7388 results about "Energy conservation" patented technology

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

The embodiment of the invention relates to the technical field of computers, in particular to an energy conservation and emission reduction scheme generation method and system based on a digital factory, and the method comprises the steps: obtaining real-time energy consumption data streams and emission monitoring signals of a plurality of pieces of production equipment in the digital factory; extracting dynamic energy consumption characteristics from the real-time energy consumption data stream, and identifying periodic emission parameters from the emission monitoring signal; a target energy consumption prediction algorithm is called to process the dynamic energy consumption characteristics and the periodic emission parameters, and energy consumption prediction results and emission intensity distribution of multiple time periods are generated; and generating an equipment-level optimization regulation and control scheme and a production line-level scheduling strategy according to the energy consumption prediction result and the emission intensity distribution in combination with preset energy conservation and emission reduction constraint conditions. Therefore, collaborative optimization of equipment-level real-time regulation and control and production line-level flexible scheduling can be realized.

The invention relates to the technical field of power distribution cabinet temperature control, in particular to an intelligent temperature and humidity control system for a power distribution cabinet. Comprising an environmental parameter acquisition module, a data analysis processing module, a temperature and humidity regulation and control module, an energy consumption recovery optimization module and a monitoring alarm module. The environmental parameter acquisition module monitors environmental data such as temperature, humidity and voltage through a sensor, and the data analysis processing module analyzes a trend based on a time convolutional neural network. The temperature and humidity regulation and control module achieves optimal control through an optimization algorithm, and the energy consumption recovery optimization module recovers and optimizes energy through a genetic algorithm. And the monitoring alarm module adopts a Bayesian network to be combined with a self-adaptive sliding window mechanism to monitor environmental fluctuation in real time, and gives an alarm when the environmental fluctuation is abnormal. Efficient operation, safety and stability of the power distribution cabinet in a complex environment are ensured, the safety and reliability of the power distribution cabinet are improved, the operation and maintenance cost can be effectively reduced, and energy conservation and environmental protection are achieved.

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

The invention discloses a BIM (Building Information Modeling)-based hoisting construction safety monitoring and early warning system. The system comprises a terminal sensing layer which is used for collecting environmental parameters and personnel behavior data in a closed space in real time; the edge computing layer is used for carrying out cleaning, compression and encrypted transmission on original data by utilizing an explosion-proof edge computing gateway; the cloud collaboration layer is used for storing full data based on a BIM digital twinborn platform, constructing a'danger mode-construction feature-disposal measure 'three-dimensional meta-knowledge graph by adopting an MAML + + algorithm, meanwhile, coupling a physical mechanism data enhancement engine with a multi-physics field coupling model and a physical constraint generative adversarial network, generating virtual data conforming to mass conservation and energy conservation, and sending the virtual data to the cloud collaboration layer; performing mixed training with real data; according to the intelligent decision-making layer, a space-time adaptive threshold evolutionary algorithm encodes a space-time context through a graph attention network and Transform, an alarm threshold is dynamically optimized through deep reinforcement learning, meanwhile, a digital twin deduction engine calculates a shortest safety path in real time, and rescue resource allocation is optimized.

The invention belongs to the field of wind power plant grid-connected control, particularly relates to a wind power plant active power efficient distribution method considering the fatigue damage degree of fans, and aims to solve the problem that scheduling instruction power is uniformly distributed to all fans in the prior art. And the fan fault probability is increased due to the fact that the influence of power adjustment on fan fatigue damage is not considered. The method comprises the steps of fan load estimation based on stress analysis and energy conservation, low-complexity fatigue damage quantification based on an improved energy method, and construction of a multi-target optimization model with the minimum total fatigue load of a wind power plant and the most uniform fatigue load distribution between fans as targets based on a Gulin method, an entropy weight method and an improved matter element extension model. According to the method, rapid and accurate power distribution is realized, so that the fan runs in a proper state, the accumulation of fatigue damage and the risk of failure occurrence are reduced, and the overall running efficiency and economic benefit of a wind power plant are improved.

The invention discloses an energy equipment carbon emission scheduling optimization method in an integrated energy system, and the method comprises the steps: determining an energy equipment carbon emission factor, constructing a carbon emission cost function, employing Monte Carlo simulation to generate a carbon transaction price fluctuation scene, and carrying out the carbon transaction price fluctuation scene. The method comprises the following steps: calculating a carbon transaction risk index of equipment in each scene according to a cost function, determining start-stop time by economic benefit optimization, constructing a comprehensive energy system topology network structure, updating a carbon flow path set according to the start-stop time, calculating a comprehensive carbon emission coefficient, establishing an energy flow and carbon flow coupling model between a high emission path and a low emission path, and constructing a comprehensive energy system topology network structure. A scheduling strategy feasible space is obtained through a cooperation strategy, and an optimal cooperation scheduling operation strategy is obtained through optimization of a particle swarm algorithm by taking the minimum carbon emission as a target. The method can effectively reduce the carbon emission / optimize the start and stop of the equipment, improves the economical efficiency and environmental protection property of system operation, achieves the dual goals of energy conservation and emission reduction and benefit improvement, and has good interpretability at the same time.

The invention discloses an electric power and electric quantity balancing method and system, and relates to the technical field of electric power management. S2, demand prediction; s3, evaluating the power supply capability; s4, carrying out resource optimization configuration; s5, integrating renewable energy sources; s6, demand side management; s7, performing real-time monitoring and adjustment; and S8, performing performance evaluation and feedback, and constructing an electric power and electric quantity balance system based on the method. According to the electric power and electric quantity balancing method and system, the accuracy and efficiency of electric power and electric quantity balancing can be remarkably improved, various electric power demand changes can be effectively handled, and stable operation of a power grid is ensured. Meanwhile, by dynamically adjusting access and demand side management of renewable energy sources, the purposes of maximum utilization of energy sources, energy conservation and emission reduction are achieved. Finally, through regular performance evaluation and feedback, the system performance is continuously optimized, a closed-loop optimization process is formed, and powerful support is provided for sustainable development of a power system.

The invention discloses tempered low-emissivity coated glass, which comprises a tempered glass substrate and a composite metal film layer laminated on the substrate, and is tempered coated glass with three metal silver film layers. After a common glass substrate is plated with a composite metal film layer, toughening treatment is carried out, and after toughening treatment, all the metal film layers reflect, interfere and refract light, so that the glass is light blue in the sun, and the decoration and energy-saving effects are good; the visible light transmittance is high, the outdoor visible light reflectivity is low, the solar energy transmittance is low, and the solar energy reflectivity is high; the prepared hollow glass has better energy-saving effects of light control, heat insulation, heat preservation and the like, enables buildings to be closer to nature, is used for near-zero-energy-consumption buildings, enables the buildings to meet the requirements of the national standard GB55015-2021 General Standard for Energy conservation and Renewable Energy Utilization of Building, and has great contribution and significance on carbon neutralization and carbon peak reaching of the buildings.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention relates to a rail transit energy efficiency balance dynamic monitoring method and system, relates to the field of rail transit energy conservation and monitoring, and solves the problems that in an energy consumption monitoring link, simple statistical analysis and a rule-based system are difficult to adapt to a complex and changeable operating environment due to fixed threshold values and rules, and misjudgment and missed judgment are easy to occur. The method comprises the following steps: according to a comparison result of actual energy consumption of equipment and a personalized energy efficiency standard threshold value, determining whether the corresponding equipment has energy consumption abnormity or not; if yes, the personalized threshold value and historical energy consumption data of the equipment are combined, the built comprehensive energy loss analysis model based on the fault tree and the Bayesian network is utilized, the fault mode of the equipment is started, the operation history, the maintenance record and the environmental factors of the equipment are combined, and Bayesian network reasoning and dynamic fault tree analysis are carried out; and the source reason of the energy loss is positioned and sent to the terminal held by the person in charge. The method has the following effect that the energy efficiency management level of rail transit is effectively improved.

The invention relates to a coal conveying control system and method for a thermal power plant. The method comprises the following steps that S1, operation data of a coal conveying system are collected in real time through a preset multi-source sensor network; s2, constructing a time sequence prediction model based on an attention mechanism, and dynamically predicting the operation state of the coal conveying system; s3, executing real-time optimization calculation at the edge calculation node, and generating a control instruction set including the belt speed, the coal feeder opening degree and the transfer station cooperation strategy; s4, the control instruction is issued to an execution mechanism through a 5G-MEC network, and meanwhile key data is uploaded to a cloud end for model iteration updating; and S5, dynamically adjusting a safety protection threshold value based on an equipment vibration spectrum analysis result, and triggering a hierarchical protection mechanism when a coal blockage risk is detected. The method has the effects of effectively improving the performance of the coal conveying system and being green and energy-saving.

The invention provides a computing-electric-thermal coupled data center integrated energy system collaborative optimization method, and relates to the field of data center energy-saving management and control, and the method comprises the steps: firstly obtaining real-time data collected by a data center; secondly, a data center load model is constructed based on the relation among real-time data quantification calculation power, electric power and heating power; constructing a collaborative optimization model according to the data center load model, and solving the collaborative optimization model to obtain an optimization result; and finally, adjusting calculation task distribution, a power supply strategy and cooling system operation parameters according to an optimization result to form a calculation-electricity-heat coupled data center integrated energy system collaborative optimization strategy. According to the calculation-electricity-heat coupled data center comprehensive energy system collaborative optimization method, collaborative optimization is carried out on the data center energy system through multi-energy complementation, and energy conservation and efficiency improvement of the data center are achieved.

The invention relates to the technical field of dust collector control, and discloses a multi-motor cooperative speed regulation and energy efficiency optimization control method and device for a dust collector, and the method comprises the steps: monitoring the rotating speed, current, voltage, temperature and other operation parameters of a plurality of motors in real time, and determining the working load state of each motor through a load recognition model. Different cooperative speed regulation strategies are matched according to load states, such as PID control for low loads, fuzzy logic control for medium loads and model prediction control for high loads. A dynamic weight distribution algorithm is used for adjusting the target rotating speed, and an energy efficiency optimization algorithm is used for optimizing power distribution. Meanwhile, environmental parameters are considered, and control parameters are optimized through a self-adaptive parameter adjustment model. And the system also has the functions of adjusting strategies according to load fluctuation and power consumption, motor priority ranking during high load, energy efficiency algorithm parameter optimization, rotating speed adjustment during filter screen blockage, motor state monitoring and prediction and the like, so that accurate speed regulation and efficient energy conservation are realized, and the performance and the intelligent level of the dust collector are improved.

The invention relates to the field of multi-energy complementary clean energy systems, in particular to a water-wind-light-storage multi-energy complementary clean energy base capacity configuration method and system, and the method comprises the following steps: collecting the related basic data of a hydropower station, wind power, photovoltaic, pumped storage and power transmission lines in a research area; analyzing and determining the wind and light electricity abandoning rate, the annual utilization hours of the power transmission channel comprehensive internet access channel, the comprehensive utilization demand of the reservoir and the pumping power limitation of the pumped storage power station; constructing a water-wind-light-storage multi-energy complementary clean energy base capacity configuration double-layer nested optimization model; and obtaining the optimal capacity configuration scheme of the clean energy base and the output process of each power supply under the capacity configuration scheme. Technical support can be provided for planning, development and construction of a watershed or regional water-wind-light-storage clean energy base, the wind-light-electricity absorption capacity of a hydropower station and a pumped storage power station is effectively improved, the wind-light-abandoning rate of a power grid is reduced, safe and stable operation of the power grid is guaranteed, and the energy conservation and emission reduction target of China is smoothly achieved.

The invention provides an injection molding machine mold opening and closing parameter optimization method and system oriented to energy conservation and efficiency improvement, and relates to the technical field of industrial process intelligent control. The method comprises the steps that mold temperature, hydraulic pressure and vibration signals are collected in real time through a multi-source sensor, and a composite optimization target of a dynamic energy consumption coefficient and a process stability index is constructed; an improved multi-target genetic algorithm is adopted to iteratively generate a candidate parameter set meeting energy consumption and stability constraints, and the algorithm fuses hydraulic system safety boundary dynamic constraints and an adaptive variation strategy; a parameter adjustment effect is rehearsed through a digital twin model, and motion track deviation is calibrated online in combination with a recursive least square method, so that parameter dynamic injection is realized; and triggering abnormal working condition recovery and material self-adaptive optimization based on a closed-loop feedback mechanism. The system comprises a multi-source data acquisition module, a composite optimization calculation module, a dynamic parameter injection module and a closed-loop feedback control module, and the real-time performance and generalization ability are improved through an FPGA hardware acceleration optimization engine and a transfer learning algorithm.

An automatic control method and system for an electrostatic precipitation system. The method combines a neural network control algorithm with a PID regulation control algorithm, and uses the neural network control algorithm to carry out preprocessing on the control of the electrostatic precipitation system and uses the PID regulation control algorithm to carry out initialization processing when the electrostatic precipitation system is out of control, thereby avoiding the uncontrollable state of the electrostatic precipitation system caused by PID control overshoot or oscillation; moreover, the PID regulation control algorithm is used to carry out real-time tracking regulation on the electrostatic precipitation system, so that the real-time response speed of intelligent control of the electrostatic precipitation system can be effectively increased. Therefore, the energy conservation and efficiency improvement of the electrostatic precipitation system are better realized while the operation stability and response rapidness of an electric precipitator are ensured and controlled. By combining the two control algorithms, once intelligent control is used, the automatic control method effectively prevents the electrostatic precipitation system from being temporarily out of control due to excessive overshoot, thereby ensuring the stable operation of the electrostatic precipitation system, and implementing intelligent control.

The invention relates to the technical field of communication machine room energy conservation, in particular to a temperature energy-saving control method and system for a communication machine room, and the method comprises the steps: obtaining multi-region temperature and humidity and equipment load data in the machine room, inputting a pre-training model to obtain a cabinet heat dissipation risk level, judging an environment state through combination with an incidence matrix, entering an operation mode if a requirement is satisfied, and entering a control mode if a requirement is satisfied. If not, a local temperature control adjusting mechanism is triggered; the method comprises the following steps: calculating a refrigeration efficiency ratio according to the air volume of ventilation equipment, air conditioner power consumption, real-time electricity price and the like, generating a candidate scheme set by using a dynamic weight multi-target optimization algorithm in combination with factors such as equipment life, and selecting a target scheme through virtual verification; and according to air conditioner waste heat and cabinet heat dissipation and storage heat, a heat energy distribution strategy is dynamically generated by using a deep Q network reinforcement learning algorithm. Therefore, the problems that in the prior art, energy is wasted, cost is high, heat dissipation requirements of all areas are difficult to accurately adapt, the service life and stability of equipment are affected, and fault risks are increased are solved.

The invention relates to the technical field of smart communities, and discloses a building low-carbon operation management method based on data analysis, which comprises the following steps: acquiring building data, preprocessing the data to obtain a multi-dimensional data set, performing feature extraction by adopting a principal component analysis method aiming at the multi-dimensional data set, and calculating a feature value and a feature vector of a covariance matrix to obtain a building low-carbon operation management result. Selecting the first k principal components with relatively large feature values as new feature spaces to obtain a feature data set after dimension reduction, taking the feature data set after dimension reduction as input, constructing a credit rating prediction model by using a random forest algorithm, calculating importance scores of all features to rating results, and sorting the results according to the importance scores of the features to obtain a credit rating result. And selecting the first n features with higher scores as key feature factors, and constructing a credit evaluation feature vector. According to the method, a more accurate and comprehensive evaluation result is provided for building credit rating, and green building development and realization of energy conservation and emission reduction targets are effectively supported.

The invention relates to the technical field of boiler engineering and artificial intelligence modeling, in particular to a coal-fired boiler biomass blending combustion working condition intelligent optimization method, which comprises the following steps: constructing a three-dimensional mathematical model of coal-fired boiler biomass blending combustion based on experimental test data; performing multi-working-condition numerical simulation according to boiler combustion working condition fluctuation in the three-dimensional mathematical model; according to the numerical simulation data, constructing a time-space coupling model for co-combustion of biomass in the coal-fired boiler; the modeling accuracy is improved through a numerical simulation technology, the boiler operation condition is dynamically optimized in combination with space-time modeling, and theoretical support is provided for energy conservation and emission reduction and blending combustion proportion optimization; the method is suitable for environment-friendly transformation and operation optimization of the coal-fired boiler, and carbon emission and pollutant emission can be effectively reduced.

The invention relates to the technical field of computing power resource scheduling, and particularly discloses a green data center computing power demand prediction and energy consumption control method, system and device. Firstly, computing power information and environmental parameters of a data center are collected in real time based on edge equipment; afterwards, a time sequence trend is predicted by using an LSTM neural network, a CNN network is fused to extract computing power features, an LSTM-CNN hybrid model is constructed, repeated training is performed through an Adam optimization algorithm, and the model can accurately pre-judge a future computing power demand; and in combination with analysis of historical energy consumption and real-time monitoring data, an energy consumption analysis model is established to evaluate power consumption conditions under different computing power requirements. Based on the prediction result and the energy consumption analysis, the working state of the server cluster is dynamically adjusted, the load is scheduled, the energy use is optimized, the energy efficiency ratio of the data center is continuously improved by verifying and adjusting the energy consumption strategy, the overall operation cost and the energy consumption are remarkably reduced, and the development requirements of green energy conservation are met.

The invention relates to the technical field of energy and energy conservation, in particular to a ventilation air conditioner control method based on load prediction. The technical problems that in the prior art, a ventilation air conditioner control method has certain hysteresis quality, advanced adjustment cannot be achieved, and autonomous adjustment and control cannot be conducted on the occasion with large flow are solved. According to the technical scheme, the ventilation air conditioner control method based on load prediction comprises the steps of analyzing the relation between the operation parameters of each component device of an air conditioner system and the building cooling load according to a model prediction result, fitting the change of the relation into a curve, and compiling an operation control scheme under typical working conditions according to the fitted relation curve. The building cooling load prediction value within a period of time can be obtained according to the algorithm, the system formulates the air conditioner system control strategy according to the prediction value, the problems of unreasonable building ventilation air conditioner operation mode and control lag can be solved, the operation energy efficiency of the air conditioner system is improved, on-demand cooling is effectively achieved, and the energy consumption is reduced. And intelligent building operation management is powerfully supported.

The invention discloses a preparation method of tempered low-emissivity coated glass. The preparation method comprises the following steps: sintering a target material, carrying out moisture removal and degassing pretreatment on glass, carrying out coating treatment and tempering treatment, and laminating a composite metal film layer on a tempered glass substrate to prepare the tempered coated glass with three metal silver film layers. According to the method, after a common glass substrate is plated with the composite metal film layers, toughening treatment is carried out, and after toughening treatment, the metal film layers reflect, interfere and refract light, so that the glass is light blue in the sun, and the decoration and energy-saving effects are good; the visible light transmittance is high, the outdoor visible light reflectivity is low, the solar energy transmittance is low, and the solar energy reflectivity is high; the prepared hollow glass has better energy-saving effects of light control, heat insulation, heat preservation and the like, enables buildings to be closer to nature, is used for near-zero-energy-consumption buildings, enables the buildings to meet the requirements of the national standard General Standard for Energy conservation and Renewable Energy Utilization of Building, and has great contribution and significance on carbon neutralization and carbon peak reaching of the buildings.

The invention relates to an aluminum profile extrusion process energy consumption optimization method based on digital twinning, and the method comprises the steps: building a digital twinning model of an aluminum profile extrusion process, setting the range of extrusion process parameters, importing preset process parameters into the model for simulation, and extracting a quality index and an energy consumption index. The process parameters are optimized through a multi-objective optimization algorithm; the optimized parameters are imported into the model for simulation again, and results before and after optimization are compared to judge whether optimization is achieved or not. If the product quality is qualified and the energy consumption is optimal, applying optimization parameters to actual production; otherwise, continuing to optimize until the preset requirement is met. Through a digital twinning means, the strength and the workload of finding the optimal energy consumption process parameters in the aluminum profile extrusion process only through repeated manual physical experiments are reduced, energy conservation and emission reduction in the aluminum profile extrusion process are achieved, and meanwhile the production efficiency of an enterprise is improved.

The invention discloses an energy consumption optimization control method and system for heating and ventilation equipment, and the method comprises the steps: carrying out the comprehensive evaluation of the energy consumption condition of the heating and ventilation equipment, and recognizing the current energy consumption condition and energy-saving potential of the equipment; the system is connected with heating and ventilation equipment through the Internet of Things technology, and collects parameters of the heating and ventilation equipment in real time. Based on the preprocessed data, establishing energy consumption models of the water chilling unit, the cooling tower, the freezing pump and the cooling pump; building an energy consumption prediction regression model by adopting a decision tree algorithm, and predicting the total energy consumption of the heating and ventilation system in real time; according to the predicted energy consumption data, an optimization control strategy of the heating and ventilation equipment is formulated in combination with a set energy consumption control objective function; and applying the formulated optimization control strategy to the heating and ventilation equipment, carrying out real-time monitoring and feedback, and carrying out iterative optimization on the control strategy according to an actual effect. According to the method, comprehensive evaluation, real-time monitoring and optimal control of the energy consumption of the heating and ventilation equipment can be realized, and a powerful guarantee is provided for energy conservation and emission reduction of a building.

The invention relates to a method, device and equipment for controlling energy conservation of a clean room with an AI algorithm, and the method comprises the steps: collecting real-time environment parameters and equipment power consumption data through a temperature and humidity sensor, a cleanliness monitor, a dust falling particle catcher and an intelligent electric meter which are disposed in the clean room; associating the real-time environment parameters and the equipment electricity consumption data with production scheduling information to generate a dynamic regulation and control strategy; based on the real-time environment parameters, the equipment electricity consumption data, the dynamic regulation and control strategy and historical energy consumption data, a start-stop strategy of the fan filter unit is calculated through a deep learning model, and the start-stop strategy comprises a three-gear hierarchical regulation and control strategy for an alternating current type FFU. And the voltage and the rotating speed of the fan FFU are dynamically adjusted according to the direct current type FFU, so that intelligent linkage of production requirements and environment control is achieved, and the purposes of reducing cost and increasing efficiency are achieved.

The invention belongs to the cross technical field of the fusion graph attention network and the ship resistance field, and discloses a ship resistance intelligent forecasting method and system, and the method comprises the steps: constructing nodes and edges in a graph attention network, and carrying out the preprocessing of data; designing and improving a loss function; a dynamic weight multi-attention mechanism is established, and attention weights are automatically distributed according to different ship type features; applying a training strategy of an Adam and SGD combined optimizer to obtain a graph attention network model; and carrying out ship resistance prediction evaluation through the dynamic weight map attention network model. By introducing the ship resistance intelligent forecasting method based on the dynamic weight map attention network and the physical constraint, the ship resistance intelligent forecasting method has the advantages of simple structure and clear algorithm, is specially designed for forecasting resistance under different ship types and solving the matching problem of propellers and power devices, shows remarkable innovativeness and practical value, and is suitable for popularization and application. Powerful support is provided for optimization design, energy conservation and emission reduction and safe navigation of the ship.

The invention relates to the field of energy conservation and environmental protection, in particular to an intelligent circulating fluidized bed method desulfurization and carbon-pollution deep purification system, which constructs a high-reliability and explainable accurate prediction model for the concentration of various pollutants through a modeling method cooperatively driven by knowledge and data in a multi-pollutant generation-removal process, predicts the change of the concentration of the pollutants 90 seconds ahead of time, and improves the efficiency of desulfurization and carbon-pollution deep purification. The average relative percentage error is smaller than 2%, and the problems of control lag and difficulty in real-time accurate optimization caused by data measurement delay are solved; an intelligent regulation and control technology of slaked lime feeder frequency / bed pressure / water spraying amount multi-parameter collaborative optimization-model predictive control double-layer structure coupling is established, and while full-time stable ultra-low emission is achieved, outlet pollutant concentration fluctuation is reduced by more than 50%, outlet temperature fluctuation is within + / -2 DEG C, bed pressure drop fluctuation is within + / -75 Pa, slaked lime consumption is reduced by more than 10%, and full-time stable ultra-low emission is achieved. The water consumption is reduced by 5% or above, CO2 emission is reduced by 6% or above, and the problems of efficient and stable removal and synergistic carbon reduction of multiple pollutants in flue gas under complex working conditions are solved.

The invention discloses a carbon emission intelligent prediction system based on a neural network, and relates to the technical field of carbon emission prediction. The system obtains historical energy consumption and carbon emission data of a target enterprise, performs feature extraction and dimension reduction processing, and constructs an energy consumption data set and a carbon emission data set. A complex nonlinear relation between energy consumption data and carbon emission data is captured through a Siamese network, potential features are extracted through a double-branch structure, and an association degree matrix and nonlinear features are output. And performing time sequence modeling on the correlation degree matrix and the nonlinear characteristics through a long short-term memory network, capturing a time sequence dependency relationship between carbon emission and energy consumption, and predicting a future carbon emission value of the target enterprise. Through a gradient descent algorithm, according to an error between a predicted value and an actual value, a parameter weight of the model is optimized. Accurate carbon emission prediction is provided for a target enterprise, and the enterprise is assisted to make a more scientific decision in the aspects of carbon emission management, energy conservation and consumption reduction.