detected that the current strategy cannot meet the energy efficiency target, it is possible to prevent inefficient operation from continuing to consume energy; by deploying the policy generation model at the edge, bypassing the cloud data transmission delay, it is possible to quickly respond to environmental changes, ensure that equipment parameters are adjusted quickly and accurately, and when the network is interrupted, the edge can still maintain operation to avoid system paralysis.

[0042] In one of the optional embodiments, please refer to Figure 3 and Figure 4, inputting user expectation information and environmental parameter information into the edge strategy generation model to generate the second central air-conditioning operation strategy, and then further including:

[0043] Step S304: input the user expectation information and the environmental parameter information into the central strategy generation model to generate a third central air-conditioning operation strategy.

[0044] Specifically, the edge computing device can transmit user expectation information and environmental parameter information through a communication channel and input it into the central computing device, and can input the user expectation information and environmental parameter information into the central strategy generation model carried by the central computing device to generate a third central air-conditioning operation strategy. The central computing device can be an Internet of Things central device.

[0045] Step S305: Update the second central air-conditioning operation strategy based on the third central air-conditioning operation strategy.

[0046] Step S307: Optimize the edge strategy generation model based on the third central air-conditioning operation strategy.

[0047] Specifically, the edge computing device can optimize the edge strategy generation model that the edge computing device can carry based on the third central air-conditioning operation strategy, and the edge computing device can be a networked edge device.

[0048] In the above-mentioned method of efficient and energy-saving cooling and heating of central air conditioning, by simultaneously inputting user expectation information and environmental parameter information into the edge policy generation model and the central policy generation model, the second and third central air conditioning operation strategies are generated, which can realize a multi-layer optimization mechanism, better cope with complex and changing environments and user needs, and improve the overall optimization effect; through the collaborative work of the edge policy generation model and the central policy generation model, real-time feedback and adjustment can be achieved to ensure the accuracy and effectiveness of the strategy; by optimizing the edge policy generation model based on the third central air conditioning operation strategy, the performance of the edge policy generation model can be continuously improved, and then the edge policy generation model can be continuously learned and improved in actual applications, thereby improving the quality and accuracy of its generated strategy; through the collaborative work of the edge policy generation model and the central policy generation model, automatic operation strategy optimization can be realized, reducing the need for manual intervention, which can not only reduce management complexity, but also improve the reliability and stability of the system.

[0049] In one of the optional embodiments, please refer to Figure 3 The central policy generation model carried by the central computing device can be a deep learning neural network model, and the central policy generation model can include an improved convolution module and an improved converter module.

[0050] The edge policy generation model carried by the edge computing device can be an improved BP neural network, and the improved BP neural network is obtained by distilling the central policy generation model into the initial BP neural network.

[0051] Among them, the loss function of the central strategy generation model is a multi-objective comprehensive loss function, and the expression of the multi-objective comprehensive loss function is:

[0052] ;

[0053] Where, is a multi-objective comprehensive loss function, 、 、 and They are energy efficiency loss, regulation target loss, strategy stability loss and strategy classification loss, 、 、 and They are energy efficiency loss parameter, adjustment target loss parameter, strategy stability loss parameter and strategy classification loss parameter, respectively. is the total number of air conditioning equipment, 、 and Respectively The actual energy consumption, set energy consumption and performance coefficient of each air conditioning equipment, is the number of environmental data samples, is the total physical properties of the air regulated by the central air conditioner, and They are respectively the first The weight parameters and allowable error parameters of the physical properties of air, and Respectively The second sampling The measured and set values ​​of the physical properties of the air, is the total number of adjustment parameters of the third central air-conditioning operation strategy and the first central air-conditioning operation strategy, and The third central air-conditioning operation strategy The adjustment parameters and The output corresponding to the adjustment parameter is and They are the first central air-conditioning operation strategy The adjustment parameters and The output corresponding to the adjustment parameter is and Respectively The maximum and minimum output values ​​corresponding to the adjustment parameters, is the adjustment parameter coefficient, To adjust the parameter output coefficient, is the number of samples, is the number of categories of the third central air-conditioning operation strategy, is the balance factor, is the focusing parameter, Generate a model for the central strategy The samples belong to The predicted probability of the class.

[0054] Schematically, when the air conditioning equipment is a refrigeration equipment, the performance coefficient of the air conditioning equipment can be:

[0055] ;

[0056] Where, is the coefficient of performance of the refrigeration equipment, is the cooling rate, is the actual input power.

[0057] Schematically, when the air conditioning equipment is a heating equipment, the performance coefficient of the air conditioning equipment can be:

[0058] ;

[0059] Where, is the heating performance coefficient of the heating equipment, is the heating rate, is the actual input power.

[0060] In the above-mentioned method of efficient and energy-saving cooling and heating of central air conditioning, by combining deep learning and improved BP neural network and adopting a multi-objective comprehensive loss function, it is possible to comprehensively consider multiple aspects such as energy efficiency, adjustment objectives, strategy stability and strategy classification, and generate efficient, accurate and stable operation strategies, thereby significantly improving the operating efficiency and management effect of the central air-conditioning system.

[0061] In one of the optional embodiments, please refer to Figure 5 The method for high-efficiency and energy-saving cooling and heating of central air conditioning further comprises:

[0062] Step S501: Obtain information on the proportion of time during which the first central air-conditioning operation strategy is judged to be invalid within a preset sampling period.

[0063] Specifically, the expression of duration ratio information is:

[0064] ;

[0065] Where, is the duration ratio information, is the sampling period, is the number of times the first central air-conditioning operation strategy is judged to be invalid within the sampling period, For the The sampling history coefficient attenuation term when the first central air-conditioning operation strategy is judged to be ineffective, For the The sampling history time when the first central air-conditioning operation strategy is judged to be invalid, For the The strategy adjustment fuzzy coefficient item when the first central air-conditioning operation strategy is judged to be ineffective is: For the The adjustment parameters when the first central air-conditioning operation strategy is judged to be invalid, For the The adjustment parameters of the second central air-conditioning operation strategy corresponding to the first central air-conditioning operation strategy are determined to be invalid. For the The duration when the first central air-conditioning operation strategy is judged to be invalid.

[0066] Optional, The sampling history coefficient attenuation term when the first central air-conditioning operation strategy is judged to be ineffective The expression can be:

[0067] ;

[0068] Where, and are the proportional coefficient and exponential decay coefficient of the sampling history, respectively. is the upper limit coefficient of sampling history impact.

[0069] In step S502, if the duration ratio information exceeds the preset normal threshold of the working environment, the future change trend of the central air-conditioning operation demand is predicted based on the edge strategy prediction model, and the fourth central air-conditioning operation strategy and the first activation timing information of the fourth central air-conditioning operation strategy are generated.

[0070] For details, please refer to Figure 3 If the duration ratio information exceeds the preset normal threshold of the working environment, the edge computing device can predict the future change trend of the central air-conditioning operation demand based on the edge strategy prediction model mounted on the edge computing device, and generate a fourth central air-conditioning operation strategy and the first activation timing information corresponding to the fourth central air-conditioning operation strategy. The fourth central air-conditioning operation strategy is used to update the first central air-conditioning operation strategy at the time corresponding to the first activation timing information.

[0071] In the above-mentioned method of efficient and energy-saving cooling and heating of central air conditioning, by obtaining information on the proportion of time that the first central air conditioning operation strategy is judged to be invalid within a preset sampling period, the system can monitor the effectiveness of the operation strategy in real time; by updating the first central air conditioning operation strategy at predicted future times, the central air conditioning can better adapt to changes in the environment and user needs, and improve the accuracy and effectiveness of the operation strategy.

[0072] In one of the optional embodiments, please refer to Figure 5 If the duration ratio information exceeds the preset normal environment threshold, the future central air-conditioning operation demand change trend is predicted based on the edge strategy prediction model, and the fourth central air-conditioning operation strategy and the activation timing information of the fourth central air-conditioning operation strategy are generated. After that, it also includes:

[0073] Step S503: input the user expectation information and the environmental parameter information into the central strategy prediction model to generate a fifth central air-conditioning operation strategy and second activation timing information of the fifth central air-conditioning operation strategy.

[0074] Step S504: updating the fourth central air-conditioning operation strategy based on the fifth central air-conditioning operation strategy, and updating the first activation timing information based on the second activation timing information.

[0075] Step S505 , optimizing the edge strategy generation model, the edge strategy prediction model, and the central strategy generation model based on the fifth central air-conditioning operation strategy.

[0076] For details, please refer to Figure 3 , the edge policy generation model and edge policy prediction model mounted on the edge computing device and the central policy generation model mounted on the central computing device can be optimized based on the fifth central air-conditioning operation strategy mounted on the central computing device.

[0077] In the above-mentioned method of efficient and energy-saving cooling and heating of central air conditioning, by combining the edge strategy prediction model and the central strategy prediction model, it is possible to predict in advance the changing trend of future central air conditioning operation demand, generate and update reasonable operation strategies and activation timing information, thereby improving the operation efficiency, response speed and stability of the central air conditioning, as well as improving user satisfaction and the overall performance of the central air conditioning.

[0078] In one optional embodiment, the edge strategy prediction model may be a regression prediction model.

[0079] The central policy prediction model can be constructed based on an improved converter model and an improved long short-term memory network, and the improved converter model can include a residual convolution module.

[0080] In one of the optional embodiments, Figure 6As shown, user expectation information, air conditioning equipment information and environmental parameter information are obtained, including:

[0081] Step S601: Acquire indoor environment information, outdoor environment information, user tag information, and user control instructions.

[0082] Specifically, the edge computing center can obtain indoor environment information, outdoor environment information, user tag information, and user control instructions based on sensor devices. Among them, user control instructions can include mode control instructions, target control instructions, and wind speed control instructions.

[0083] Optionally, the target control instruction may include a partitioned temperature control instruction, a partitioned humidity control instruction, and a partitioned air quality control instruction.

[0084] Step S602: Generate environmental parameter information based on the indoor environment information and the outdoor environment information.

[0085] Step S603: Generate user expectation information based on user tag information, user control instructions and environmental parameter information.

[0086] Optionally, the edge computing device can obtain user tag information generated based on user registration information, questionnaires, and historical usage records, including information about the user's basic attributes, living habits, and usage scenarios. The edge computing device can also obtain user control instructions and environmental parameter information based on sensors. Environmental parameter information may include, but is not limited to, outdoor temperature, outdoor humidity, outdoor light intensity, outdoor air quality, indoor temperature, indoor humidity, indoor light intensity, indoor air quality, seasonal information, weather information, and indoor occupancy information.

[0087] Step S604: setting the air conditioning equipment information to refrigeration equipment information and / or heating equipment information based on the environmental parameter information and the user expectation information.

[0088] Schematically, the edge computing device can identify the working scenario of the central air conditioner based on environmental parameter information and user expectation information - if cooling and heating are required at the same time, the air conditioning equipment information is set to cooling equipment information and heating equipment information; if only cooling is required, the air conditioning equipment information is set to cooling equipment information; if only heating is required, the air conditioning equipment information is set to heating equipment information.

[0089] In the above-mentioned method of efficient and energy-saving cooling and heating of central air conditioning, by comprehensively collecting user expectation information, air conditioning equipment information and environmental parameter information, and conducting comprehensive analysis and optimization, it is possible to improve system reliability, optimize operation strategies, improve energy utilization efficiency, enhance system adaptability, reduce management complexity, improve user comfort and extend equipment life.

[0090] In one optional embodiment, the user tag information includes first user tag information and second user tag information, and generating user expected information based on the user tag information, the user control instruction, and the environmental parameter information includes:

[0091] Specifically, the edge computing center can input the first user label information and the second user label information into the conditional variational generative adversarial network to generate comprehensive user label information, which is used to represent the label information that can simultaneously meet the first user label information and the second user label information to the greatest extent.

[0092] For example, the Conditional Variational Generative Adversarial Network (CVAE-GAN) is a deep learning model that combines a variational autoencoder (VAE) and a generative adversarial network (GAN). Using this model, a comprehensive user label is generated based on multiple user labels, ensuring that the comprehensive user label information is as consistent as possible with all of them. By introducing conditional information, the controllability of the comprehensive user label information is enhanced.

[0093] Specifically, the edge computing center can generate user expectation information based on comprehensive user tag information, user control instructions and environmental parameter information.

[0094] In one of the optional embodiments, please refer to Figure 7 The refrigeration equipment information and the heating equipment information include global load index information and load distribution ratio information. Adjusting the refrigeration equipment information and / or heating equipment information of the central air conditioner based on the updated first central air conditioner operation strategy includes:

[0095] Step S706: Generate global load index information of the central air-conditioning refrigeration equipment information and / or heating equipment information based on the updated first central air-conditioning operation strategy.

[0096] Optionally, the global load index information may include but is not limited to total cooling capacity, total heating capacity, total air supply capacity, total dehumidification capacity and total energy consumption target.

[0097] Step S707 : performing multi-objective optimization using a genetic algorithm based on the global load index information to generate load distribution ratio information of the central air-conditioning refrigeration equipment information and / or heating equipment information.

[0098] Optionally, a genetic algorithm can be used for multi-objective optimization to include the actual energy consumption of air conditioning equipment. and performance coefficient and , and user expectations .

[0099] Step S708: Adjust the refrigeration equipment information and / or heating equipment information of the central air conditioner based on the load distribution ratio information.

[0100] In the above-mentioned method of high-efficiency and energy-saving cooling and heating of central air conditioning, through sampling genetic algorithm optimization, multiple objectives can be considered at the same time to maximize the overall benefits; by optimizing load distribution, unnecessary energy consumption can be reduced, the goal of energy conservation and emission reduction can be achieved, and operating costs can be reduced.

[0101] In an exemplary embodiment, Figure 3 As shown, a method for high-efficiency and energy-saving cooling and heating of a central air conditioner is provided, comprising the following steps S301 to S307. In which:

[0102] Step S301: Acquire user expectation information, air conditioning equipment information, and environmental parameter information.

[0103] Step S302 : determining the effectiveness of the first central air-conditioning operation strategy based on user expectation information, air conditioning equipment information, and environmental parameter information.

[0104] In step S303 , if the first central air-conditioning operation strategy is determined to be invalid, the user expectation information and the environmental parameter information are input into the edge strategy generation model to generate a second central air-conditioning operation strategy.

[0105] Step S304: input the user expectation information and the environmental parameter information into the central strategy generation model to generate a third central air-conditioning operation strategy.

[0106] Step S305: Update the second central air-conditioning operation strategy based on the third central air-conditioning operation strategy.

[0107] Step S306: updating the first central air-conditioning operation strategy based on the second central air-conditioning operation strategy, and adjusting the refrigeration equipment information and / or heating equipment information of the central air-conditioning based on the updated first central air-conditioning operation strategy.

[0108] Step S307: Optimize the edge strategy generation model based on the third central air-conditioning operation strategy.

[0109] In the above-mentioned method of efficient and energy-saving cooling and heating of central air conditioning, by establishing a multi-layer optimization mechanism, the operation strategy can be dynamically adjusted, the equipment utilization efficiency can be improved, and unnecessary energy waste can be reduced; by responding to user needs and environmental changes in real time, the adaptability and flexibility of the central air conditioning control strategy can be improved.

[0110] In an exemplary embodiment, Figure 7 As shown, a method for high-efficiency and energy-saving cooling and heating of central air conditioner is provided. Figure 1Taking the edge computing device 101 in FIG. 1 as an example, the following steps are included from step S701 to step S709. Among them:

[0111] Step S701: Acquire user expectation information, air conditioning equipment information, and environmental parameter information.

[0112] Step S702 : determining the effectiveness of the first central air-conditioning operation strategy based on user expectation information, air conditioning equipment information, and environmental parameter information.

[0113] In step S703 , if the first central air-conditioning operation strategy is determined to be invalid, the user expectation information and the environmental parameter information are input into the edge strategy generation model to generate a second central air-conditioning operation strategy.

[0114] Step S704: input the user expectation information and the environmental parameter information into the central strategy generation model to generate a third central air-conditioning operation strategy.

[0115] Step S705: Update the second central air-conditioning operation strategy based on the third central air-conditioning operation strategy.

[0116] Step S706: Generate global load index information of the central air-conditioning refrigeration equipment information and / or heating equipment information based on the updated first central air-conditioning operation strategy.

[0117] Step S707 : performing multi-objective optimization using a genetic algorithm based on the global load index information to generate load distribution ratio information of the central air-conditioning refrigeration equipment information and / or heating equipment information.

[0118] Step S708: Adjust the refrigeration equipment information and / or heating equipment information of the central air conditioner based on the load distribution ratio information.

[0119] Step S709: Optimize the edge strategy generation model based on the third central air-conditioning operation strategy.

[0120] The above-mentioned method for efficient and energy-saving cooling and heating in central air conditioning improves the operating efficiency, responsiveness, and stability of central air conditioning systems by acquiring comprehensive multimodal information, evaluating the effectiveness of existing strategies, and implementing multi-model collaboration, strategy updates, global load assessment, multi-objective optimization, and precise adjustment. This, in turn, enables continuous improvement and performance enhancement of central air conditioning systems.

[0121] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0122] Based on the same inventive concept, embodiments of the present application also provide a central air conditioning high-efficiency energy-saving cooling and heating system for implementing the aforementioned central air conditioning high-efficiency energy-saving cooling and heating method. The solution provided by this system is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the central air conditioning high-efficiency energy-saving cooling and heating system provided below can be found in the limitations of the central air conditioning high-efficiency energy-saving cooling and heating method described above and will not be repeated here.

[0123] In an exemplary embodiment, Figure 8 As shown, a central air-conditioning high-efficiency energy-saving cooling and heating system 800 is provided, comprising:

[0124] The control parameter acquisition module 801 can be used to acquire user expectation information, air conditioning equipment information and environmental parameter information. The air conditioning equipment information includes refrigeration equipment information and heating equipment information.

[0125] The control strategy determination module 802 can be used to determine the effectiveness of the first central air-conditioning operation strategy based on user expectation information, air-conditioning equipment information and environmental parameter information. The first central air-conditioning operation strategy is used to characterize the real-time operating status of the central air-conditioning air-conditioning equipment.

[0126] The control strategy generation module 803 can be used to input user expectation information and environmental parameter information into the edge strategy generation model if the first central air-conditioning operation strategy is judged to be invalid, and generate a second central air-conditioning operation strategy. The second central air-conditioning operation strategy is used to characterize the expected operating status of the central air-conditioning air conditioning equipment.

[0127] The control strategy updating module 804 may be configured to update the first central air conditioner operation strategy based on the second central air conditioner operation strategy, and adjust the central air conditioner's refrigeration equipment information and / or heating equipment information based on the updated first central air conditioner operation strategy.

[0128] In an optional embodiment, the control strategy generation module 803 can also be used to input user expectation information and environmental parameter information into the central policy generation model to generate a third central air-conditioning operation strategy, and the central policy generation model is installed in the Internet of Things central device; the control strategy update module 804 can also be used to update the second central air-conditioning operation strategy based on the third central air-conditioning operation strategy; optimize the edge strategy generation model based on the third central air-conditioning operation strategy, and the edge strategy generation model is installed in the Internet of Things edge device.

[0129] In an optional embodiment, the control strategy determination module 802 can also be used to obtain the time percentage information of the first central air-conditioning operation strategy being judged as invalid within a preset sampling period; the control strategy generation module 803 can also be used to predict the future change trend of the central air-conditioning operation demand based on the edge strategy prediction model if the time percentage information exceeds the preset normal threshold of the working environment, and generate a fourth central air-conditioning operation strategy and the first activation timing information of the fourth central air-conditioning operation strategy. The fourth central air-conditioning operation strategy is used to update the first central air-conditioning operation strategy at the time corresponding to the first activation timing information.

[0130] In an optional embodiment, the control strategy generation module 803 can also be used to input user expectation information and environmental parameter information into the central strategy prediction model to generate a fifth central air-conditioning operation strategy and the second activation timing information of the fifth central air-conditioning operation strategy; the control strategy update module 804 can also be used to update the fourth central air-conditioning operation strategy based on the fifth central air-conditioning operation strategy, and update the first activation timing information based on the second activation timing information; optimize the edge strategy generation model, the edge strategy prediction model and the central strategy generation model based on the fifth central air-conditioning operation strategy.

[0131] In an optional embodiment, the control parameter acquisition module 801 can also be used to obtain indoor environmental information, outdoor environmental information, user tag information and user control instructions, where the user control instructions include mode control instructions, target control instructions and wind speed control instructions; generate environmental parameter information based on the indoor environmental information and outdoor environmental information; generate user expected information based on the user tag information, user control instructions and environmental parameter information; and set the air conditioning equipment information to cooling equipment information and / or heating equipment information based on the environmental parameter information and user expected information.

[0132] In an optional embodiment, the control parameter acquisition module 801 can also be used to input the first user label information and the second user label information into the conditional variational generative adversarial network to generate comprehensive user label information, where the comprehensive user label information is used to represent label information that can simultaneously conform to the first user label information and the second user label information to the greatest extent; and generate user expected information based on the comprehensive user label information, user control instructions and environmental parameter information.

[0133] The control strategy update module 804 can also be used to generate global load index information of the central air-conditioning refrigeration equipment information and / or heating equipment information based on the updated first central air-conditioning operation strategy; use genetic algorithms to perform multi-objective optimization based on the global load index information to generate load distribution ratio information of the central air-conditioning refrigeration equipment information and / or heating equipment information; and adjust the central air-conditioning refrigeration equipment information and / or heating equipment information based on the load distribution ratio information.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for efficient and energy-saving cooling and heating of a central air conditioner as described above are implemented.

[0135] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0136] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0137] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A method for central air conditioning cooling and heating, characterized in that: The method comprises: Acquiring user expectation information, air conditioning equipment information, and environmental parameter information, wherein the air conditioning equipment information includes refrigeration equipment information and heating equipment information; Determining the effectiveness of a first central air-conditioning operation strategy based on the user expectation information, the air-conditioning equipment information, and the environmental parameter information, wherein the first central air-conditioning operation strategy is used to represent a real-time operating status of the air-conditioning equipment of the central air-conditioning; If the first central air-conditioning operation strategy is determined to be invalid, the user expectation information and the environmental parameter information are input into the edge strategy generation model to generate a second central air-conditioning operation strategy, wherein the second central air-conditioning operation strategy is used to represent the expected operating state of the central air-conditioning air conditioning equipment; updating the first central air-conditioning operation strategy based on the second central air-conditioning operation strategy, and adjusting the refrigeration equipment information and / or the heating equipment information of the central air-conditioning based on the updated first central air-conditioning operation strategy; Obtaining information on the proportion of time during which the first central air-conditioning operation strategy is judged to be ineffective within a preset sampling period; If the duration ratio information exceeds a preset normal working environment threshold, a future change trend of the central air-conditioning operation demand is predicted based on the edge strategy prediction model, and a fourth central air-conditioning operation strategy and first activation timing information of the fourth central air-conditioning operation strategy are generated, and the fourth central air-conditioning operation strategy is used to update the first central air-conditioning operation strategy at a time corresponding to the first activation timing information; The expression of the duration ratio information is: ; Where, is the duration ratio information, is the sampling period, is the number of times the first central air-conditioning operation strategy is judged to be invalid within the sampling period, For the The sampling history coefficient attenuation term when the first central air-conditioning operation strategy is judged to be ineffective, For the The sampling history time when the first central air-conditioning operation strategy is judged to be invalid, For the The strategy adjustment fuzzy coefficient item when the first central air-conditioning operation strategy is judged to be ineffective is: For the The adjustment parameters when the first central air-conditioning operation strategy is judged to be invalid, For the The adjustment parameters of the second central air-conditioning operation strategy corresponding to the first central air-conditioning operation strategy are determined to be invalid. For the The duration when the first central air-conditioning operation strategy is judged to be invalid.

2. The method according to claim 1, characterized in that The step of inputting the user expectation information and the environmental parameter information into the edge strategy generation model to generate a second central air-conditioning operation strategy further includes: Inputting the user expectation information and the environmental parameter information into a central policy generation model to generate a third central air-conditioning operation strategy, wherein the central policy generation model is installed in an Internet of Things central device; Updating the second central air conditioning operation strategy based on the third central air conditioning operation strategy; The edge strategy generation model is optimized based on the third central air-conditioning operation strategy, and the edge strategy generation model is installed in the Internet of Things edge device.

3. The method according to claim 2, wherein: The central strategy generation model is a deep learning neural network model, and the central strategy generation model includes an improved convolution module and an improved converter module; The edge strategy generation model is an improved BP neural network, and the improved BP neural network is obtained by distilling the central strategy generation model into the initial BP neural network; Among them, the loss function of the central strategy generation model is a multi-objective comprehensive loss function, and the expression of the multi-objective comprehensive loss function is: ； Where, is the multi-objective comprehensive loss function, 、 、 and They are energy efficiency loss, regulation target loss, strategy stability loss and strategy classification loss, 、 、 and They are energy efficiency loss parameter, adjustment target loss parameter, strategy stability loss parameter and strategy classification loss parameter, respectively. is the total number of the air conditioning equipment, 、 and Respectively The actual energy consumption, set energy consumption and performance coefficient of each air conditioning equipment, is the number of environmental data samples, is the total number of physical properties of the air regulated by the central air conditioner, and They are respectively the first The weight parameters and allowable error parameters of the physical properties of air, and Respectively The second sampling The measured and set values ​​of the physical properties of the air, is the total number of adjustment parameters of the third central air-conditioning operation strategy and the first central air-conditioning operation strategy, and are the first and second phases of the third central air-conditioning operation strategy. The adjustment parameters and The output corresponding to the adjustment parameter is and They are respectively the first central air-conditioning operation strategy The adjustment parameters and The output corresponding to the adjustment parameter is and Respectively The maximum and minimum output values ​​corresponding to the adjustment parameters, is the adjustment parameter coefficient, To adjust the parameter output coefficient, is the number of samples, is the number of categories of the third central air-conditioning operation strategy, is the balance factor, is the focusing parameter, Generate a model for the central strategy The samples belong to The predicted probability of the class.

4. The method according to claim 1, wherein If the duration ratio information exceeds a preset normal environment threshold, predicting the future central air-conditioning operation demand change trend based on the edge strategy prediction model, generating a fourth central air-conditioning operation strategy and activation timing information of the fourth central air-conditioning operation strategy, and then further including: Inputting the user expectation information and the environmental parameter information into a central strategy prediction model to generate a fifth central air-conditioning operation strategy and second activation timing information of the fifth central air-conditioning operation strategy; Updating the fourth central air-conditioning operation strategy based on the fifth central air-conditioning operation strategy, and updating the first activation timing information based on the second activation timing information; The edge strategy generation model, the edge strategy prediction model and the central strategy generation model are optimized based on the fifth central air-conditioning operation strategy.

5. The method according to claim 4, characterized in that: The edge strategy prediction model is a regression prediction model; The center policy prediction model is constructed based on an improved converter model and an improved long short-term memory network, and the improved converter model includes a residual convolution module.

6. The method according to claim 1, characterized in that The obtaining of user expectation information, air conditioning equipment information and environmental parameter information includes: Acquiring indoor environment information, outdoor environment information, user tag information, and user control instructions, wherein the user control instructions include mode control instructions, target control instructions, and wind speed control instructions; generating the environmental parameter information based on the indoor environmental information and the outdoor environmental information; generating the user expected information based on the user tag information, the user control instruction, and the environmental parameter information; The air conditioning equipment information is set as the refrigeration equipment information and / or heating equipment information based on the environmental parameter information and the user expectation information.

7. The method according to claim 6, characterized in that The user tag information includes first user tag information and second user tag information, and generating the user expected information based on the user tag information, the user control instruction, and the environmental parameter information includes: Inputting the first user label information and the second user label information into a conditional variational generative adversarial network to generate comprehensive user label information, where the comprehensive user label information is used to represent label information that can simultaneously conform to the first user label information and the second user label information to the greatest extent possible; The user expectation information is generated based on the comprehensive user tag information, the user control instruction and the environmental parameter information.

8. The method according to any one of claims 1 to 7, characterized in that The refrigeration equipment information and the heating equipment information include global load index information and load distribution ratio information. The adjusting of the refrigeration equipment information and / or the heating equipment information of the central air conditioner based on the updated first central air conditioner operation strategy includes: generating global load index information of the refrigeration equipment information and / or the heating equipment information of the central air conditioner based on the updated first central air conditioner operation strategy; Based on the global load index information, a genetic algorithm is used to perform multi-objective optimization to generate the load distribution ratio information of the refrigeration equipment information and / or the heating equipment information of the central air conditioner; The refrigeration equipment information and / or the heating equipment information of the central air conditioner are adjusted based on the load distribution ratio information.

9. The method according to claim 8, characterized in that: The refrigeration equipment information includes cold source equipment information and cold recovery equipment information, and the heating equipment information includes heat source equipment information and heat recovery equipment information.

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