13722 results about "Air conditioning" patented technology

The invention belongs to the technical field of rail transit intelligent monitoring, and particularly relates to an intelligent monitoring method and device for a rail transit air conditioning system. According to the method, the sensor array with the self-adaptive sampling frequency is used for collecting the multi-modal operation data, then the collected multi-modal operation data is preprocessed, the feature degradation track atlas is established, powerful data support is provided for subsequent fault early warning and diagnosis, and the fault diagnosis accuracy is improved. Historical abnormal events are introduced to dynamically correct the health state evaluation base line, the timeliness and accuracy of the evaluation base line are ensured, in comparative analysis of real-time operation data and the dynamic evaluation base line, a health degree scoring and dynamic threshold mechanism is adopted, quantitative evaluation of the health state of the air conditioner system is achieved, and the evaluation accuracy of the health state of the air conditioner system is improved. According to the method, a decision graph containing fault diagnosis and predictive maintenance suggestions is generated by analyzing the propagation path and time sequence relevance of abnormal parameters in the air conditioning system and combining an equipment topological relation graph, so that the troubleshooting and repairing efficiency is improved.

Air conditioning device (1) for motorhomes, comprising: - at least one first heat exchanger for exchanging heat with the air circulating through it; - a cooling circuit (8) for a cooling fluid, with a path passing through the first heat exchanger; - a compressor (7) which includes an electric motor, for circulating the cooling fluid in the cooling circuit (8); - a power supply unit that can be connected to an external power supply to receive an electrical input current or electrical input power and to generate a variable supply voltage to drive the electric motor with an electrical supply current, - a controller (11) connected to the power supply unit that is suitable for receiving input data representative of a reference value of the electrical input current or electrical input power and that is programmed to adjust the power supply unit to generate the supply voltage with a reference amplitude and / or frequency such that the electrical input current or electrical input power is below the reference value; - a positioning device designed to determine the position of the air conditioning device (1); and - a memory containing a plurality of reference values ​​corresponding to a plurality of locations, wherein the controller (11) is capable of receiving a location signal from the positioning device and of setting as input data one of the reference values ​​present in the memory depending on the location signal.

The shutdown of a compressor (10) installed in a refrigerant circuit (2) in air conditioning or refrigeration system is controlled so as to prevent unpowered reverse rotation of the compressor. Prior to terminating electric power to the compressor drive motor (24), the pressure within the system is relieved and substantially equalized across the compressor, thereby eliminating the possibility of unpowered reverse rotation of the compressor at shutdown. Pressure relief and equalization may be achieved by reducing the operating speed of the compressor to a low forward speed for a period of time prior to deenergizing the compressor drive motor. Pressure equalization may also be achieved by driving the compressor in reverse rotation prior to deenergizing the drive motor.

The invention discloses an air conditioning system and a control method thereof. The air conditioning system comprises an air conditioning outdoor unit; the air conditioning outdoor unit comprises a compressor, an outdoor heat exchanger and an outdoor fan; the outdoor fan is at least provided with a first working mode and a second working mode; and when the outdoor fan runs in the first working mode, a wind gear is higher than that when the outdoor fan runs in the second working mode. The control method comprises the following steps that S1, the air conditioning system is controlled to enter arefrigeration mode; S2, according to the outdoor environment temperature and / or preset hours, the outdoor fan is controlled to enter the first working mode or the second working mode. According to the control method of the air conditioning system, the air conditioning system can control the outdoor fan to enter the first working mode or the second working mode according to the outdoor environmenttemperature and / or the preset hours, and when the outdoor fan runs in the second working mode, the wind gear of the outdoor fan is lowered, so that the use reliability the air conditioning system cannot be affected, and the requirement of a user on comfortableness can be met.

The invention provides a customized energy-saving air conditioner control method and device for industrial process requirements, and is applied to the technical field of data processing. According to the method, electromagnetic interference and vibration noise are eliminated through Kalman filtering, and a standardized process-environment-energy consumption correlation sequence is generated; and converting the three-dimensional process load map into a three-dimensional process load map, and establishing an individualized air conditioner dynamic load prediction model in combination with heat and humidity characteristics of a scene through fluid dynamic simulation and self-adaptive grid division. Extracting process priority factors to construct an adjustment matrix, calculating an air conditioner parameter combination under target energy consumption, extracting energy consumption characteristics, dividing standard exceeding risk levels, and constructing a multi-dimensional characteristic matrix; and comparing real-time data with historical data to identify abnormity, and generating an energy consumption optimization correction factor. Users are grouped according to enterprise conditions, key factors are screened by using a gradient boosting tree, a personalized energy-saving control model is constructed by fusing multiple information, target parameters are output, and a real-time adjustment instruction and a time-phased energy-saving strategy are generated in combination with a time sequence.

The invention relates to the technical field of air conditioning, in particular to a laboratory heating and ventilation load prediction and self-adaptive regulation and control method. According to the method, the infrared frame and the power sampling time mark are synchronized, the sensing flow is aligned and packaged, and the thermal diffusion evolution rate is generated. Lagging characteristics are determined in combination with power jump and temperature rise moments, and a heterogeneous dynamic coupling model is established. Extracting a physical evolution parameter as a mechanism operator, injecting the mechanism operator into a hidden layer of the prediction model, reconstructing a phase space, and calculating an air load increment in a lag window. And reverse mapping is executed based on the heat exchange characteristics to generate a feedforward instruction, and when the rate exceeds a threshold value, the weight is issued and dynamically corrected, so that closed-loop correction is completed. According to the method, deep coupling of feedforward prediction compensation and feedback residual adjustment is executed, and accurate regulation and control of the air volume and cooling and heating loads of the laboratory are achieved.

The embodiment of the invention provides an energy-saving optimization system for heating, ventilating and air conditioning of an indoor ski field. The energy-saving optimization system comprises a multi-source sensing layer, an edge computing layer, a cloud decision-making layer and an equipment execution layer. The multi-source sensing layer is used for collecting multi-source data such as weather, passenger flow, temperature and humidity and equipment state; the edge calculation layer carries out fusion processing on the data and generates a load prediction result through a load prediction mechanism; the cloud decision-making layer generates an optimization control instruction based on a multi-agent deep reinforcement learning and model prediction control optimization strategy; and the equipment execution layer receives and executes the instruction and feeds back the equipment state. Through a multi-layer collaborative optimization architecture, accurate load prediction and equipment intelligent collaborative control are realized, five-stage stepped optimization and a dynamic priority mechanism are adopted, the energy efficiency of the system is remarkably improved, the energy consumption is reduced while the environmental comfort is ensured, and the economical efficiency and the stability of system operation are effectively improved.

The invention relates to an energy management method, device and equipment based on an air conditioning system and a medium. The method comprises the steps that in a preset period, a target cold storage capacity calculation model matched with the current working condition is selected from a plurality of candidate cold storage capacity calculation models according to the pre-obtained photovoltaic power generation power, the air conditioner system load and the change trend of the environment temperature; the environment temperature and the electric energy are input into the target cold storage capacity calculation model, and the cold storage capacity, output by the target cold storage capacity calculation model, of a phase change cold storage material in the air conditioner system is obtained; based on the cold storage capacity, the photovoltaic power generation power and pre-obtained planned electricity consumption probability distribution, analysis processing is conducted, and an energy management strategy of the air conditioning system is obtained; the energy management strategy is used for guiding the air conditioning system to adopt commercial power supply or photovoltaic power supply, whether to utilize the phase change cold storage material for cold storage or not and whether to start a compressor to assist in executing the cold storage process or not. By adopting the method, the power consumption cost can be reduced.

The invention relates to a food fresh-keeping method, device and equipment and a storage medium, and the method comprises the steps: collecting respiration intensity data and decay gas concentration of target food in a cold chain carriage, carrying out fresh-keeping threshold matching, and generating food fresh-keeping information; real-time environment parameters of the cold chain compartment are collected, a fresh-keeping instruction is generated according to the food fresh-keeping information and the real-time environment parameters, and an air adjusting valve opening instruction and a compressor frequency instruction are obtained; dynamic regulation and control operation is executed based on the air regulating valve opening instruction and the compressor frequency instruction, and putrefying bacterium comprehensive evaluation indexes and compressor power consumption data are obtained in real time; and according to the putrefying bacterium comprehensive evaluation index and the compressor power consumption data, instruction feedback analysis is carried out, strategy construction is carried out, and a fresh-keeping regulation and control strategy is output. The air conditioning and refrigeration strategy can be dynamically adjusted according to the breathing characteristics of different foods and the growth dynamic state of putrefying bacteria.

The invention discloses an airport ground air conditioning unit multi-system energy-saving control method and system, and the method comprises the steps: collecting refrigerant flow data, temperature monitoring data and load demand data of an airport air conditioning system, recognizing a phase change boundary, extracting a gas-liquid conversion point, and carrying out enthalpy value coupling in combination with superheat degree distribution to generate a phase change energy field; identifying heat transfer bottleneck points based on the phase change energy field, generating an equivalent thermal resistance network, and extracting heat capacity characteristic parameters to construct a thermal response path diagram; refrigerant shunting analysis is carried out to extract primary and secondary loops, and an optimal distribution point is identified to generate a load balance matrix; reconstructing a compressor adjusting curve, and identifying a high-efficiency operation interval to generate a frequency conversion adjusting sequence; determining an energy storage window period, and obtaining a dynamic buffer threshold to construct a peak clipping and valley filling instruction set; and an execution deviation track is monitored, the optimal convergence path is recognized, self-adaptive correction is conducted, an energy-saving control instruction is generated, and intelligent multi-system coordination efficient energy-saving control over the airport ground air conditioning unit is achieved.

The invention relates to the field of energy allocation, and discloses a regional building group multi-energy complementary cooperative regulation and control method, which comprises the following steps of: acquisition processing: acquiring multi-energy data in a regional building group by utilizing multiple sensors, and preprocessing to obtain processed data; load prediction: inputting the processed data into an LSTM-Attention mixed deep learning model, performing fusion calculation in combination with a grey prediction model, and outputting a cold and heat power ultra-short-term load prediction result; and cooperative regulation and control: based on the load prediction result and the real-time operation parameters of the building air conditioning system, the electric vehicle and the multi-element energy storage system of the building group. Multi-energy data in a regional building group are collected and preprocessed through multiple sensors, an LSTM-Attention mixed deep learning model and a grey prediction model are fused to calculate a load prediction result, an interactive regulation and control model is constructed based on load prediction and real-time equipment parameters, and a collaborative regulation and control strategy is formulated and executed.

The invention discloses a multi-modal data fusion air conditioner optimization control method and system, and belongs to the technical field of intelligent building equipment control. According to the method, temperature, humidity, energy consumption, user behaviors and meteorological data are collected through a multi-source sensor, the data are subjected to dynamic window standardization processing and then input into a gated convolution LSTM network to predict the system state, an optimization strategy is generated in combination with an online reinforcement learning algorithm, multi-modal instructions are dynamically weighted and fused, and execution parameters are adjusted in real time through a feedback correction mechanism. The system comprises a multi-source sensing array, an edge computing unit, a strategy optimization engine and an intelligent execution controller. According to the method, through collaborative optimization of multi-modal spatial-temporal feature fusion and deep reinforcement learning, the problem of unbalance of energy efficiency and comfort is solved, the energy efficiency level and the user comfort of the air conditioning system are remarkably improved, and the method has the characteristics of real-time response and high stability, is suitable for intelligent air conditioning control of modern buildings and has wide application prospects.

The invention relates to the technical field of building environment intelligent control, provides an air conditioner distributed regulation and control system based on a neural network and a method thereof, and is suitable for energy efficiency optimization and comfort guarantee of multi-area buildings. The system comprises a coordination control module, an optimization decomposition module, a region control module, a model evolution module and a strategy self-learning module, and region-level scheduling and parallel solving of a control task are realized by constructing a multi-objective optimization model and performing decomposition based on an alternating direction multiplier method; constructing a control model by using a hypergraph neural network, and supporting online updating, migration deployment and adaptive adjustment; strategy self-learning is realized through a strategy evaluation and aggregation mechanism driven by control errors, and a control closed loop is formed. The system improves the precision, flexibility and energy efficiency performance of air conditioner regulation and control, and is suitable for building scenes such as intelligent buildings and green parks.

To provide an outdoor unit for an air conditioning system for reducing workload in arranging the outdoor unit even when the size or the weight of the outdoor unit for the air conditioning system is increased, while enabling good arrangement also in a space having a small installation area or in a complicated or constrained space.SOLUTION: The outdoor unit for the air conditioning system includes: a first unit having a first casing storing part for housing part of components including a heat exchanger and an air fan out of a structural component group including a compressor for compressing refrigerant, the heat exchanger for making heat exchange of the refrigerant with outside air, and the air fan for blowing outside air to the heat exchanger; and a second unit having a second casing for housing the remaining component including the compressor of the structural component group. The first casing and the second casing are constructed to be freely joined to and separated from each other. The outdoor unit is driven both in a joined state in which the first casing and the second casing are joined to each other and in a separated state in which the first casing and the second casing are separated from each other.SELECTED DRAWING: Figure 1

The invention provides a water flow dynamic distribution control method driven by air conditioner terminal load prediction, and relates to the technical field of air conditioner engineering.The method comprises the steps that 1, the people flow density, the environment temperature and humidity and the equipment running state of each air conditioner terminal service area are collected in real time, and a load prediction mechanism is constructed based on a spatial-temporal feature fusion algorithm; the minimum cooling capacity demand of each tail end in the future time period is predicted; 2, based on the minimum cooling capacity requirement, areas with similar load characteristics are classified into the same cluster through hot area clustering, and building scale dynamic cooling load distribution is generated with cluster mass center data as the benchmark; and step 3, calculating an energy flexibility penalty coefficient based on the dynamic cooling load distribution in combination with the real-time deviation between the indoor temperature and the comfortable temperature difference, and generating a regional water flow regulation weight. According to the method, dynamic distribution of the water flow is driven through load prediction, the water pump frequency and the refrigerating unit strategy are dynamically adjusted in combination with real-time energy consumption feedback, and self-adaptive adjustment of the branch water flow is achieved.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention discloses a classroom environment intelligent supervision method and system based on data analysis, and the method comprises the steps: obtaining multi-source heterogeneous data in a classroom environment, eliminating the sensor noise of the multi-source heterogeneous data through employing an improved Kalman filtering algorithm, carrying out the time-space alignment of the data after noise elimination, and obtaining the aligned multi-source heterogeneous data; establishing a CRNN three-layer convolutional recurrent neural network model, and inputting the aligned multi-source heterogeneous data into the CRNN three-layer convolutional recurrent neural network model for recognition to obtain a classroom teaching environment state; and carrying out classroom risk early warning and self-adaptive classroom environment regulation and control according to the classroom teaching environment state. The classroom emergency risk can be actively prevented, automatic adjustment can be achieved through an intelligent air conditioner and an illumination system, a safe and comfortable teaching environment is created, and classroom efficiency improvement and teacher and student health guarantee are facilitated.

There is provided a conveyance assembly that can be easily provided in a conveyance cabin, and that can perform efficient air conditioning for an occupant. A conveyance assembly is a conveyance assembly including an interior member provided in a conveyance. The interior member is composed of at least a conveyance seat, the interior member includes an air conditioning device, and the air conditioning device includes a housing, a blowing device accommodated in the housing, and a heating device accommodated in the housing and disposed in a blowing direction of air blown by the blowing device. Since the air conditioning device is unitized, the air conditioning device can be easily provided in the interior member, and can perform efficient air conditioning for an occupant.

The invention relates to the technical field of water level monitoring, in particular to a visual monitoring system for the condensate water level of a micro-hyperbaric oxygen chamber, which adopts the technical scheme that an internal image of a water receiving box is acquired at a preset frequency, and water mist and reflective interference are eliminated by adopting a dynamic illumination compensation and frequency domain filtering technology; a U-Net deep learning model is integrated, a water level line is segmented through multi-scale feature fusion, foam and impurity artifacts are recognized in combination with time sequence analysis, a water level value is dynamically corrected, graded alarm is triggered according to a water level threshold value, an air conditioner drainage system is linked, traditional image processing and the deep learning model are combined, and detection robustness under complex working conditions is improved. Real water levels and interferents are effectively distinguished through time sequence analysis and physical characteristic modeling, the real-time requirement under the high-pressure environment is met through the model compression and hardware acceleration technology, and absolute reliability of equipment operation is ensured through linkage of a grading early warning mechanism and a drainage system.

The invention discloses a building heating, ventilating and air conditioning control method with dynamic energy efficiency optimization, and belongs to the technical field of heating, ventilating and air conditioning control. The method comprises the following steps: step 1, acquiring temperature, humidity, carbon dioxide concentration and personnel distribution data in real time, and performing preliminary data processing through an edge computing node; step 2, realizing accurate prediction of future load change of the building; step 3, generating an optimal control strategy by using a reinforcement learning algorithm, and realizing dynamic optimization control of equipment operation; 4, the compliance of equipment operation, network communication and control instructions is monitored in real time, and a closed-loop dynamic tuning mechanism is constructed to continuously optimize control strategy parameters; step 5, dividing a control task into a group control layer, a region layer and a terminal layer, and establishing communication among the layers through a standardized interface so as to realize accurate transmission and execution of an instruction; and step 6, designing a friendly human-computer interaction interface, displaying a building operation state, energy consumption analysis and a control strategy, and supporting multi-terminal access and remote monitoring.

The invention relates to the technical field of temperature control, in particular to a heating and ventilation heating power constant-temperature variable-flow control method, and provides the following scheme that circulating pump frequency instructions and water temperature response data of multiple historical regulation and control periods are collected, and a dynamic track of the current water temperature in a temperature inertia field is constructed; calculating an attraction potential energy state between the current water temperature and a target constant temperature value in combination with the disturbance residual matrix; and outputting the frequency bandwidth interval of the circulating pump based on the state to realize dynamic adjustment. An adjacency matrix and a Laplacian matrix are constructed by introducing a hydraulic communication relation, spatial convolution and weighted updating are performed on disturbance residues in combination with a topological diffusion core, and accurate propagation and suppression of disturbance are realized. The method can effectively solve the problems of nonlinear response, delay effect and disturbance coupling of the water temperature to the pump frequency, and the temperature stability and the energy utilization efficiency of a heating system are improved.

The invention relates to the technical field of lime kiln control, and discloses a lime kiln partition intelligent temperature control system and a storage medium, and the system comprises a data collection unit which is used for collecting state data of each region of a lime kiln; the working condition recognition unit recognizes working conditions based on the state data; the temperature control unit is provided with a PID (Proportion Integration Differentiation) control strategy and a prediction control strategy, and the opening degrees of the fuel regulating valves and the air regulating valves in different areas in the lime kiln are calculated according to the corresponding control strategies selected on the basis of working conditions; the discharging control unit is provided with a balance control strategy, the balance control strategy obtains the current discharging speed through the current productivity of the lime kiln and the material level height in the lime kiln, and the execution unit correspondingly controls the lime kiln according to the control quantity. The method effectively solves the problems of difficulty in multi-area coordination, poor working condition adaptability, high energy consumption and the like in a traditional control mode, and has the remarkable advantages of improving the temperature control precision, ensuring the production stability and reducing the energy consumption.

The invention relates to the technical field of green building energy consumption, and discloses a green building energy consumption real-time monitoring and intelligent adjusting system based on the Internet of Things, and the system comprises an Internet of Things multi-source sensing module which integrates a building internal and external environment sensing unit and an equipment operation state collection unit, and generates a comprehensive energy consumption characteristic signal through a heterogeneous data fusion gateway; the dual-channel decision module receives the signal, the load prediction unit outputs a building cooling and heating load prediction signal and a regional energy efficiency imbalance risk signal, the energy efficiency optimization unit generates an optimal equipment adjustment strategy signal, and the dynamic adjustment unit outputs a real-time energy consumption safety threshold; the intelligent execution control module and the self-adaptive equipment regulation and control unit adjust heating and ventilation equipment parameters, the multi-equipment collaborative management unit optimizes the running state of the lighting and air conditioning equipment group and collects feedback signals, and the man-machine interaction early warning platform generates a comprehensive energy efficiency risk index. According to the system, real-time monitoring and intelligent adjustment of energy consumption of the green building are realized, and refinement and collaboration of energy consumption management are improved.

The invention discloses a data center machine room energy-saving optimization method and system based on thermal environment prediction. The method comprises two stages of offline modeling and online prediction optimization. In the off-line stage, a CFD simulation model is constructed based on a machine room physical structure, equipment layout and thermal load parameters, and high-precision temperature field data is generated; using the thermal environment prediction model to input equipment parameters and load change to output future space temperature distribution; meanwhile, an XGBoost hybrid energy consumption prediction model is constructed based on the wind speed ratio or the fan frequency, the number of running fans and related characteristics. In the online stage, the lowest energy consumption and the minimum temperature deviation serve as targets, and a Pareto optimal solution set is generated through an MOEA / D algorithm; the optimal wind speed ratio / frequency and equipment number combination is selected as required to control operation of the air conditioner; and dynamically updating model parameters by sliding a time window to realize long-term robust control of the system. According to the method, intelligent energy-saving control of the machine room is realized by fusing CFD simulation, time sequence prediction, energy consumption modeling and a multi-objective optimization algorithm.

The invention is suitable for the technical field of copper pipe cutting, and provides a copper pipe cutting device and method for refrigeration air conditioner machining, the copper pipe cutting device for refrigeration air conditioner machining comprises a workbench, a limiting seat is arranged on the workbench, and clamping mechanisms used for fixing a copper pipe are arranged on the two sides of the limiting seat; the workbench is fixedly connected with a supporting frame, the supporting frame is fixedly connected with an electric telescopic rod, the telescopic end of the electric telescopic rod is fixedly connected with a fixing frame, the fixing frame is connected with a limiting mechanism, and the limiting mechanism can vertically limit a copper pipe; buffering assemblies are fixedly connected to the two ends of the electric telescopic rod correspondingly, and a cutting assembly is horizontally limited between the two buffering assemblies. According to the method, the regulation and control analysis model fuses vibratology, thermodynamics and the material processing principle, real-time quantitative prediction of'cutting precision 'which cannot be directly observed is achieved through measurable parameters, and a theoretical core is provided for intelligent manufacturing.

The invention discloses a cold storage temperature optimization control system and method for a chilled water storage air conditioning system, and relates to the technical field of chilled water storage air conditioning management.The method comprises the steps that 1, a plurality of operation parameters in the system operation process are collected; performing time synchronization and abnormal value elimination on the operation parameters to form a dynamic operation data set; and 2, judging the current hydraulic circulation state based on the dynamic operation data set, and outputting a circulation deviation signal when any index of the total flow of the system, the flow distribution of each branch or the temperature difference change rate of the supplied water and the returned water exceeds a set corresponding threshold value. By constructing the dynamic operation data set and monitoring key parameters such as the system temperature, the flow and the water pump operation state in real time, a unified multi-dimensional data basis is established, support is provided for follow-up operation state recognition and control strategy execution, and the real-time performance and accuracy of control are remarkably improved.

The invention discloses a railway station energy consumption intelligent management method and system based on artificial intelligence. The method comprises the steps that sensors are deployed in subsystems of a railway station, and environment parameters, equipment operation parameters and historical energy consumption data are collected; a unified communication protocol and a standard interface are adopted, and any one or more subsystems of air conditioner ventilation, cold and heat sources, illumination and elevators and escalators are interconnected; according to the real-time environment change and the load demand, carrying out self-adaptive control on the plurality of pieces of energy consumption equipment; based on a mechanism model and an artificial intelligence prediction model, energy consumption simulation and comparison are carried out on different control strategies, and the global maximum is automatically searched through an intelligent algorithm on the premise of meeting passenger comfort and safe operation. According to the technical scheme, through deep combination of mechanism modeling and artificial intelligence, high-precision prediction and adaptive regulation and control can be realized in railway station multi-system energy consumption management, and the energy utilization efficiency is comprehensively improved.

The invention discloses a low-carbon transformation method, system and device for a building air conditioning system. The objective of the invention is to solve the problems of static model, single target, one-sided evaluation and non-standardized process in the existing reconstruction technology. According to the method, building multi-dimensional data are collected; constructing a building energy consumption prediction physical simulation model coupled with a user behavior prediction model based on machine learning to dynamically reflect a real operation condition; a multi-dimensional objective function is defined, and a Pareto optimal transformation scheme set is generated; constructing an evaluation model based on an analytic hierarchy process and a fuzzy comprehensive evaluation method, and calculating a comprehensive evaluation value of each scheme; and selecting an optimal scheme according to the comprehensive evaluation value, and generating a detailed implementation report. The method can scientifically and efficiently determine the global optimal transformation strategy considering energy conservation, carbon reduction, comfort and economy, and significantly improves the scientificity and comprehensive benefits of building transformation decision.

The embodiment of the invention provides an air conditioner control method and device and a storage medium, the method is used for conducting cooperative control on multiple air conditioner systems deployed in different buildings of a target park, and the method comprises the steps that people flow trend prediction data of the target park in the future time period is obtained, the people flow trend prediction data represents migration and / or distribution conditions of people among different buildings; based on the people flow trend prediction data, target loads of the multiple air conditioning systems are determined; based on the respective target loads of the multiple air conditioning systems, the multiple air conditioning systems serve as a whole, combined planning is conducted on the operation parameters of the multiple air conditioning systems, and a target control strategy used for conducting cooperative control on the multiple air conditioning systems is obtained; and on the basis of the target control strategy, control operation is executed on the multiple air conditioning systems.

The invention discloses an indoor environment automatic adjustment method based on self-supervised learning, and the method comprises the steps: collecting the multi-modal data of an indoor environment and a user behavior, completing the synchronization and standardization processing, and constructing a causal graph model which comprises an environment variable, a user behavior variable and an adjustment response variable; adjusting causal representation vectors are generated through reverse causal modeling, building structure parameters, user preferences and climate regionalization information are fused, and a unified cross-modal embedding space is constructed; and further training an adjustment strategy migration model based on a federal migration learning mechanism, and generating a personalized control instruction in combination with local terminal data. The method has the advantages of being high in causal reasoning ability, accurate in cross-modal fusion expression, high in control strategy adaptability and the like, and is suitable for application scenes such as intelligent buildings, energy-saving air conditioning systems and environment sensing type home furnishing.