3485 results about "Energy conservation" patented technology

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 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 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.

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 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 discloses an air conditioner energy-saving control method based on user behavior prediction, and belongs to the field of air conditioner intelligent control. According to the method, the position information, the activity state and the environment parameters of a user are collected in real time through a multi-mode sensor, the behavior and the space occupation state of the user are intelligently predicted through spatial-temporal feature extraction and a graph neural network model, and the use requirements of the air conditioner are accurately mastered. And the system dynamically adjusts the operation parameters of the air conditioner based on the prediction result in combination with a multi-objective optimization algorithm, so that the energy consumption is effectively reduced on the premise of meeting the comfort level of the user. Meanwhile, the system has an online self-adaption and anomaly detection mechanism, a control strategy can be automatically optimized according to user feedback and environment changes, and the operation intelligence, robustness and safety are improved. The system can be widely applied to indoor environments such as families and office buildings, and has the advantages of energy conservation, high efficiency, intelligent self-adaption, excellent user experience and the like.

The invention relates to the technical field of high polymer material processing, in particular to an insulation board extrusion molding control method and system.The method comprises the steps that 1, multi-dimensional parameters are collected in real time, at least three detection sections are arranged on an outlet section of a die head of an extruding machine in the material flowing direction, and each section synchronously collects the following data; 2, evaluating the rheological state of the melt in real time; 3, dynamically reconstructing a temperature field, dividing the die cavity into an axial independent temperature control section and a circumferential heating unit, and dynamically adjusting the power of the heating unit according to a state judgment result in the step 2; 4, pressure-rotating speed cooperative compensation is carried out; and 5, closed-loop verification is carried out, the density uniformity of the formed plate is detected after the adjustment instruction is executed, and if the density range exceeds the limit, the step 2 is executed again. By accurately controlling the temperature and the concentration of the foaming agent, the feasibility of high-proportion mixing of the reclaimed material is optimized, the quality fluctuation caused by temperature and viscosity fluctuation is reduced, and green manufacturing, energy conservation and consumption reduction are promoted.

The invention provides a frying and baking equipment heat preservation automatic control method and system, and relates to the technical field of food processing equipment control, and the method comprises the steps: collecting temperature signals of different positions through a plurality of groups of thermocouples and infrared thermal imaging sensors which are arranged in a frying and baking area, constructing a dynamic three-dimensional temperature field model, and obtaining a dynamic three-dimensional temperature field model; calculating a temperature standard deviation and a maximum temperature gradient in real time according to the dynamic three-dimensional temperature field model; and when the temperature standard deviation is larger than or equal to a preset threshold value, according to the heat distribution difference of the three-dimensional temperature field model, a multi-region differential compensation strategy is generated, the power distribution proportion of each heating unit is dynamically adjusted, and the duty ratio parameter of pulse width modulation is optimized in real time. Through multi-sensor accurate monitoring, dynamic regulation and control and intelligent stop judgment, uniform and stable temperature control, energy conservation and consumption reduction are achieved, the service life of equipment is prolonged, meanwhile, the use safety of a user is guaranteed, and the cooking experience is improved.

The invention relates to a turbine blade global temperature field reconstruction method based on a multi-modal physical constraint network, and belongs to the technical field of aero-engine thermal management. Aiming at the problems of insufficient global heat conduction path modeling, lack of physical rules and blank region differentiation perception, carrying out normalization and position coding on sparse temperature measurement points to form a Transform encoder input sequence; extracting features through an encoder and compressing the features into global feature vectors through self-attention pooling; decoding the image into a temperature field image by adopting a U-Net generator fused with jump connection; a multi-scale discriminator is constructed, a semantic weighting mechanism is introduced, and differentiated weights are distributed to cooling holes, tenons and pressure surface areas; a physical constraint loss and local energy conservation loss are constructed based on a heat conduction equation, and a weighted training model is combined with confrontation loss and temperature error loss. The method is used for providing high-precision temperature distribution data support for turbine blade structure optimization design, cooling system efficiency evaluation and thermal fatigue prevention.

The invention provides low-carbon fuel blending coupling combustion test equipment and method, and the equipment comprises a blending fuel feeding system which is used for blending solid-phase, liquid-phase and gas-phase low-carbon fuels with fossil solid fuels or other solid fuels in proportion; the one-dimensional combustion furnace is used for coupling combustion of the blended fuel; the flue gas treatment system is used for carrying out cooling, dust removal, desulfurization, denitration and carbon dioxide adsorption and trapping treatment on flue gas generated by coupling combustion; the control cabinet is used for controlling the stable and safe operation of the whole experiment platform; the low-carbon coupling combustion test system and method and the coupling combustion low-carbon technology have the beneficial effects that the coupling combustion condition that the actual boiler is internally mixed with low-carbon fuel is simulated by adopting a feeding and conveying mode of accurately controlling the blending combustion proportion, and the low-carbon coupling combustion test system and method and the coupling combustion low-carbon technology are formed; the device has multiple innovative functions of controlling the blending combustion ratio, efficiently coupling combustion, comprehensively purifying flue gas, adsorbing and trapping carbon dioxide, saving energy, reducing carbon and the like.

The invention provides an electric energy metering box temperature field simulation analysis method based on finite elements, and relates to the technical field of electrical equipment fault diagnosis. The method comprises the following steps: constructing a three-dimensional structure model considering asymmetric layout and material attributes of internal elements; identifying key heating elements and establishing a dynamic heat source; constructing an unsteady state heat conduction model by combining a Fourier law and an energy conservation equation; setting environment related boundary conditions; and outputting a temperature distribution diagram and an abnormal hot spot early warning result, thereby realizing accurate modeling and risk pre-judgment of the thermal behavior of the electric energy metering box.

The invention provides a reusable rocket landing stage starting height prediction method, a guidance system and a processor, and aims to dynamically and accurately determine the ignition time of an engine by constructing a real-time online starting height prediction model based on an energy conservation principle and combining multiple safety thresholds (including an allowable starting height upper limit and a standby starting height lower limit) so as to predict the starting height of the rocket in a landing stage. And the safety and fuel optimality in the whole flight envelope are ensured, so that the reliability, accuracy and economical efficiency of the reusable rocket landing are remarkably improved. The problems that in the prior art, engine starting height prediction precision and robustness are insufficient, online adaptation to uncertainty of a complex flight environment cannot be achieved, and safety margin consideration is insufficient are solved, and potential safety risks are thoroughly eliminated.

The invention provides a consumption reduction method and device for a wind and light energy storage complementary thermal power plant system, and the method comprises the steps: obtaining the wind speed, illumination intensity, energy storage charge state, auxiliary power load curve, power grid dynamic electricity price signal and thermal power generating unit auxiliary machine operation parameters of the thermal power plant system, and taking the parameters as a data set; based on a weather prediction model, a load prediction model and an electricity price fluctuation model, generating a wind power generation output prediction value, a photovoltaic power generation output prediction value, a load demand prediction value and an electricity price interval prediction value in a future preset time period according to the data set, and taking the prediction values as prediction results; by taking minimization of plant power cost and maximization of renewable energy consumption as targets, generating an energy storage charging and discharging instruction, a wind power generation and photovoltaic power generation grid-connected priority sequence and a thermal power auxiliary engine regulation and control strategy according to the data set and the prediction result to serve as control instructions; control instructions are executed through control equipment in the wind-solar energy storage complementary thermal power plant system, and energy conservation and consumption reduction are achieved by integrating wind energy, solar energy and an energy storage system.

The invention provides a multi-parameter cold source intelligent control system and method based on big data, and the system comprises a data collection and control module which is used for collecting related parameters of a cold source system; the comprehensive energy consumption standardization module is used for converting the energy consumption parameters into unified standardized energy consumption indexes; the comfort degree algorithm construction module is used for generating a dynamic comfort degree index; the association rule mining module is used for mining a multi-dimensional association rule from historical data; the intelligent decision module is used for generating an optimal control strategy based on the index, the index and the association rule; according to the embodiment of the invention, based on a multi-parameter fusion cold source intelligent control technology of deep reinforcement learning and an improved FP-Growth algorithm, through deep mining and intelligent decision making of multi-source data, the optimal control strategy is converted into equipment regulation and control parameters, and the equipment regulation and control parameters are obtained. And high efficiency and energy conservation of the cold source system and accurate regulation and control of indoor environment comfort are realized.

The invention provides an intelligent electric meter calibration method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a single meter-transformer area double-layer data system through a special transformer collection terminal, and collecting transformer area total intelligent electric meter data, user meter temperature and humidity, an electric energy observation value, transformer area line impedance and load unbalance degree data; constructing an energy conservation optimization function according to the user meter electric energy observation value and the total meter electric energy by combining the deviation with the observation value; determining line loss data according to total meter current and user temperature and humidity, determining power supply stability through total meter voltage, and determining optimization function constraint conditions according to corresponding data; and solving an optimal user table software compensation coefficient and a transformer area line loss correction coefficient by a generalized genetic algorithm, updating a humidity-error correlation coefficient and a line loss reference value by a feedback performance attenuation model to determine an adjusted single table calibration error range, and performing single table calibration by combining the user table temperature and humidity and an electric energy observation value. By adopting the scheme, the accuracy, the reliability and the environmental adaptability of the calibration of the intelligent electric meter are improved.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention belongs to the technical field of wireless communication and antennas, and particularly relates to a phased array antenna pattern synthesis method and system based on a graph neural network, array element parameters and environmental parameters are converted into a graph node and edge feature matrix, and array element excitation parameters are optimized by using a multilayer graph attention network; according to the method, energy conservation and array element spacing constraint are ensured through a physical constraint module, and the performance of a directional diagram is verified and iteratively optimized through a fast moment method; under the 1024 array element scene, the calculation efficiency is greatly improved, second-level optimization is achieved, and meanwhile, the system has the dynamic self-adaptive capacity, array element faults and carrier deformation can be responded in real time, and the optimized directional diagram performance can be kept; according to the technical progress, the calculation efficiency is improved, the stability and adaptability of the system are enhanced, and an innovative solution is brought to the technical field of wireless communication and antennas.

The invention belongs to the technical field of electronic equipment thermal management, and discloses an electronic equipment thermal field rapid prediction method for multi-physical field strong coupling. According to the method, a layered heat source intensity distribution model is constructed, a grid mapping relation topological graph technology and a boundary node energy conservation compensation mechanism are developed, a multi-time scale coupling transfer function is designed, and a dynamic adjustment strategy based on phase deviation analysis is introduced. According to the method, the non-uniform heat source distribution and thermal diffusion dynamic characteristics under the high-frequency condition are accurately captured, the energy consistency of multi-physical field coupling is ensured, the thermal field prediction precision is improved, particularly, the method has outstanding advantages in the aspect of phase and amplitude prediction of transient thermal response, and meanwhile calculation resource consumption is greatly reduced.

The invention relates to the technical field of heat preservation and energy conservation, and particularly discloses a system for testing the heat conductivity coefficient of a pipeline heat preservation material under an engineering meteorological environment condition, which comprises a straight pipe test piece with calibration ends fixedly mounted at two ends, and a heat preservation covering material wrapped on the outer wall of the straight pipe test piece to form a pipe fitting to be tested; the ventilation hood is positioned in the sealed space; air pipes at the output end of the air conditioning system are all located in the sealed space, and air outlets of the air conditioning system directly face the ventilation hood. By simulating multi-parameter coupling working conditions such as in-pipe medium temperature gradient, external temperature and humidity / wind speed circulation and mechanical vibration, the heat conductivity coefficient of the material is dynamically corrected, whether the heat conductivity performance of each layer of material meets the standard specification or not is verified, and the rationality of the composite structure is quantitatively evaluated based on a thermal resistance network model. Data support is provided for optimizing the heat preservation layer combination mode and thickness design, and the problem of design deviation caused by the fact that a traditional method cannot reflect actual engineering working conditions is solved.

The invention discloses a production process control system for functional food, and belongs to the technical field of production equipment control systems, and the system specifically comprises a parameter collection module which is used for collecting raw material characteristic parameters of functional food raw materials, and the raw material characteristic parameters comprise rheological characteristic parameters, thermal stability parameters and structural strength parameters; the process adaptation module is used for constructing a dynamic process adaptation model and mapping the raw material characteristic parameters into an equipment control instruction set; dividing energy demand levels according to raw material processing stages, and generating an on-demand energy supply tree structure; the equipment adjusting module is used for monitoring operation state data of the production equipment in real time; triggering a process parameter dynamic compensation instruction based on the equipment operation state data; the equipment feedback module is used for comparing the deviation between the actual product characteristics of the production line and the target characteristics through the closed-loop verification unit, and iteratively optimizing the dynamic process adaptation model; precise production, energy conservation and efficiency improvement of functional food are achieved through the multi-module cooperation technology.

The control method comprises the steps that when the refrigerator enters a loading recovery period, if it is detected that the temperature rise of a compartment is too high, the rotating speed of a compressor is sequentially increased in at least two continuous adjustment periods; and when it is detected that the temperature of the chamber is decreased or the temperature of the chamber is increased to be normal in any adjustment period, the compressor is controlled to operate at the standard rotating speed which is smaller than the rotating speed in the adjustment period. According to the method, an extensive control mode that the rotating speed of the compressor is directly and greatly increased in a traditional scheme is changed, a periodic progressive speed increasing strategy is adopted, the rotating speed is gradually increased through a plurality of continuous adjusting periods at the initial stage with the large temperature difference, high-frequency starting and excessive power output of the compressor are avoided, energy consumption is reduced, and the purpose of saving energy is achieved.

The invention provides an energy-saving thermal-insulation self-adaptive dynamic control method and system for frying and baking equipment, and relates to the technical field of data processing, and the method comprises the steps: 1, collecting the surface temperature distribution, thickness gradient and environment temperature and humidity data of food materials in real time, extracting three-dimensional thermal diffusion morphological features, and generating an initial morphological feature vector; and step 2, based on a real-time deviation value of a set target temperature and an actually measured food material core temperature, a pseudo partial derivative estimation value is calculated in combination with the morphological feature vector, and periodic intermittent power parameters including a duty ratio and a period are generated according to the pseudo partial derivative estimation value. Through multi-source data acquisition and analysis, parameter dynamic calculation and optimization and knowledge base intelligent matching, accurate temperature control, high efficiency and energy saving are realized, and the service life of equipment is prolonged while the food material cooking quality is improved.

The invention discloses a temperature and humidity cooperative control method for an air source heat pump drying room, and relates to the technical field of energy conservation, environmental protection and intelligent manufacturing. The method comprises the following steps: acquiring gas heat state parameters and material and environment characteristic parameters in an air source heat pump drying room, and constructing a multi-source state vector; calculating a dynamic response coefficient group and a material evaporation influence coefficient based on continuous sampling data, and establishing a cross response model; a control compensation signal is generated and corrected according to the disturbance estimator, and the comprehensive control quantity is input into an execution module of the air source heat pump system; a dynamic response coefficient group and a lag correction parameter are updated through a periodic feedback data set, and dynamic coordination between temperature and humidity variables is achieved; the response stability of temperature and humidity control can be maintained under the condition that the material moisture content, the stacking density and the external environment condition change, system oscillation and energy consumption deviation are reduced, and the energy efficiency level of the air source heat pump drying process and the product drying uniformity are improved.

The invention provides an electromagnetic thermal multi-physical field simulation method based on an FDTD / FVTD algorithm, and belongs to the technical field of electromagnetic simulation. Performing time domain finite difference discretization on the established spatial discrete model by adopting a Yee cellular structure, and calculating electromagnetic field distribution according to a Maxwell rotation equation; constructing an adaptive steady-state judgment criterion based on the instantaneous total energy change rate, and updating electromagnetic field distribution of the spatial discrete model by adopting an FDTD explicit method; calculating the electromagnetic loss power density of the spatial discrete model, and transmitting the electromagnetic loss power density into a thermal field for calculation; based on an FVTD method, thermal field distribution of the discrete model is calculated through an explicit or implicit format, and thermal field distribution information is obtained. The FDTD method is based on explicit time domain iteration, transient behaviors such as electromagnetic wave propagation and scattering can be visually captured, and the FDTD method has broadband response capability and complex medium adaptability and is especially suitable for the high-frequency electromagnetic problem. The FVTD method strictly ensures energy conservation, directly treats boundary heat flux and is suitable for non-uniform materials and complex heat transfer boundary conditions.

The invention relates to the technical field of energy utilization, and discloses an urban heat and cold supply centralized energy comprehensive utilization method. The method comprises the following steps: S1, acquiring physical parameters and operation data of an urban heating and cooling system, wherein the physical parameters comprise a system initial temperature field, a thermal diffusion coefficient, heat source distribution, equipment performance parameters and heating and cooling demand loads; the invention further provides an urban heat and cold supply centralized energy comprehensive utilization system. The data acquisition module is used for acquiring physical parameters and operation data of the urban heating and cooling system; and the thermodynamic modeling module is used for constructing a thermodynamic model based on the law of conservation of energy. By constructing an optimization model based on energy conservation and entropy increase minimization, energy loss in a heating and cooling system is remarkably reduced, by optimizing energy distribution in real time and dynamically adjusting temperature field distribution, invalid energy consumption is reduced to the maximum extent, the overall energy utilization rate of the system is increased, and the energy utilization rate of the system is increased. And therefore, the heating and cooling system can operate efficiently under various working conditions.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention relates to the technical field of green manufacturing, in particular to a manufactured cutting fluid recovery treatment method based on an oil-scrap separation system, which comprises the following steps: step 1, dynamic viscosity regulation and control pretreatment; 2, gradient centrifugal separation, wherein the pretreated mixed liquid is subjected to three-stage centrifugal separation; 3, interface adsorption fine treatment is conducted, specifically, the oil liquid obtained after centrifugal separation flows through a layered variable-porosity adsorption tower, and regeneration of an adsorption material is triggered according to the pressure drop change; and step 4, optimizing closed-loop parameters, and dynamically correcting the control parameter thresholds in the steps 1-3 by collecting and processing the associated data of the process parameters and the final oil metal residual quantity. According to the method, through precise matching and dynamic adjustment of multiple steps, the separation effect can be improved to the maximum extent in the green manufacturing and cutting fluid recycling process, waste emission and resource waste are reduced, and the purposes of energy conservation and emission reduction are achieved.

The invention belongs to the technical field of high-energy-consumption industrial energy conservation and carbon reduction, and particularly relates to an electrolysis process waste heat gradient utilization and green electricity energy storage coupling system and a regulation and control method thereof. An electrolysis process waste heat gradient utilization and green electricity energy storage coupling system comprises a waste heat recovery module used for recovering and gradient utilization of electrolysis high-temperature and medium-low-temperature waste heat for power generation or heat supply; the green electricity direct supply module comprises a photovoltaic unit, a hybrid energy storage unit and an electricity-heat conversion unit and is used for preferential direct supply, fluctuation absorption and storage of green electricity; and the multi-energy coupling regulation and control module is in communication connection with each module and a power grid, and dynamically optimizes and coordinates energy distribution among waste heat power, green power, energy storage and the power grid based on a preset algorithm.

The invention discloses a low-carbon transformation method, system and device for a building air conditioning system. The objective of the invention is to solve the problems of static model, single target, one-sided evaluation and non-standardized process in the existing reconstruction technology. According to the method, building multi-dimensional data are collected; constructing a building energy consumption prediction physical simulation model coupled with a user behavior prediction model based on machine learning to dynamically reflect a real operation condition; a multi-dimensional objective function is defined, and a Pareto optimal transformation scheme set is generated; constructing an evaluation model based on an analytic hierarchy process and a fuzzy comprehensive evaluation method, and calculating a comprehensive evaluation value of each scheme; and selecting an optimal scheme according to the comprehensive evaluation value, and generating a detailed implementation report. The method can scientifically and efficiently determine the global optimal transformation strategy considering energy conservation, carbon reduction, comfort and economy, and significantly improves the scientificity and comprehensive benefits of building transformation decision.