6340results about "Control inputs involving air properties" patented technology

The invention discloses a clean workshop production environment quality control method and system, and relates to the technical field of environment control, and the method comprises the steps: constructing a multi-layer sensing network, deploying temperature and humidity, particle concentration, pressure difference and VOC gas sensors, and carrying out the preprocessing data of each node through edge calculation; fusing the data based on a dynamic weight distribution algorithm, adjusting the weight according to the confidence score, and generating an environment quality comprehensive index; an LSTM pollution diffusion prediction model is established, a diffusion path is calculated in combination with airflow field simulation when pollution suddenly occurs, and an emergency response partition strategy is generated; fresh air system control parameters are optimized through reinforcement learning, a dynamic ventilation frequency adjusting model is established based on a real-time environment quality index and historical energy consumption data, and energy consumption is minimized through a Q-learning algorithm on the premise that cleanliness is guaranteed; a double-threshold early warning mechanism is set, local supercharging purification is started when a comprehensive index exceeds a first-level threshold, a second-level threshold is linked with adjacent areas to form a dynamic isolation barrier, and an intervention scheme effect is simulated through a digital twin system.

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

The invention discloses an integrated cold station intelligent control system based on multi-modal data fusion, and relates to the technical field of data fusion, an intelligent decision and optimization module adopts reinforcement learning and model prediction control based on fused data to realize dynamic optimization of start and stop of a water chilling unit, chilled water temperature setting and water pump frequency adjustment; the fault detection and adaptive optimization module evaluates the data fusion accuracy by calculating an environment entropy value and cold station dynamic response delay time, and dynamically adjusts a control strategy based on a prediction deviation rate and the data fusion accuracy; the closed-loop feedback module analyzes and adjusts control parameters through operation data, intelligent control models between different cold stations are optimized through transfer learning, intelligent deployment of a new cold station is accelerated, and the generalization ability of the models is improved. More accurate, more energy-saving and more stable intelligent control of the cold station is realized, and the operation efficiency and reliability of the cold station system are improved.

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 relates to the technical field of host operation and maintenance, in particular to a cross analysis digital twin intelligent operation and maintenance method and system based on artificial intelligence. The method comprises the following steps that multi-mode sensing data and central air conditioner structure data are obtained, multi-mode air conditioner operation sensing is conducted, and multi-mode operation sensing data are obtained; carrying out feature weight dynamic assignment on the multi-modal operation sensing data to obtain a space-time interaction feature matrix; a real-time updating digital twinborn model is constructed based on the central air conditioner structure data; quantitatively evaluating the equipment degradation degree based on the real-time updated digital twin model to obtain a fault critical point prediction matrix; and performing double-layer game fault risk assessment on the fault critical point prediction matrix, and executing dynamic collaborative optimization of host operation parameters to obtain an intelligent operation and maintenance scheme of the central air conditioner. According to the invention, the operation and maintenance efficiency and the fault detection accuracy can be improved.

Techniques for operating an environmental sensing system are described. In an example, a cloud-based server system receives a first indication that a first type of air pollutant is present within a first structure from a first plurality of indoor air quality (IAQ) sensing devices positioned within the first structure. A second structure within a predefined distance to the first structure is then identified by the server system. The server system then determines that the first type of air pollutant is not present within the second structure. The server system then causes a second plurality of IAQ sensing devices positioned within the second structure to change an operating mode from a normal sensitivity mode to a high-sensitivity mode.

An IAQ sensor module includes: a sensor configured to measure an amount of an item in air, the item being one of particulate matter, volatile organic compounds, and carbon dioxide; a minimum module configured to selectively store the amount of the item as a minimum value of the amount when a mitigation device has been on for at least a predetermined period, the mitigation device being configured to decrease the amount of the item in the air when on; a storing module configured to selectively store the minimum value as an initial minimum value; an offset module configured to determine a drift offset for the sensor based on a difference between the minimum value and the initial minimum value; and an adjustment module configured to determine an adjusted amount of the item in the air at the IAQ sensor module based on the amount and the drift offset.

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 the technical field of intelligent control, and particularly discloses a linkage control method for energy-saving ventilation equipment, which comprises the following steps of: acquiring indoor and outdoor temperature, indoor carbon dioxide concentration and personnel occupancy state data in real time; according to the method, deep collaborative analysis is carried out on the indoor personnel occupancy state and the deviation between the carbon dioxide concentration and a target set value, so that joint characterization of the current indoor fresh air volume regulation and control requirement is achieved, control parameters of a fresh air volume PI D controller are dynamically optimized accordingly, and a basic fresh air volume regulation instruction is calculated. On the basis, thermal disturbance introduced by fresh air is further quantified based on the outdoor and indoor temperature difference, and the thermal disturbance serves as a feedforward signal to be compensated to an air conditioner load adjusting loop, so that dynamic adjustment of the air conditioner load is achieved. In this way, cooperative control over ventilation and air conditioning systems can be achieved, the fresh air-air conditioning equipment energy consumption coupling conflict in traditional control is restrained, and therefore the building energy consumption is reduced while the indoor environment quality is guaranteed.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

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 air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

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-cooled air conditioner control, in particular to an air-cooled air conditioner control method and system based on deep learning, and the method comprises the following steps: obtaining sensor readings of indoor temperature, outdoor temperature and humidity and Wi-Fi channel state information, judging the position number and the activity state of personnel, calculating the position distribution of the personnel in combination with a time window, and calculating the position distribution of the personnel. And integrating to obtain a multi-dimensional environment and load state vector. According to the method, indoor and outdoor temperature, humidity, Wi-Fi channel state information and personnel position distribution and activity states are integrated through a multi-dimensional environment and load state vector, the personnel density change trend is dynamically calculated through a time window, the comprehensiveness and real-time performance of environment perception are enhanced, and high-dimensional data support is provided for control decision making. And a basic comfort degree deviation index is constructed based on the temperature deviation value and the humidity deviation value, and a multi-target performance index set is generated in combination with the wind speed value, the compressor start-stop frequency and the energy consumption power.

The invention relates to the technical field of energy consumption management, and particularly discloses an AI-based clean room dynamic energy consumption management system and method. According to the method, a deep learning algorithm is introduced to carry out deep interaction analysis on clean room indoor air quality parameter time sequence data and environment change driving event data such as the number of indoor personnel, the external environment temperature, the room door opening and closing state and the equipment power, so that intelligent reasoning of the dynamic evolution process of the clean room indoor air quality is realized; according to the method, the air quality parameter prediction result is compared with the preset air quality standard range so as to predict the air quality change in the future short time under the current driving event, and then the short-time air quality parameter prediction result is compared with the preset air quality standard range so as to carry out feedforward adjustment on the HVAC system operation parameters of the clean room according to the difference between the short-time air quality parameter prediction result and the preset air quality standard range. According to the method, environmental changes can be actively predicted, smooth pre-adjustment is conducted in advance, the air quality in the clean room is kept within the preset standard range, meanwhile, unnecessary energy waste is remarkably reduced, and the environmental stability is improved.

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 air feeder regulation and control, and discloses an intelligent regulation and control method and system for an air conditioner air feeder. Performing parameter preprocessing to obtain an environment data set; according to the environment data set and the indoor geometric features, a fluid dynamic model is adopted to calculate and simulate the motion state of indoor airflow, and a temperature prediction result is obtained; performing result analysis based on a preset constraint condition to obtain an initial parameter scheme; performing feedback processing on the parameter scheme to obtain real-time feedback data; based on a preset stability standard ratio, a tuyere parameter control algorithm is adopted for calculation, and a parameter correction scheme is obtained; and balance adjustment is conducted, a final parameter scheme is obtained, and dynamic response and accurate control over the air supply state are achieved in combination with the correlation mechanism of indoor airflow movement and air outlet parameters.

The intelligent control method comprises the steps that a plurality of sensors and frequency converters are installed and used for monitoring the air temperature and humidity, the moisture content and the front-back pressure difference of a fresh air filter in real time; regulating and controlling the rotating speed of an air feeder, the rotating speed of a regeneration fan and the rotating speed of a rotating wheel through a frequency converter, obtaining sensor data under corresponding rotating speeds, and establishing an air supply quantity model, a fresh air quantity model and a rotating wheel dehumidification quantity model; the power of an air feeder, the front-back pressure difference of a fresh air filter and air parameter data are collected in real time, and the current dehumidification load is calculated according to a fan air supply amount model, a fresh air amount model and a preset formula; and selecting a preset control strategy to dynamically adjust the rotating speed of the rotating wheel, the rotating speed of the regeneration fan and the regeneration temperature according to the current dehumidification load and / or the rotating wheel dehumidification amount model so as to realize indoor humidity control. Start and stop can be automatically controlled and the dehumidification amount can be matched according to the actual wet load, excessive dehumidification or idle work is avoided, and the dehumidification efficiency is improved through multi-parameter cooperative control.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention relates to the technical field of ward air purification systems, and discloses a ward air purification system which comprises an environment sensing module, a purification path planning module, a filter unit configuration module, a purification execution module, a dynamic adjustment module, a state monitoring module and a feedback control module. The environment sensing module obtains ward space information to construct an air flow topology; the purification path planning module calls air quality data to label the topology; the filter unit configuration module sets purification nodes and adjustment intervals; the purification execution module implements basic purification; the state monitoring module analyzes data to determine operation state characteristics, and the feedback control module generates monitoring records and performs visual feedback. The system can accurately perceive the air quality of the ward, scientifically plan the purification path, dynamically adjust the purification strategy, effectively improve the air quality of the ward and guarantee the health of patients and medical staff, and has high practical value.

The invention relates to the technical field of mine intelligent ventilation and poison filtration, and discloses a mine intelligent ventilation and poison filtration dynamic regulation and control method and system.The mine intelligent ventilation and poison filtration dynamic regulation and control method comprises the steps that a neural physical hybrid model is constructed, physical constraint conditions are combined with a deep neural network, and mine airflow field distribution is predicted; decomposing the neural physical hybrid model into three hierarchical sub-models, and deploying the three hierarchical sub-models to corresponding computing nodes to realize a hierarchical computing architecture; establishing a multi-level cooperation mechanism based on federal learning, and realizing distributed model training and optimization; constructing a task allocation decision model, and dynamically determining an optimal execution hierarchy of an airflow field calculation task according to task characteristics, resource states and real-time requirements; dynamically adjusting the precision and performance balance of airflow field calculation; according to the method, the key technical problems in the mine ventilation and poison filtration system are solved through the neural physical hybrid model, the multi-level distributed computing architecture and the federal learning collaboration mechanism.

A system and method for controlling HVAC components of a building are disclosed, the method including: receiving user objective indicators, each indicating a corresponding user objective for the HVAC components; receiving a plurality of forecasts, each predicting a dynamic state of the building or a usage parameter of one of the HVAC components; receiving a plurality of current states of the building; maintaining a plurality of control modules; and upon detecting a change in at least one of the forecasts or at least one of the user objective indicators: performing a plurality of HVAC control simulations, each simulating the performance of a corresponding subset of the control modules; selecting a subset of the control modules based on results of the simulations and the one or more user objective indicators; and deploying the selected subset of control modules to control the HVAC components.

The invention provides an intelligent control method and system for air purification equipment. According to the method, multi-dimensional environment data, including pollutant three-dimensional space data captured by air purification equipment, real-time airflow velocity and concentration distribution, in an open type pollution environment are obtained, a pollutant distribution thermodynamic diagram is generated, and the multi-dimensional data and the thermodynamic diagram are jointly coded to form a pollution concentration gradient map. Meanwhile, a historical control strategy data set is collected, cross-environment matching is carried out on the historical control strategy data set and the atlas, and diversion control characteristics associated with the pollutant diffusion direction are extracted; on the basis of the mapping relation between the characteristics and the atlas, a dynamic flow guide parameter set is determined, an intelligent control instruction of the air purification equipment is finally generated, and directional airflow disturbance and air distribution optimization control over the high-concentration area are achieved. According to the technical scheme, the intelligent control efficiency and precision of the air purification equipment can be improved.

The invention discloses an air conditioner efficiency test system and a control method of an air conditioner efficiency test platform. The method comprises the following steps: dynamically adjusting the air dry bulb / wet bulb temperature of a test room of an existing test bed based on a dynamic temperature control adjusting technology; acquiring a real-time building thermal load of the test room through a building thermal load prediction simulator; the dynamic change of the air dry-bulb temperature of the test room is calculated in real time through the indoor air dry-bulb temperature prediction simulator; and the dynamic change of the air wet bulb temperature of the test room is calculated in real time through the indoor air wet bulb temperature prediction simulator. According to the invention, the problem that the dynamic operation energy efficiency and performance parameters of the tested air conditioner cannot be obtained through the enthalpy difference method test bench because the existing enthalpy difference method test bench cannot control the dynamic change process of the building thermal load of the test room along with the refrigeration / heating capability of the tested air conditioner in the prior art is solved; and the actual operation performance level of the air conditioner cannot be reflected.

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

A dehumidifier according to an embodiment may include a housing including a suction grill formed on a rear surface thereof, and a discharge port formed on an upper surface thereof; a dehumidification module provided inside of the housing; a water tank detachably provided on one side of the housing and that stores condensate generated during a dehumidification process; a discharge cover that opens and closes the discharge port; a tilting module connected to a bottom of the discharge cover inside of the housing to tilt the discharge cover; and a shielding cover that accommodates the tilting module. The dehumidification module may include at least a compressor, a condenser, an evaporator, and a fan module. The tilting module may include a tilting motor fixed to the shielding cover; a tilting gear connected to a shaft of the tilting motor; and a tilting neck curved at a predetermined curvature and having on one surface thereof a tilting rack that engages with the tilting gear.

The invention relates to the technical field of intelligent control, in particular to an air conditioner control system and method based on a smart home. The air conditioning module is used for refrigerating or heating air; the image acquisition module is used for acquiring an image of a user smart home environment and defining the image as a first image; the analysis module is used for judging whether a user exists in the first image or not, identifying the identity, position and behavior of the user, retrieving a personal data packet of the user based on an analysis result, and outputting a control signal; the temperature adjusting module is used for adjusting the air supply temperature and speed of each air supply outlet; the wind direction adjusting module is used for adjusting wind directions of the air supply outlets. The operation parameters of each air supply outlet of the air conditioner are automatically adjusted based on the corresponding personal information database by recognizing the feature information such as the identity, the position and the behavior of the user, so that corresponding operation control is performed when different users are in different areas and different states, and the user experience is improved. And the air supply parameters of the air conditioner blown to the user are more suitable for the personalized requirements of the user in the current state.

The invention relates to the technical field of refrigeration equipment control, in particular to an air conditioner control method and system. Firstly, a refrigerant flow prediction model is established by combining the advantages of LSTM and XGBoost, a time sequence and static characteristics are accurately captured, prediction precision and system adaptability are improved, reliable data support is provided for intelligent regulation and control, refrigerant distribution is optimized, energy efficiency is improved, and energy consumption is reduced; secondly, the opening degree of an electronic expansion valve and the frequency of a compressor are controlled in a feedforward mode through a multivariable cooperative control algorithm, the load and the flow are accurately matched, the system efficiency is improved, it is ensured that all assemblies operate in a coordinated mode, energy waste is reduced, and stability and comfort are optimized; finally, PID feedback control is combined, the frequencies of the electronic expansion valve and the compressor are finely adjusted in real time, the optimal energy efficiency and stability of the system are ensured, closed-loop control is achieved, the temperature control precision is further improved, fluctuation is reduced, and the energy efficiency is optimized.

The invention discloses an intelligent regulation and control method based on perception and dynamic partitioning. In order to solve the problem that an existing public building air conditioning system is difficult to deal with personnel activity intensity difference, emotion fluctuation and space density dynamic change, accurate recognition of personnel behaviors and activity density is achieved by fusing YOLOv5, OpenPose and LSTM networks, and the group emotion state is obtained by combining infrared thermal imaging and expression recognition. Based on behavior-density partitioning and an emotional load correction model, space thermal load distribution is dynamically judged, an NSGA-I multi-objective optimization algorithm is introduced, the air conditioner energy efficiency ratio and the thermal comfort degree are optimized, and differential intelligent control over regular and irregular partitioning is achieved through self-adaptive regulation and control of a meta-reinforcement learning control framework. According to the method, the building energy consumption is effectively reduced, the local comfort degree is improved, good real-time performance and intelligent adjusting capacity are achieved, and the method is suitable for optimization design of an intelligent air conditioning system of a large public building.