1613 results about "Central air conditioning" patented technology

The invention discloses a central air-conditioning system optimization control method oriented to building load prediction, which comprises the following steps: building a building load prediction model based on a long short-term memory network, combining real-time acquired building electrical load, indoor and outdoor temperature, humidity and equipment operation power data to accurately predict building cooling load requirements in a future time period, and on this basis, optimizing a central air-conditioning system. According to the method, time-of-use electricity price information is used for dynamically adjusting a cooling capacity distribution strategy of the central air conditioning system, so that the energy consumption and the operation cost of the system are reduced to the maximum extent while the load requirement is met. Compared with the prior art, the method has the advantages that the problems of insufficient load fluctuation response and insufficient energy consumption optimization are effectively solved, and the operation efficiency and economical efficiency of the central air-conditioning system of the building can be remarkably improved.

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 discloses a central air conditioner energy-saving optimization system based on load prediction, and relates to the technical field of air conditioner energy saving.According to the central air conditioner energy-saving optimization system based on load prediction, a VMDTransformerGRU hybrid deep learning model is introduced, through multi-source data fusion, the precision of cold load prediction is remarkably improved, and the prediction model can recognize the load change trend in advance. According to the improvement, the dynamic response capacity of the system is effectively enhanced, pre-regulation and control can be carried out before load changes, energy waste is reduced, a water chilling unit, a cooling tower and a pump are modeled to be independent intelligent agents, multi-agent deep reinforcement learning (MADRL) is adopted, and all the intelligent agents can autonomously learn and optimize control strategies of the intelligent agents. The intelligent agents cooperate with one another, efficient operation of the whole system is achieved through autonomous decision, and operation conflicts between devices are avoided. In this way, dynamic coordination and joint optimization among all the devices are achieved, invalid energy consumption is reduced, and the overall energy-saving effect and operation stability of the system are remarkably 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 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.

A central air-conditioning system energy consumption simulation method based on a multi-region coupling model comprises the steps that basic data and initial parameters of a cold-chain logistics center site are obtained, and the basic data and the initial parameters are preprocessed; and constructing a multi-region coupling physical mechanism model based on a heat and mass transfer equation, and outputting a physical model state through a finite difference method by taking the basic data and the initial parameters as boundary conditions. And taking the physical model state as the input of a convolutional neural network, extracting short-period penetration load characteristics through time sequence convolution, and outputting a short-period penetration load prediction result. And combining the short-term penetration load prediction result with the cargo thermal parameters and the partition state as the input of a Transform model, and outputting multi-region energy consumption distribution prediction data. And performing strategy optimization processing on the multi-region energy consumption distribution prediction data through MPC, and calculating a preliminary control strategy. And searching a global optimal solution in the high-dimensional nonlinear parameter space through a myxomycete optimization algorithm, and outputting an optimal control strategy.

The invention discloses a control method and device for a fusion scheduling efficient refrigerating machine room system and a storage medium, and relates to the technical field of central air conditioner control. The method comprises the steps that operation data of all edge end devices and sensor data in all areas are obtained; calculating the energy efficiency index of each subsystem based on the operation data, the distribution weight of the subsystem corresponding to each equipment type, the comfort coefficient associated with each region and the sensor data in each region; according to the energy efficiency index, a control strategy of each subsystem is formulated in combination with the cooling capacity prediction data associated with each region; and performing reverse decomposition on the control strategy to obtain control parameters of the edge end equipment in each region, and issuing the control parameters to the edge end equipment in the corresponding region. The technical defect that when the cold source machine room is dispatched, the refrigeration effect of the central air conditioner is poor is overcome, and the overall energy utilization rate and the refrigeration effect are improved.

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 relates to the technical field of air conditioner energy-saving systems, and discloses a central air conditioner energy-saving automatic control system and method.The central air conditioner energy-saving automatic control system comprises a sensor module, a control module, a communication module, an intelligent control algorithm module, a dynamic adjusting module and an energy-saving optimization module; the sensor module comprises a temperature sensor, a humidity sensor, a CO2 sensor, an illumination sensor and an occupation sensor, the temperature sensor is used for monitoring indoor and outdoor temperatures and providing real-time data for decision making of a control system, and an NTC (negative temperature coefficient thermistor), a PTC (positive temperature coefficient thermistor) or a DS18B20 digital temperature sensor is adopted. The environment parameters are monitored in real time, the operation state of the air conditioning equipment is dynamically adjusted, the high-energy-consumption operation mode of a traditional air conditioning system is avoided, cooling / heating according to needs is achieved, energy consumption is effectively reduced, the mode is automatically switched to the energy-saving mode in the state that the number of personnel activities is small or no one exists, and therefore the effect of remarkably reducing the operation load and energy consumption can be achieved.

The invention discloses a water-cooling central air conditioner load management and control method based on artificial intelligence, and relates to the technical field of load management and control. Load prediction is carried out by combining a long and short-term memory time sequence model and a physical constraint neural network in a water chilling unit load prediction link and utilizing historical data; errors are corrected through an energy balance equation of the water chilling unit, and the prediction accuracy is improved; multi-objective optimization control is adopted, COP maximization of a water chilling unit, energy consumption minimization of a cooling tower fan and cooling water supply temperature stability serve as optimization objectives, environment temperature and humidity prediction is combined, and a cooling water supply temperature set point and a cooling tower fan start-stop strategy are dynamically calculated; based on a cooling water supply temperature set point and a fan start-stop strategy, a water chilling unit, a cooling tower fan and a water pump are dynamically adjusted, and the high-frequency operation phenomenon is reduced through fan start-stop optimization.

According to the non-intrusive identification method suitable for the operation load of the central air conditioner, the central air conditioners of different brands are selected, electrical waveforms of the complete operation process of the central air conditioners are recorded, time-frequency domain characteristic analysis is carried out, common characteristics are extracted, and total daily electrical load data and daily air temperature data of a user in the past one year are selected; analyzing the incidence relation between the daily electrical load curve and the daily temperature curve of the user, obtaining a heating load area, a basic load area and a cooling load area, performing linear data fitting with the temperature to obtain a load curve, and subtracting the basic load curve from the cooling load curve to obtain a load curve; further judging whether the central air conditioner is in a starting / stopping state or not, and obtaining the electric quantity of the central air conditioner equipment in the operation period through steady-state power integration by adopting sub-item electric quantity estimation in the starting event and stopping event period of the user air conditioner; and running state tracking and electricity consumption estimation of the central air conditioner are realized.

The invention discloses a marine central air conditioner health management and fault diagnosis system, which relates to the technical field of air conditioner health management and comprises a sensor module, a data acquisition module, a health management module, a fault diagnosis module, a user interface management module, a communication module and a maintenance management module. The sensor module is used for being installed on all devices and pipelines of the central air conditioner through sensors and detecting state parameters of the central air conditioner. The data acquisition module receives analog quantity signals of various sensors. Through cooperative work of various sensors and various functional modules, various parameters of the central air conditioner can be comprehensively monitored, faults including state monitoring and fault diagnosis of key parts such as a compressor, a refrigerant water system and a cooling water system can be accurately diagnosed, the equipment state can be monitored in real time in cooperation with the health management module, and the health of the central air conditioner can be improved. And reasonable maintenance suggestions and decisions can be provided through data analysis and evaluation to prevent faults in advance, so that the reliability and the service life of the equipment are 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 provides an energy-saving evaluation and operation strategy optimization method for a central air-conditioning system. The method comprises the following steps: firstly, building an Internet of Things platform based on the central air-conditioning system, and monitoring and collecting operation parameters of various equipment in the system in real time; unifying formats and preprocessing operation parameters, uploading the operation parameters to a cloud database for storage, and dividing the operation parameters according to categories; performing mapping modeling on the central air-conditioning system in the energy analysis platform, and performing parameter configuration at the same time; then, the divided data in the cloud database are connected according to evaluation categories, and meanwhile, an extensible formula expert database is introduced to evaluate the running state of the central air conditioning system based on an empirical algorithm, and abnormal strategy reminding is conducted; and prompting to adjust the running state of the equipment according to the strategy. According to the method, the problem of strategy missing is effectively solved through an optimization strategy of the extensible expert experience library; and through analysis and verification of seven functions, integrated, refined and systematic analysis of energy consumption and optimization strategy improvement and advanced management are realized.

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 a real-time optimization control method for a central air-conditioning system based on an agent model. The machine learning algorithm is adopted to construct the agent model used for predicting the optimal control strategy result, the real-time operation condition is input into the agent model in the actual application process, the optimal control strategy result under the operation condition is predicted, and compared with a traditional control method based on model optimization, the method has the advantages that the operation efficiency is improved, and the cost is reduced. According to the method, the calculation complexity is reduced, the calculation efficiency of the optimal control strategy is remarkably improved, and the real-time optimization control requirement of the central air conditioning system is met. The mathematical optimization model of the central air-conditioning system is established based on the equipment composition and the topological structure of the target central air-conditioning system as well as the optimization target and the constraint condition during model training, so that a large number of optimal control strategies corresponding to the operation condition sample are generated through the randomly generated operation condition; and forming a training data set for agent model training, so that the trained agent model can accurately predict an optimal control strategy.

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 relates to a final target temperature-based large central air conditioner multi-model optimization control method, which relates to the technical field of air conditioner system control, and comprises the following steps: S1, locally constructing physical models of an actual building and a central air conditioner; s2, establishing a corresponding control strategy designed for each scene in a multi-scene simulation environment; s3, acquiring current environmental parameters of the actual building, and inputting the parameters into the physical model; s4, a real-time control feedback mechanism is established, and the current indoor temperature and the current external environment change are continuously obtained in each time step length; s5, inputting the output data of the central air conditioner and the environmental parameters into the actual building physical model to predict the temperature in the actual building; and S6, all the models are iterated through an optimization algorithm, the working efficiency of the central air conditioner can be remarkably improved through an optimization control strategy, meanwhile, a real-time feedback mechanism can quickly respond to environment changes, and it is ensured that the indoor temperature meets the comfort standard all the time.

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.