1226 results about "Central air conditioning" patented technology

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

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 provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention relates to an ice crystal evaporator and an ice crystal water cooling device producing from the same, belonging to the technical field of refrigeration. The ice crystal evaporator is characterized in that a liquid inlet channel is formed by an evaporator tube, a liquid inlet and a liquid outlet in a tubular casing; and the outer channel of the evaporator tube of the tubular casing is divided into more than one S-shaped channel forming a water return channel through a water inlet of a water divider and a water outlet of a water collector. The ice crystal water cooling device is characterized in that the return air inlet of the ice crystal evaporator is communicated with a compressor, a condenser, a liquid feeding valve, a dry filter, a liquid feeding electromagnetic valve anda thermostatic expansion valve in sequence, and then is communicated with the liquid inlet of the ice crystal evaporator; a cooling water inlet / outlet of the condenser is connected with a cooling tower in series; and a water inlet / outlet of the ice crystal evaporator is communicated with a water circulating pump and an ice crystal energy storage tank in series. The invention has the advantages ofhigh cooling efficiency, no icing and low cooling temperature and is suitable for production of energy storage central air-conditioning, marine fishing, seafood processing, fruit, vegetable or meat processing, and the like; the cooled medium is municipal water.

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.

The invention discloses a central air conditioner energy-saving control method based on AI self-adaptive adjustment, and relates to the technical field of intelligent control, and the method comprises the following steps: collecting environmental data, equipment operation data and energy consumption data of a central air conditioner in real time, preprocessing multi-modal data, constructing a physical constraint equation in combination with a thermodynamic law, and generating a multi-modal data set; generating a multi-target optimization control instruction according to the optimization weight, solving an optimal equipment parameter combination through a Pareto frontier algorithm, and transmitting the optimal equipment parameter combination to a central air conditioner actuator; actual data after instruction execution are collected, the deviation degree of the energy-saving efficiency and the comfort degree is calculated, physical information neural network parameters are updated through a meta-learning framework, and thermodynamic partial differential equation coefficients are adjusted. According to the method, by constructing a multi-modal physical information fusion framework and a dynamic closed-loop optimization system, the comprehensive regulation and control capability of the central air conditioner in a complex building environment is improved.

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 device control, and provides a control method for a central air conditioner tail end detection device, which is characterized in that scene dimension parameters are comprehensively analyzed, dynamic reference values of basic operation parameters are accurately determined, and quick identification and positioning of abnormity are realized in combination with multi-dimension verification; according to the method, the limitation that traditional central air conditioner control depends on fixed parameter setting and single abnormity judgment is broken through, the self-adaptive capacity of a detection device to complex scenes is remarkably improved, the operation stability of the central air conditioner is improved, and the method is suitable for popularization and application. The abnormal response time is greatly shortened, the energy consumption is reduced, the problems that an existing central air conditioner is low in operation efficiency, inaccurate in abnormal diagnosis, waste in energy and the like are effectively solved, more comfortable, energy-saving and intelligent use experience is provided for a user, meanwhile, the operation and maintenance cost is reduced, and the service life of the central air conditioner is prolonged.

The invention relates to an intelligent regulation and control method for a fan coil of a central air conditioner, relates to an internet of things technology, and aims to solve the problems that multi-source environment parameter data is large in noise, working condition modeling is inaccurate, and equipment regulation response and energy consumption optimization are not coordinated. A regulation and control system integrating multi-parameter distributed sensing, data preprocessing, feature extraction and cross-domain knowledge migration is provided. A high-quality structured data set is formed through multi-dimensional real-time acquisition of room temperature and humidity, fan current, valve opening, human traffic, external weather and building information and in combination with denoising, normalization, alignment and missing completion. And a multi-target neural network and digital twinborn simulation are utilized to realize load trend prediction, equipment abnormity early warning and virtual deduction of an adjustment strategy, an optimal adjustment time sequence window and an activation threshold are output, and dynamic adaptive control is realized in combination with reinforcement learning.

The invention discloses a multi-type central air conditioner integrated optimization automatic control system, which relates to the technical field of air conditioner control, and comprises an equipment dynamic modeling module, an MPC optimization control module, a dynamic reference and diagnosis module, an RL driving MPC setting module, a dynamic sliding window processing module and a large model management module, the dynamic sliding window processing module gathers and performs continuous cutting and context association processing on information streams from the modules to generate serialized information fragments adaptive to the input length of the large model management module, and the large model management module serves as a core cognition and decision center; and performing deep fusion analysis on the received serialized information fragments by using an internal attention mechanism to realize advanced operation strategy generation and deep fault reason tracing, and providing decision support and system state interpretation through a natural language interaction interface. And finally, comprehensive cognition, predictive insight and self-adaptive evolutionary management of the global state of the central air conditioning system are achieved.

The invention relates to the technical field of air conditioner simulation optimization, in particular to an efficient machine room multi-subsystem cooperative control method based on intelligent simulation, and the method comprises the steps: building a digital twinborn model and a virtual simulation environment of central air conditioner key equipment; performing simulation analysis on the health state and the operation performance of key equipment in the digital twin model through a virtual simulation environment, and establishing a performance reference model; acquiring an external data source to obtain equipment operation parameters; in the virtual simulation environment, the equipment operation parameters are analyzed based on the performance reference model, and health state parameters and operation performance parameters are obtained; dynamically updating the digital twin model based on the health state parameters and the operation performance parameters to obtain an equipment state twin model; and based on the equipment state twin model, performing simulation design in a virtual simulation environment to obtain equipment compensatory cooperative control parameters. According to the invention, through simulation analysis of the digital twin model, cooperative control of the central air-conditioning subsystem is realized.

The invention belongs to the technical field of central air conditioner control, and particularly relates to an internet-of-things edge control method based on a central air conditioner water chilling unit, which comprises the following steps of: acquiring multi-dimensional operation parameters in real time; the parameters are classified according to hospital operating rooms, wards and public areas and then input into a cold load prediction model, and future cold load values and the like of all the areas are predicted through a long-short term memory (LSTM) network. Through deep fusion of Internet of Things edge calculation, a deep learning model and a dynamic optimization algorithm, the key problems of insufficient control precision, large energy efficiency fluctuation, cooperative control lagging and the like of a traditional central air conditioning system in a complex scene are solved, and the purposes of deeply fusing real-time data features, dynamically optimizing a control strategy and improving the control efficiency are achieved. And a multivariable coupling relationship can be effectively processed, so that the effect of high-precision and high-response temperature control requirements in a medical scene can be met.

The invention relates to the technical field of central air conditioners, and discloses a central air conditioner water system energy-saving and consumption-reducing method which comprises the following steps: S1, acquiring operation data of a central air conditioner water system, and preprocessing the operation data; and S2, a cold load prediction model is established based on the operation data, and the model combines wavelet threshold denoising, a convolutional neural network and a long and short term memory network to accurately predict the future cold load demand. According to the energy-saving and consumption-reducing method for the water system of the central air conditioner, through the synergistic effect of a cooling load prediction model and a parameter optimization strategy, the system can dynamically match real-time load requirements, the model is combined with a convolutional neural network to extract local features and capture time correlation of a long-short-term memory network, and high-precision prediction of cooling loads in the next two hours is achieved; and compared with a traditional fixed parameter operation mode, invalid energy consumption of equipment such as a water pump and a fan is reduced, and energy conservation is achieved.

The invention relates to a central air conditioner energy consumption optimization control method and system, electronic equipment and a storage medium, and the method comprises the steps that characteristic parameters of load calculation are obtained, and according to the characteristic parameters, the cooling load requirements of a central air conditioner under different working conditions are calculated through a dynamic load calculation model; acquiring real-time operation data of the central air conditioner; establishing a digital simulation model of the central air conditioner based on the cooling load demand and the operation data, and calculating the energy consumption distribution of each device of the central air conditioner under different working conditions through the digital simulation model; with the overall energy efficiency optimization of the central air conditioner as the target, the optimal operation parameter combination of the central air conditioner is obtained through an intelligent optimization algorithm; and synchronously displaying the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executing an optimized operation strategy according to a user instruction based on the optimal operation parameter combination. According to the method, the accuracy of energy consumption calculation is improved, and the overall energy consumption of the central air conditioner is reduced.

The invention discloses a central air conditioner energy consumption big data intelligent analysis method based on data mining. The method comprises the following steps that historical data of central air conditioner energy consumption is collected; performing data preprocessing on historical data of the energy consumption data of the central air conditioner; establishing an analysis engine, namely establishing an energy consumption model and establishing a load prediction model; establishing a central air conditioner energy efficiency detection strategy model; establishing a fault prediction model; after energy consumption data of the central air conditioner are collected for data preprocessing, energy efficiency detection is conducted through the energy efficiency detection strategy model of the central air conditioner according to predicted energy consumption and load data output by an analysis engine, fault prediction is conducted through the fault prediction model, and finally the data are summarized to obtain an analysis report. Through data mining and intelligent analysis, the problem that in the prior art, data dimensions are complex is solved, and the problem of overfitting of the position where the central air conditioner is located due to building specificity is solved.

The invention relates to the field of heating ventilation air conditioner automatic control, and discloses an efficient central air conditioner cold station optimization control system and method based on physical AI. The system comprises a data acquisition module, an energy efficiency modeling module, a load prediction module, a rolling optimization module, an execution control module and a feedback correction module. The data acquisition module forms a time sequence data set; the energy efficiency modeling module adopts transfer learning to obtain a system energy efficiency model of a target domain cold station; the load prediction module outputs a predicted cold load sequence; the rolling optimization module is used for solving an optimal control sequence under the constraints of cooling capacity balance, an equipment operation boundary and a temperature difference threshold by taking a predicted cooling load sequence, a system energy efficiency model and a current equipment operation state as input under a model prediction control framework; the execution control module issues a first control action to control the water supply temperature, the water pump and fan frequency and unit start and stop; and the feedback correction module calculates a residual error and is used for closed-loop correction of the next period.

The invention discloses a fault early warning method and system for the equipment health state of a central air-conditioning system, and belongs to the technical field of central air-conditioning system operation monitoring. Multi-source operation state data in the central air-conditioning system are obtained, a state feature matrix is constructed, potential target feature points are extracted, and an equipment health state evolution path is constructed; performing multi-stage time sequence segmentation on the evolution path, identifying similar evolution trend fragments based on a density peak clustering algorithm, and classifying typical degradation modes; according to the evolution trend and the health scoring result, constructing a dynamic early warning threshold value; when the deviation trend between the monitored operation state characteristics and the early warning threshold value exceeds a set range, structured fault early warning information is output and synchronously fed back to a central air conditioner control system, and early intervention and maintenance scheduling are achieved; the method has high trend recognition capacity and prediction perspectiveness, and the accuracy of fault early warning and the intelligent operation level of the central air conditioning system are improved.

The invention discloses a refrigeration host parallel system prediction and energy efficiency control method and system, relates to the technical field of refrigeration system energy efficiency optimization control, and is used for solving the problem that energy efficiency optimization and load dynamic response are difficult to cooperate when a plurality of refrigeration hosts are in parallel operation under variable working conditions. According to the method, a working condition-load-energy efficiency mapping relation is established by constructing a refrigeration host three-dimensional energy efficiency mapping table, day-ahead and intra-day load prediction is executed in parallel based on historical data and environmental parameters, and a comprehensive load value is obtained through prediction error inverse proportion dynamic weighted fusion. And enumerating an optimal host operation combination and issuing a control instruction by taking the maximization of the comprehensive revenue function as a target, and updating prediction model parameters on line by utilizing a residual error of an actual load and a prediction value. The method is mainly used for energy efficiency optimization and self-adaptive control of a central air conditioning system, a ground source heat pump system or an industrial refrigerating unit group, the overall energy efficiency of the system can be effectively improved, and frequent start and stop are reduced.

The invention relates to the technical field of machine learning, in particular to a cooling pump optimization algorithm of a central air-conditioning water chilling unit based on a knowledge graph, which comprises the following steps of: acquiring an alignment relationship between a pump start-stop signal and current response time, extracting an action section and a path node sequence to construct a logic sequence, and identifying control signal alternation and direction deviation to extract a path limit number, and screening response delay and a load fluctuation section to correspondingly adjust an identification area. According to the method, behavior mapping between the pump action and the signal time sequence is constructed, alternating signals and direction deviation fragments in a control chain are extracted, a path limiting identifier is generated to be used for restraining control interference, a trigger area is regulated and controlled in combination with matching of load response and an equipment action section, and an accessible path is screened to replace a limited section; the path structure is recombined, instruction chains are sorted, the matching capability of path scheduling and the response collaboration of linkage nodes are enhanced, and the control process is promoted to be kept continuous and stable in a differentiated state.

The invention relates to an energy efficiency optimization method for a dynamic load of a central air conditioner, which comprises the following steps of: establishing a response relationship among a cold and heat source, a transmission and distribution system and end equipment, identifying a nonlinear decoupling characteristic of a cold and heat response behavior by monitoring operation deflection of each level of equipment under a low-load working condition, and defining and updating a cooling capacity response deviation threshold in real time; on the basis of the cooling capacity response deviation threshold, delay compression control is conducted on response actions of the refrigerant side and the air medium side, and by adjusting the air side response time sequence in advance, dynamic thermodynamic equilibrium is formed between refrigerant transfer delay and air medium response in advance; by establishing the response relationship among the cold and heat source, the transmission and distribution system and the end equipment and introducing a cold capacity response deviation threshold dynamic updating mechanism, nonlinear coupling mismatch in a cold and heat transmission chain can be accurately identified, so that the system is more flexible to operate under low-load and variable-load conditions, frequent start and stop or redundant operation of the cold and heat source is avoided, and the system reliability is improved. And therefore, the overall COP value of the system is remarkably increased, and unit cooling capacity consumption is reduced.

The invention discloses a commercial building electricity comprehensive energy-saving control device, and relates to the technical field of building energy management, the commercial building electricity comprehensive energy-saving control device comprises a comprehensive energy service cloud platform, an air conditioner energy router and an intelligent terminal, the comprehensive energy service cloud platform is in signal connection with the intelligent terminal through the air conditioner energy router; the integrated energy service cloud platform is communicated with the air conditioner energy router through the Ethernet, 4G and NBIOT; the air conditioner energy router is used for providing a traditional gateway data forwarding function and undertaking local data storage, fusion and terminal equipment management. From the perspective of energy efficiency, firstly, a perfect energy efficiency index of the central air-conditioning system is put forward, on this basis, a specific energy consumption optimization strategy is researched from links such as a cold and heat source system, a water system and a terminal system, and finally, from the whole building, operation mode adjustment based on self-learning optimization is achieved in combination with a load prediction model. And comprehensive energy-saving control of electricity utilization of commercial buildings is realized.

The invention discloses an optimal control method and system for a central air conditioner. The method comprises the steps that a starting instruction is responded; selecting an operation parameter combination from a preset database and sending a test request to the central air-conditioning system; energy efficiency detection results are received, wherein the energy efficiency detection results comprise energy consumption, equipment efficiency and user thermal comfort indexes; determining a next round of test combination based on the detection result and the mapping relation table; and selecting the combination with the highest energy efficiency from the tested combinations as a final result and configuring the final result to the system. Energy efficiency self-adaptive optimization is achieved by establishing the preset database and the mapping relation table in combination with a multi-round test mechanism, the test is intelligently guided according to the divided energy efficiency value interval, blind traversal is avoided, and the search efficiency is improved. A termination test strategy is set to effectively control a period and reduce interference, a load prediction model is introduced to make optimization more targeted, energy waste caused by lagging response is avoided, key parameters are collaboratively optimized, comfort is improved, and energy consumption is reduced.

The invention discloses an energy-saving central air conditioner fresh air system device, and relates to the technical field of air conditioner testing, the energy-saving central air conditioner fresh air system device comprises a base, the upper end of the base is provided with a supporting column, the upper end of the supporting column is provided with a sleeve, the inner wall of the sleeve is provided with an adjusting block, and one end of the adjusting block is provided with a fixing block; according to the energy-saving central air conditioner fresh air system device, the ball body is matched with the extension rods to drive the movable barrel to move, so that the limiting rods arranged on the two sides of the movable barrel are driven to move, the upper end bodies of the limiting rods are connected with the butt-joint blocks, and therefore the butt-joint blocks can be synchronously driven to adjust when the movable barrel moves, and the energy-saving central air conditioner fresh air system device is suitable for the current scene; the angle of the positioning assembly is accurately adjusted, so that the air outlet condition of the central air conditioner fresh air system is accurately monitored by the detection equipment installed on the positioning assembly, and whether the central air conditioner fresh air system can keep required temperature distribution uniformity under different flow conditions or not can be detected by monitoring the air flow of the central air conditioner fresh air system.

The invention provides an energy-saving central air conditioner, which belongs to the technical field of central air conditioners, and comprises an air conditioner body and a pipeline arranged at the lower end of the air conditioner body, and further comprises an air inlet adjusting assembly, an induction dust collecting assembly and a cleaning and collecting assembly, air is purified through electrostatic adsorption of the adsorption plate, dust accumulation and local blockage conditions are accurately detected through cooperation of an elastic part, a magnetic coating and a Hall sensor, the air inlet quality of the air conditioner and system operation are prevented from being affected by feedback signals, tiny impurities can be effectively captured by the adsorption plate, and the air cleanliness is improved; a wind power and knocking combined mode is adopted for dust removal, a filter screen plate and an adsorption plate are cleaned in all directions, and inlet air cleanliness is guaranteed; when the ash discharging port is not used for removing ash, the magnetic blocking plate blocks the ash discharging port to prevent leakage, and the pressing rod limits the position of the connecting plate to ensure the monitoring precision of the sensing rod, reduce part shaking, prevent dust from falling off in advance and ensure the adsorption effect; the baffle is connected with related parts to control the opening degree of the pipeline, dust is prevented from entering the wall during dust removal, and dust collection is guided.

The invention discloses a functional application microenvironment-based central air-conditioning terminal system transformation method and system, and the core innovation of the method is as follows: 1) dynamic microenvironment division: defining a minimum 4m < 2 > control unit according to the actual use demand, and breaking through the physical boundary limitation; (2) multi-source cold and heat collaboration: 15 types of equipment such as a central air conditioner, an independent air conditioner and an electric heater are compatible, and energy is dynamically distributed according to an energy efficiency-preference-electricity price model; and (3) the central air-conditioning microenvironment controller integrates the adaptive capability of day and garden hardware (TY-HY-A / B valves) and multiple protocols, and realizes the temperature difference control precision of + / -0.5 DEG C. The scheme is adaptive to the energy-saving transformation of existing buildings, especially the energy-saving transformation of central air-conditioning fan coils of hotel guest rooms, the energy-saving transformation of office buildings, the energy-saving transformation of data centers and other application scenes.

The invention provides a central air conditioner installation method and system based on an intelligent sensing mechanism, and the method mainly comprises an installation scheme set acquisition step, a heat source prediction model construction step, an installation scheme dynamic adjustment step and a scheme verification and calibration step. Cooperative optimization of a central air conditioner installation scheme on the aspects of thermal comfort, balance, energy consumption and response speed is achieved, and an efficient solution is provided for accurate deployment of an intelligent air conditioner system of a building.

The invention relates to the technical field of building power system energy saving optimization, and discloses a central air conditioner green power consumption optimization control system based on carbon footprint tracing. The system comprises a carbon footprint traceability data acquisition unit, a green power consumption priority evaluation unit, an energy scheduling optimization unit, a real-time load matching unit and a carbon footprint verification unit. The carbon footprint traceability data acquisition unit acquires related data; the green power consumption priority evaluation unit generates a consumption strategy sequence based on a dynamic game theory model; the energy scheduling optimization unit adopts a specific algorithm to construct an optimization model; the real-time load matching unit adjusts a matching relationship by using deep reinforcement learning; the carbon footprint verification unit verifies the carbon footprint based on a zero-knowledge proof algorithm. According to the method, optimal control over green power consumption of the central air conditioner is achieved, the energy utilization efficiency is improved, carbon emission is reduced, and carbon footprints are accurately managed.

The invention discloses a central air conditioner control system which comprises at least one outdoor unit and at least one indoor unit in communication connection with the outdoor units through communication buses. The controllers can communicate with one another and are respectively connected with the communication bus; the temperature and humidity sensor is used for collecting indoor temperature data and humidity data; wherein the controller is used for storing current regional information, weather information and equipment operation records; the controller is also used for establishing a thermal comfort evaluation / recommendation model and an intelligent prediction schedule model by using a machine learning algorithm; the controller is further used for forming recommended control parameters according to the set requirements of the user and sending the recommended control parameters to the outdoor unit or the indoor unit. According to the invention, based on the information of the area where the user is located, the weather information, the indoor environment information and the user information, a machine learning algorithm is utilized to establish a thermal comfort evaluation recommendation model and an intelligent prediction schedule model, the models are combined to form a recommendation control strategy according to a scene set by the user, and optimal control parameters are provided for the user. And in cooperation with the energy-saving effect of the central air conditioning equipment, the purpose of efficient and energy-saving operation is achieved on the premise that the comfort level of a user is met.

The invention discloses a central air conditioning system and an environment regulation and control method thereof. The method comprises the following steps: monitoring environmental data of a current actual scene in real time through a plurality of sensors, establishing a physical field model through a controller according to the environmental data and building topology data, and establishing a behavior occupancy field model according to user behavior data in the environment; generating a multi-target demand mapping operator based on the physical field model, and obtaining scene condition representation according to the multi-target demand mapping operator and the behavior occupancy field model; and generating the virtual sensing data for compensating the environment data based on the scene condition characterization. And compensating the environment data by using the virtual sensing data, and dynamically regulating and controlling the current actual scene according to the compensated environment data. The method is used for achieving the effects that the accuracy of virtual sensing data and the adaptability in a real scene are improved, and the regulation and control accuracy of the central air conditioning system on the building space environment is enhanced.

The invention discloses an air conditioning system control method and system based on reinforcement learning and a twinborn model strategy. The method comprises the steps that S1, a data-driven twinborn model of a central air conditioning system is constructed; s2, performing offline pre-training based on a virtual environment formed by a twin model; s3, performing online integration on a plurality of pre-trained reinforcement learning algorithms and the twin model to form a reinforcement learning strategy integration framework; and S4, the final control action is issued to execution equipment of the central air conditioning system. According to the invention, by constructing the serial data-driven twinborn model, the running state, energy consumption and cooling and heating load supply-demand relationship of the central air-conditioning system are accurately simulated, a high-fidelity virtual environment is provided for various reinforcement learning algorithms, offline pre-training is realized, an initial control strategy is generated, and risks caused by blind exploration in a real system are avoided. Continuous self-adaptive optimization is achieved, and the stability and the energy efficiency level of intelligent control of the air conditioning system are remarkably improved.