19617 results about "Air conditioning" patented technology

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

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.

The invention provides an air conditioner energy-saving control method and system based on a virtual power plant, and the method comprises the steps: determining an air conditioner energy-saving control model corresponding to a plurality of air conditioner devices through obtaining an operation parameter set and real-time power grid load data of the plurality of air conditioner devices in a target region, and carrying out the energy-saving control of the plurality of air conditioner devices according to the real-time power grid load data and load prediction data; and determining a power grid load fluctuation threshold value of the target area, dynamically optimizing the operation parameter set of the plurality of air conditioning equipment through a parameter optimization model, generating an optimized equipment operation parameter subset, and adjusting temperature set values and operation modes of the plurality of air conditioning equipment in a future time period. And generating an air conditioner group control strategy corresponding to a future time period, obtaining operation feedback data, updating weight parameters of the load prediction model according to a deviation value between actual power grid load data and load prediction data, calibrating an optimization rule of the parameter optimization model, and generating a calibrated model. According to the invention, the fine regulation and control capability and the energy coordination efficiency of the air conditioner group can be improved.

The invention provides an air conditioner dispatching method and device based on virtual power plant response. The method comprises the steps that a real-time operation parameter set of all air conditioner terminals in a target area in a preset time window is obtained; performing feature alignment processing on the real-time operation parameter set and a dynamic regulation and control instruction issued by a virtual power plant to generate a joint input feature; the combined input features are input into a pre-trained multi-target optimization model, hierarchical weight distribution is conducted on the combined input features through a feature fusion layer in the multi-target optimization model, an air conditioner regulation and control priority sequence is generated, and a target scheduling strategy is generated based on the priority sequence; and the target scheduling strategy is issued to each air conditioner terminal in the target area to be executed, a feedback parameter set after execution is collected in real time, and incremental parameter updating is conducted on the multi-target optimization model through the feedback parameter set. According to the invention, the accuracy, real-time performance and sustainability of air conditioner terminal scheduling can be improved.

The invention relates to the technical field of railways, and discloses a railway passenger train operation state monitoring system based on artificial intelligence, which comprises a multi-modal sensor array, an edge AI judgment platform and a cloud intelligent decision center. According to the railway passenger train operation state monitoring method and system based on artificial intelligence, 28 parameters of a contact network, a running gear and the like are collected in real time through a multi-modal sensor array, an edge AI judgment platform utilizes a cross-modal fusion algorithm to deeply mine multi-source data potential association, the fault diagnosis accuracy is improved, and the fault diagnosis efficiency is improved. The cloud intelligent decision center dynamically aggregates gradient parameters of the edge end model based on federated learning, filters abnormal nodes and updates the abnormal nodes in an hour-level period, and constructs a digital twinborn model in combination with a Spark framework to realize dynamic parameter optimization; the fire-fighting early warning fusion model is linked with the air conditioner pressure and the compartment sealing state correction threshold value through a three-stage mechanism of parameter initial judgment, visual verification and environment verification, it is ensured that the early warning response time is shorter than 200 ms, and cooperation with the overall state of the train is achieved.

The invention provides a building energy-saving control system based on centralized control of the Internet of Things, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting personnel activity data, environment parameter data and equipment state data in a building in real time; the data preprocessing module is used for carrying out cleaning, denoising and time sequence alignment processing; the area use mode recognition module is used for analyzing the use conditions of equipment in different areas in the building according to a multi-layer classification rule; the energy-saving control strategy generation module is used for analyzing equipment in the building and generating a region-level energy-saving control instruction set; the execution control module is used for controlling an air conditioning system, a lighting system and an elevator system in the building in real time; the feedback regulation module is used for monitoring real-time feedback data of the equipment operation parameter set and feeding back to the energy-saving control strategy generation module in combination with environmental parameter data change; according to the invention, autonomy and accuracy of building energy-saving control are 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 invention provides an air conditioner working condition monitoring method and system based on the Internet of Things. The method comprises the following steps: monitoring the air conditioning system and the Internet of Things sensing data of the environment where the air conditioning system is located; performing dynamic simulation on the air conditioning system based on the sensing data of the Internet of Things to obtain a real-time simulation working condition of the air conditioning system; the real-time simulation working condition is input into the fault feature decoupling model, and potential fault risks existing in the air conditioning system are predicted from multiple dimensions so as to obtain multiple prediction results of the potential fault risks; a dynamic coordination algorithm is adopted, dynamic traceability and dynamic traceability and collaborative decision making are carried out on the multiple paths of prediction results, control chain conflicts among the multiple paths of prediction results are eliminated, and potential fault traceability information is obtained; and displaying the real-time simulation working condition and the corresponding potential fault traceability information through a real-time monitoring interface of the air conditioning system. Real-time working condition simulation and intelligent fault tracing of the air conditioning system can be achieved, the fault early warning accuracy and system coordination of the air conditioning equipment are improved, and the operation efficiency of the air conditioning equipment is improved.

The invention discloses an energy-saving control method and system for an airplane ground air conditioning unit, and belongs to the technical field of air conditioning system energy saving.The method comprises the steps that internal and external environment parameters of a cabin are collected in real time, temperature prediction is conducted based on the internal and external environment parameters of the cabin, and a predicted temperature change curve is constructed; running state parameters of the aircraft ground air conditioning unit are called, dynamic energy-saving control is carried out in combination with the predicted temperature change curve, and an energy-saving control strategy is formulated; cabin internal variable parameters are introduced to optimize the energy-saving control strategy, and an optimized energy-saving control instruction set is generated; and the optimized energy-saving control instruction set is executed for cooperative driving, cooling circulation is conducted according to the cooperative driving result, and self-adaptive energy-saving control over the aircraft ground air conditioning unit is achieved. The technical problems of high energy consumption, unstable temperature control and low equipment operation efficiency caused by the lack of precise response capability of an air conditioning unit to dynamic changes of internal and external environments of a cabin in the prior art are solved.

The invention relates to the technical field of air conditioner control, and discloses an intelligent building air conditioner group control energy-saving system and method based on multi-source data fusion, and the method comprises the steps: collecting heterogeneous tracks of the activities of a person in a building, and constructing a space-time energy demand coupling map after cross-modal coupling and abnormal outlier suppression processing; an optimized hang compensation factor beta'is obtained based on the atlas, and an energy demand significance region and a non-energy demand significance region are divided; and finally, making an intelligent air conditioner group dynamic energy-saving control strategy according to a region division result and the optimized hang compensation factor. According to the method, the air conditioner can be accurately regulated, the energy utilization efficiency of the intelligent building air conditioning system is remarkably improved, and energy consumption is reduced.

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 relates to the technical field of air conditioner energy-saving optimization, and discloses an air conditioner energy-saving optimization control system and method based on multi-parameter regulation and control, and the method comprises the following steps: obtaining environment data; the environment data comprises building information model data, indoor environment data, electricity price data and meteorological prediction data; based on the environment data, a three-dimensional game model used for air conditioner energy-saving optimization control is constructed; and solving the three-dimensional game model through a multi-target equalization algorithm, and generating and executing a control instruction of the air conditioning system. Multi-parameter regulation and control of the air conditioning system are achieved, energy can be remarkably saved, the comfort degree of the human body is guaranteed, and meanwhile the electricity utilization cost is reasonably controlled.

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 invention discloses an air environment comprehensive regulation system for a hospital purification area, and the system comprises S1, an environment parameter sensing node network which is used for obtaining data and generating sensing node data; s2, an area function identification and state labeling module which is used for generating an area state description vector; s3, a multi-modal environment state fusion module which is used for constructing an environment dynamic state map of the current area and extracting key influence factors; s4, an adaptive fuzzy control decision module generates an air conditioning parameter set through fuzzy logic and a dynamic weight adjustment mechanism; s5, the multi-channel air conditioning execution device is used for adjusting the regional air according to the air conditioning parameter set; and S6, a comfort level feedback and strategy optimization module which is used for performing closed-loop optimization on the fuzzy rule set in the adaptive fuzzy control decision module and the weight factor of each rule. The method has the advantages of being fine in response granularity, adaptive in control strategy and high in human factor comfort degree fusion degree.

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 discloses an efficient refrigerating machine room air conditioner energy-saving intelligent control system and method, and relates to the field of air conditioner intelligent control. The method comprises the steps that air conditioner operation parameters, machine room equipment operation data and machine room environment data are collected and preprocessed; building a deep neural network by using a deep Q-learning reinforcement learning algorithm to obtain a thermal load demand of the machine room; introducing a nonlinear dynamic compensation function to calculate an air-conditioner dynamic energy efficiency coefficient and air-conditioner refrigeration power, and calculating the rotating speed of an air-conditioner compressor by referring to air-conditioner operation parameters; a PID (Proportion Integration Differentiation) controller is used for carrying out primary frequency conversion on a machine room air conditioner, and a multi-stage adjusting mechanism is used for correcting a primary frequency conversion result in real time according to machine room equipment operation data, machine room environment data change and frequency conversion conditions. According to the air conditioner energy-saving control method, efficient air conditioner energy-saving control is achieved by combining the deep Q-learning reinforcement learning algorithm, the nonlinear dynamic compensation function and the PID controller.

The invention relates to the technical field of air conditioning energy consumption optimization, and discloses an HVAC multi-unit air conditioning system energy consumption optimization matching system and method, a polynomial function model is constructed based on historical data and real-time operation parameters, and a nonlinear relation between energy consumption and power distribution of each unit is accurately described; secondly, the adaptability of the model is enhanced by means of principal component analysis, segmented modeling and the like, global optimization of model parameters is realized in combination with a genetic algorithm, and the prediction precision and robustness of the model are improved; in the optimization control level, a multi-unit collaborative weight factor is introduced, a weighted particle swarm optimization algorithm is adopted, a power distribution strategy is dynamically adjusted, and collaborative optimization control among multiple units is achieved. Through a complete acquisition-modeling-optimization-execution closed-loop control structure, intelligent sensing of the running state of the multi-unit air conditioning system and optimal energy consumption scheduling are achieved.

An intelligent control method of an evaporative cooling air conditioning system based on deep reinforcement learning belongs to the technical field of refrigeration equipment regulation and control, and comprises the following steps: acquiring a space element of a refrigeration area, and converting the space element into a digital model, so as to generate a functional block division strategy of the refrigeration area based on the digital model. Based on a functional block division strategy, acquiring sensor data on each functional block fusion node, and preprocessing the sensor data to obtain a multi-dimensional feature data set; and fusing the multi-dimensional feature data set into a performance index to output a performance evaluation vector of each functional block. And based on the performance evaluation vector, constructing an experience playback pool fusion global state vector so as to carry out offline training on a DDPG algorithm and obtain an intelligent control strategy network. And the real-time performance index of the refrigeration area is input into an intelligent control strategy network to be processed, and a control instruction of the evaporative cooling air conditioning system is output. And controlling the valve opening and the fan frequency of each functional block in response to the control instruction.

The invention discloses a high-voltage power distribution cabinet heat dissipation system based on big data, which relates to the technical field of heat dissipation of electrical equipment and comprises a data acquisition module, a processing analysis module, a man-machine interaction module and a data storage module. An existing heat dissipation mode usually depends on a static fan or air conditioning equipment, dynamic adjustment cannot be carried out according to environment changes and load conditions, and the problems of high energy consumption and slow response exist. The system processes data, eliminates abnormal values, fills missing data, calculates correlation among temperature, humidity, current and fan rotating speed, predicts change trends of the temperature, the humidity and the current through a time sequence model, and displays key data through a graphical interface by a man-machine interaction module, so that the equipment management efficiency is improved; and precious historical data is provided for subsequent fault analysis and tracking, so that optimization of a maintenance strategy of the system and reduction of potential operation risks are facilitated.

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 a large windward area shutter capable of stabilizing large environmental wind induced vibration, and belongs to the technical field of ventilation air conditioning, the large windward area shutter comprises a large shutter group, a driving assembly and a stabilizing assembly, the large shutter group is composed of N layers of large shutters arranged up and down, and N is an integer greater than or equal to 1; the large shutter comprises a window frame and M large shutters with rotating shafts, and M is an integer greater than or equal to 2; the width W of the large shutter is not less than 0.5 m and not more than 2.0 m, and the height H is not less than 1.0 m and not more than 3.0 m; the rotating shafts are installed in the middle of the upper end and the middle of the lower end of the large shutter and hinged to the window frame. The driving assembly is connected with M large shutter hinges of the large shutter; the stabilizing assembly is a transverse stabilizing piece set or a vertical stabilizing piece set or a combination of the transverse stabilizing piece set and the vertical stabilizing piece set. The transverse stabilizing piece group consists of one or more transverse stabilizing pieces and is connected with a large shutter of the large shutter; when N is greater than or equal to 2, the vertical stabilizing piece group is connected with two vertically-adjacent large shutters of the upper-layer large shutter and the lower-layer large shutter; effective constraining force is formed through the stabilizing assemblies, vibration displacement of the large shutters is reduced, and the stability of the large shutters is improved.

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 discloses a laboratory air-conditioning temperature control system and method based on artificial intelligence, and relates to the field of laboratory temperature control. Optimizing the positions of the air supply and exhaust outlets by using a pre-constructed airflow analysis model; according to the optimized positions of the air supply and exhaust outlets, the air conditioning system is used for controlling airflow to flow to the corresponding air supply and exhaust outlets, and directional airflow from the first area to the second area is generated; according to laboratory electrical load data, an optimal charging and discharging strategy of an energy storage device is generated by using a reinforcement learning algorithm, and the reinforcement learning algorithm enables an intelligent agent to learn optimal strategies in different states by constructing a state space including electricity price, electrical load, energy storage electric quantity and photovoltaic power generation and setting a reward function with maximum load stability. Aiming at the low laboratory temperature control precision in the prior art, the temperature control precision is improved by simulating an airflow velocity field and radiation quantity distribution and optimizing the layout of air supply and exhaust outlets by using CFD simulation and a genetic algorithm.