24724results about "Space heating and ventilation control systems" patented technology

The invention provides a cold machine and water pump linkage energy-saving control method under a low-load working condition, which relates to the technical field of information and comprises the following steps of: dynamically predicting load requirements under the low-load working condition by analyzing historical operation data of a refrigeration system and combining weather forecast information, equipment operation parameters and data of building personnel activity rules; obtaining a cooling capacity demand curve and a load demand prediction result in future preset time; establishing a system low-load operation mode optimization model, taking the screened control combination meeting the preset energy-saving operation target as model input, and obtaining an optimized system low-load operation mode through the training model; the optimal energy efficiency interval is determined by analyzing the relation between equipment energy consumption and cold storage capacity, and during load demand prediction and control optimization, the interval serves as a target constraint to guide a refrigerator and water pump equipment to operate in a high-energy-efficiency area. And the operation efficiency and the energy-saving effect of the refrigerating system under the low-load working condition are improved.

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 relates to the technical field of air conditioner chilled water control, in particular to an air conditioner chilled water control method and system based on deep learning, and the method comprises the following steps: collecting states of an air conditioner water chilling unit, including water pump frequency and valve opening feedback, and pipe network water supply temperature, return water temperature, flow and differential pressure operation readings; according to the method, the state parameters of the water chilling unit are dynamically collected, the system operation state feature set is constructed, real-time fusion and standardization processing of multi-source heterogeneous data are achieved, the problem of time sequence dislocation caused by sensor sampling frequency differences is solved, the heat load change key input quantity is recognized based on the feature set, and the system operation state feature set is established. And the performance attenuation trend of the water pump is accurately pre-judged in combination with prediction calculation, and extra energy consumption caused by lagging adjustment is reduced. A layered optimization framework with total energy consumption minimization as a target is introduced, water supply temperature, pressure difference setting and unit operation combination are decoupled into independent optimization sub-problems, and invalid work of a water pump is reduced on the premise that the tail end heat requirement is met.

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 discloses a central air conditioner load self-adaptive regulation and control method based on artificial intelligence, which relates to the technical field of heating ventilation air conditioner automatic regulation and control, and comprises the steps of edge end data preprocessing, deep learning load prediction and self-adaptive updating, intelligent control strategy, closed-loop feedback and continuous optimization and fault diagnosis and remote operation and maintenance. Through edge end denoising and feature labeling, the data transmission pressure is remarkably reduced, and the response speed is increased; the deep learning model is combined with online iteration and reinforcement learning, key parameters such as chilled water temperature and water pump frequency are accurately regulated and controlled, and energy consumption and comfort are considered; according to the method, prediction errors and equipment states are monitored, a model and a control strategy are synchronously updated when abnormity is detected, fault early warning is triggered in combination with health assessment, energy consumption is effectively reduced, the service life of equipment is prolonged, and finally an efficient and stable self-adaptive regulation and control system capable of being operated and maintained online in a long period is constructed.

The invention discloses an intelligent linkage control management system based on air conditioner application, and relates to the technical field of intelligent control and energy management, and the system comprises a load prediction module which obtains an air conditioner load demand prediction value through an intelligent load prediction model based on preprocessed environment energy consumption data; the optimization scheduling module is used for optimizing the current air conditioner operation mode through the building energy management system based on the air conditioner load demand prediction value, and obtaining an optimized air conditioner operation scheduling instruction; the intelligent adjusting module is used for intelligently adjusting the operation parameters of the air conditioner system based on the optimized air conditioner operation scheduling instruction, adaptively adjusting the energy efficiency of the air conditioner and obtaining operation feedback data; and the fault management module is used for identifying potential fault risks of the air conditioner and formulating fault risk prevention measures through a time sequence analysis method based on the operation feedback data. According to the invention, through innovation of an intelligent load prediction and fault prevention mechanism, the energy utilization efficiency and the system stability are effectively improved.

The invention provides a machine room control method and system based on communication between an air conditioner terminal and a water chilling unit, and the method comprises the steps: obtaining three-dimensional data and equipment attribute data of a machine room and accessory equipment of the machine room through a server, and building a digital twinborn model of the machine room; the server establishes a dynamic load prediction model and sends the dynamic load prediction model to a first controller of the water chilling unit; the intelligent sensor collects environment data and equipment data; a second controller of the air conditioner terminal obtains the environment data and sends the environment data to the first controller; the first controller predicts the cold load change according to the dynamic load prediction model and the historical data and adjusts the operation strategy of the air conditioning system; the server simulates a machine room environment and an equipment operation condition in real time by using a digital twin model, generates a control strategy in combination with a self-adaptive control strategy after evaluating a result, and sends the control strategy to the first controller; and the first controller dynamically adjusts operation parameters of the air conditioning system according to the strategy. By means of the scheme, efficient, intelligent and accurate real-time monitoring and control over the energy efficiency of the air conditioning system can be achieved.

A system and method for HVAC optimization is disclosed. The system may include a digital twin (112) of an HVAC system (114) installed in a premises (108). The digital twin (112) is based on design parameters, operational data, and PMV-based thermal comfort analysis. The system further includes an RL agent (202) that is configured to receive and process at least real-time environmental conditions, occupancy patterns, and PMV value associated with the premises (108) of the HVAC system (114), identifying one or more optimal actions to adjust one or more HVAC parameters. The digital twin (112), driven by the identified optimal actions, may dynamically simulate the HVAC system (114) and the RL agent (202) may optimize operations of the HVAC system (114) based on the identified optimal actions, ensuring seamless and adaptive thermal comfort and energy efficiency management within the premises (108).

The invention provides a central air-conditioning system real-time fault prediction method and system based on digital twinning, and the method comprises the steps: deploying various types of sensors for multi-dimensional detection, and collecting the data of the sensors; edge computing nodes are deployed around the central air conditioning unit; a digital twinborn model based on the central air conditioning unit is constructed according to the preprocessed data, a dynamic view of key performance indexes is provided, and the state of the central air conditioning unit is mapped and simulated in real time; designing a fault prediction model, and predicting and identifying faults; deploying the fault prediction model at an edge computing node and operating the fault prediction model, and performing real-time fault monitoring and diagnosis; when a fault is found, the AI-drive maintains decision support, and automatically adjusts a fault prediction model parameter or arranges a maintenance strategy; and constructing a learning loop based on performance feedback, and optimizing a fault prediction model and a maintenance strategy. Quantum calculation, the digital twinning technology and edge calculation are fused, and the operation efficiency and reliability of the central air conditioning system are remarkably improved.

The present application relates to the field of energy, and specifically to an energy consumption grading optimization method, a computer device, and a storage medium. The method comprises: acquiring target operation data related to energy consumption optimization; performing data grading on the target operation data to obtain data sets corresponding to optimization levels, wherein the data sets of different optimization levels correspond to different optimization indexes; determining an initial optimization level on the basis of a device operation mechanism and a current state indicated by the target operation data; and using the initial optimization level as an optimization starting point, and performing grading optimization calculation on the basis of optimization models of the optimization levels and the data sets corresponding to the optimization levels to obtain a target energy consumption optimization strategy, so as to optimize the energy consumption of a heating and ventilation device on the basis of the target energy consumption optimization strategy.

The invention relates to the field of environmental pollution monitoring, in particular to an on-line environmental pollution monitoring method and system, and the method comprises the steps: collecting the pollutant concentration data of a parking lot, and carrying out the data preprocessing to obtain the pollutant concentration preprocessing data; extracting spatial distribution characteristics and time characteristics of pollutant diffusion from the pollutant concentration preprocessing data according to a multivariable space-time prediction model, and comprehensively analyzing the space-time trend of pollutant diffusion to obtain the diffusion trend of the pollutant concentration and obtain the predicted concentration of the pollutant; performing data analysis on the predicted pollutant concentration and a preset safe concentration threshold value, and when the predicted pollutant concentration is greater than the preset safe concentration threshold value, triggering an alarm and performing ventilation operation; the problems that pollutant monitoring in a parking lot is not comprehensive enough, pollutant concentration cannot be effectively predicted in time, and a high-concentration area of pollutants cannot be judged in time, so that air quality cannot be optimized and managed in time, and personnel health cannot be guaranteed are solved.

The invention discloses an intelligent monitoring method and system for heating and ventilation of a thousand-level dust-free workshop based on digital twinning. The method specifically comprises the steps that SolidWorks software is used for conducting three-dimensional modeling on heating and ventilation equipment and environment of the thousand-level dust-free workshop; a physical device, an IoT sensor network, a PLC controller and a data transmission module are configured to realize real-time acquisition, processing and transmission of a device operation state and environmental parameters; dynamically optimizing the operation state of the equipment and the environment parameters of the workshop; a three-dimensional visual interface is developed based on an OpenUSD platform, and heating and ventilation equipment and environmental parameters in a workshop are visually presented in a dynamic and real-time mode; an equipment health state curve is generated through a time sequence prediction algorithm, and the equipment health score and trend change are visually displayed, so that a user can grasp the equipment operation state in real time. Accurate control over the temperature and humidity, the cleanliness and the pressure difference in the workshop is achieved, the cleanliness and reliability of the production process are improved, and good adaptability and expansibility are achieved.

A system including an air conditioning system; an energy storage device; a power converter; and a controller configured to control the power converter to supply power from at least one of an AC power grid and the energy storage device to one or more components of the air conditioning system.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

The invention provides an air conditioner personalized intelligent control method, system and device and a storage medium, and relates to the technical field of air conditioner control, the method comprises the following steps: combining first people flow data and historical people flow data of a target area within a first preset duration to predict second people flow data within a target duration; determining the reference temperature of the target area within the target duration in combination with the outdoor temperature and the area characteristics of the target area; in combination with the reference temperature and the second visitor flow rate data, temperature adjusting parameters of the target area within the target duration are determined, and an initial operation scheme of an air conditioner of the target area is formulated according to the temperature adjusting parameters; and according to the environment monitoring parameters of the target area, determining an environment load coefficient, and according to the environment load coefficient, generating a target operation scheme. The method has the technical effects that the environment change is predicted in advance, the air conditioner operation parameters are adjusted in time, and it is guaranteed that the indoor environment can reach the comfortable state in time.

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 relates to the technical field of intelligent dehumidification, in particular to a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, and the method comprises the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, executing time synchronization and marking, and carrying out data normalization processing to obtain initial environment humidity detection data; according to the invention, through humidity detection, advanced data processing and trend prediction, the precision and foresight of dehumidification control are improved, subtle humidity change is identified by using a difference technology, real-time fluctuation of environment humidity is accurately reflected, the humidity change in a short period is predicted by using a linear regression model, the accuracy of a prediction result is improved, prediction errors are reduced, and the method is suitable for large-scale popularization and application. By means of the calculation result of the humidity setting difference of the different areas, accurate adjustment, continuous monitoring and dynamic adjustment of dehumidification parameters of the dehumidification equipment in the different areas are achieved, it is ensured that the equipment is kept in the optimal operation state all the time, the energy efficiency is remarkably improved, and the comfort and safety of the living and working environment are improved.

The invention discloses an intelligent regulation and control method and system for a photovoltaic energy storage air conditioner. The method comprises the steps that photovoltaic related data, energy storage related data and air conditioner operation related data are collected; inputting the photovoltaic related data into the photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction result; inputting the air conditioner operation related data into the air conditioner load prediction model to obtain an air conditioner load prediction result; inputting the photovoltaic generating capacity prediction result, the air conditioning load prediction result, the energy storage related data and the photovoltaic related data into an energy storage optimization scheduling model to obtain an energy storage charging and discharging decision; constructing a power supply strategy core rule base based on the energy storage related data, the electricity price peak time period and output results of the photovoltaic power generation prediction model and the air conditioner load prediction model; and optimizing an output result of the energy storage optimization scheduling model based on the power supply strategy core rule base. The method has the outstanding advantages of high reliability, high photovoltaic utilization rate, low dependence degree on a power grid and the like.

The invention relates to a multi-dimensional air purification method and system based on AI intelligent big data deep analysis, and solves the problem that it is difficult for an air purifier to achieve uniform and efficient purification of the whole indoor space. According to the coordinates and the areas of the sub-regions contained in the target region cluster, the mass center position of the target region cluster is obtained through calculation and serves as the target position of the air purifier; a path planning algorithm is used for planning a moving path from the current position to the target position for the air purifier, and the air purifier is controlled to move according to the moving path; and the size, the pollutant concentration and the type of the polluted area of the area cluster are obtained and input into a preset air purifier purification parameter adjustment model, air purifier purification parameters are output, and purification parameter adjustment is conducted according to the air purifier purification parameters after the air purifier moves to the target position. The indoor air purification device has the following effect that the indoor air purification efficiency and quality are improved.

The invention provides a subway station central air conditioner full-link energy-saving system and method based on wind balance. Belongs to the technical field of air-conditioning energy conservation. The method comprises the steps that environmental data inside and outside a subway station are collected in real time through a sensor network, and the collected environmental data are uploaded to a central air conditioner full-link energy-saving control platform; the central air conditioner full-link energy-saving control platform dynamically adjusts the air volume of the air supply pipe and the air return pipe through fan control according to the collected data; by collecting environment data in real time and combining an advanced control algorithm, the system can dynamically adjust the rotating speed of a fan, the load of a refrigerating unit, the ratio of fresh air to return air and the like, so that the running state of the air conditioning system is optimized, energy consumption is effectively reduced, and the overall energy-saving effect of the system 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 provides an indoor radiation floor system multi-target regulation and control method and system based on load prediction. The method comprises the following steps: based on indoor environment parameters, outdoor environment parameters, indoor personnel activity information and equipment parameters, adopting a cold and wet load prediction model to obtain a cold load and a wet load; based on the outdoor environment parameters, the cold load, the wet load and the air conditioning system operation information, an indoor environment and energy consumption prediction model is adopted, and the indoor temperature, the condensation temperature difference, the air system energy consumption and the water system energy consumption are obtained; the outdoor environment parameters, the cooling load, the wet load and the air conditioner system operation information serve as variables, target functions of the indoor temperature, the air conditioner PMV, the condensation temperature difference, the air system energy consumption and the water system energy consumption are built, corresponding weights are configured, and a total target function is calculated; and working parameters of the air conditioning system are set as constraint conditions, the total objective function is solved, and optimal regulation and control parameters are obtained.

The invention provides a BIM-based central air conditioner safety monitoring method and system. The method comprises the following steps: generating a BIM model of a building; according to the internal structural layout of the building in the BIM model and the arrangement of the central air conditioning equipment, carrying out initial safety area division on the building, carrying out dynamic adjustment based on a risk index of a time change regular term, and updating the risk index into the BIM model in real time; fault features are extracted from the real-time environment data and the equipment state data, and whether a fault exists or not is preliminarily judged; constructing a fault traceability model according to a fault positioning result, and determining a fault propagation path; a digital twinborn model is constructed for risk prediction, and a preventive maintenance plan is automatically generated; and designing an adaptive optimization algorithm based on reinforcement learning to maximize the operation efficiency of the system and minimize the risk level, and performing closed-loop feedback optimization. The invention provides a comprehensive, intelligent and efficient central air-conditioning system management scheme, and has wide application prospect and practical value.

The invention provides a medical waste whole-process supervision method based on an intelligent medical waste monitoring platform, and the method comprises the steps: enabling collected smell data to be matched with pre-established medical waste smell component data, judging the type of medical waste to which a smell source belongs, and determining whether the smell concentration exceeds a preset threshold value or not; if the odor concentration exceeds the standard, microorganism information is obtained according to the relevance between the odor component information and microorganisms, and the microorganism breeding risk is evaluated in combination with temperature and humidity control parameters of the air conditioner; simulating airflow distribution of an air conditioning system, predicting diffusion paths and influence ranges of odor in different areas of a hospital by combining with position layout of an air outlet of the air conditioner, and generating an odor diffusion thermodynamic diagram; and according to the optimized disinfection scheme, the air conditioning system is controlled to enter a disinfection mode, the fresh air introduction proportion and the filtering efficiency grade are adjusted, a disinfectant is sprayed in the key area, and the disinfection effect is monitored in real time.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention discloses an air purification system for a clean workshop. The air purification system comprises a self-adaptive control unit, an air quality monitoring module, an air flow control assembly, an environmental parameter adjusting assembly, a self-optimization filtering assembly, a human-computer interaction interface and predictive maintenance system, an energy efficiency optimization module and a remote diagnosis and early warning system. On the basis of intelligent technologies such as machine learning, reinforcement learning and a data fusion algorithm, real-time monitoring, dynamic optimization and energy efficiency management of the air quality of the clean workshop are achieved. Based on real-time data and historical data, an algorithm is used for carrying out self-adaptive optimization on the air purification system, and manual intervention is reduced. And the running state of the system is dynamically adjusted through the energy efficiency optimization module, so that the energy consumption is reduced while the cleanliness is ensured. The system can diagnose equipment faults in advance, reduce downtime and prolong the service life of equipment. And due to a man-machine interaction interface and a remote monitoring function, an operator can know the state of the system at any time and carry out regulation and control.

The invention relates to the related technical field of refrigeration energy-saving control, in particular to an energy-saving control method and system for refrigeration equipment, and the method comprises the steps: achieving the state linkage of an air valve, a compressor and an air feeder through control logic based on a refrigeration equipment unit; after starting, an initial strategy is formulated according to refrigeration requirements to manage a plurality of microclimate spaces; in combination with video monitoring, personnel tracks are captured, behavior modes are analyzed, a local customized control strategy is generated, and a comprehensive refrigeration control strategy is formulated, so that the technical problem of unnecessary energy waste caused by incapability of accurately matching refrigeration requirements and equipment operation states due to lack of real-time sensing analysis on actual operation conditions is solved; the overall refrigeration demand balance is considered, the local area is considered, the refrigeration intensity is flexibly adjusted, accurate matching between the refrigeration demand and the equipment operation state is achieved, distribution of refrigeration resources is optimized, energy waste is effectively reduced, the overall operation efficiency is improved, and the technical effect of energy-saving, intelligent and efficient management of the refrigeration equipment is achieved.

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.