73833results about "Lighting and heating apparatus" patented technology

The invention discloses a clean workshop production environment quality control method and system, and relates to the technical field of environment control, and the method comprises the steps: constructing a multi-layer sensing network, deploying temperature and humidity, particle concentration, pressure difference and VOC gas sensors, and carrying out the preprocessing data of each node through edge calculation; fusing the data based on a dynamic weight distribution algorithm, adjusting the weight according to the confidence score, and generating an environment quality comprehensive index; an LSTM pollution diffusion prediction model is established, a diffusion path is calculated in combination with airflow field simulation when pollution suddenly occurs, and an emergency response partition strategy is generated; fresh air system control parameters are optimized through reinforcement learning, a dynamic ventilation frequency adjusting model is established based on a real-time environment quality index and historical energy consumption data, and energy consumption is minimized through a Q-learning algorithm on the premise that cleanliness is guaranteed; a double-threshold early warning mechanism is set, local supercharging purification is started when a comprehensive index exceeds a first-level threshold, a second-level threshold is linked with adjacent areas to form a dynamic isolation barrier, and an intervention scheme effect is simulated through a digital twin system.

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

The invention discloses an intelligent heat supply regulation and control system based on digital twinning and deep reinforcement learning. The system comprises a physical layer, a control layer and a control layer, wherein the physical layer is a physical heat supply system composed of heat source equipment, a transmission and distribution pipe network and a user terminal; according to the digital twinborn layer, a virtual heat supply system mapped with the physical layer in real time is constructed, the virtual heat supply system comprises a multi-physics field coupling model based on the thermodynamics and fluid mechanics principle, operation data of the physical layer are collected through a distributed sensor network, and the state vector of the virtual system is dynamically updated; and the intelligent decision-making layer is integrated with a DRL intelligent agent, the state space of the DRL intelligent agent is defined as a virtual system state vector output by the digital twin layer, the action space of the DRL intelligent agent is a regulation and control instruction combination of heat source power and pump valve opening, and a reward function fuses an energy consumption penalty term, a room temperature comfort reward term and a pipe network stability constraint term. According to the method, global optimization, high-precision continuous regulation and control and collaborative balance are realized through deep collaboration of digital twinning and deep reinforcement learning.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

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

The invention provides an air conditioner working condition monitoring method and system based on the Internet of Things. The method comprises the following steps: monitoring the air conditioning system and the Internet of Things sensing data of the environment where the air conditioning system is located; performing dynamic simulation on the air conditioning system based on the sensing data of the Internet of Things to obtain a real-time simulation working condition of the air conditioning system; the real-time simulation working condition is input into the fault feature decoupling model, and potential fault risks existing in the air conditioning system are predicted from multiple dimensions so as to obtain multiple prediction results of the potential fault risks; a dynamic coordination algorithm is adopted, dynamic traceability and dynamic traceability and collaborative decision making are carried out on the multiple paths of prediction results, control chain conflicts among the multiple paths of prediction results are eliminated, and potential fault traceability information is obtained; and displaying the real-time simulation working condition and the corresponding potential fault traceability information through a real-time monitoring interface of the air conditioning system. Real-time working condition simulation and intelligent fault tracing of the air conditioning system can be achieved, the fault early warning accuracy and system coordination of the air conditioning equipment are improved, and the operation efficiency of the air conditioning equipment is improved.

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

The invention relates to the technical field of data processing, and provides a multi-heat-source networking heat supply optimization operation method and system.The method comprises the steps that environmental parameters, heat source data, market dynamic information, user behavior characteristics and a pipe network topological graph are collected by deploying IoT equipment; acquiring a thermal load time sequence predicted value in a future preset time period; the heat source data and the pipe network topological graph are processed, and a pipe network operation state matrix is obtained; performing feature dimension alignment processing on the pipe network operation state matrix to obtain a pipe network spatial topology feature mapping value; performing weighted fusion on the thermal load time sequence prediction value and the pipe network spatial topological feature mapping value to obtain a final thermal load prediction value; and optimal operation of heat supply is realized according to the final heat load predicted value. According to the method, a real-time response mechanism for environmental parameters, market dynamics and user behavior characteristics can be realized, collaborative optimization of multiple heat sources can be realized, the heat source collaborative efficiency is improved, and energy waste and operation cost are reduced.

The invention discloses an air conditioning system and a control method thereof. The air conditioning system comprises an air conditioning outdoor unit; the air conditioning outdoor unit comprises a compressor, an outdoor heat exchanger and an outdoor fan; the outdoor fan is at least provided with a first working mode and a second working mode; and when the outdoor fan runs in the first working mode, a wind gear is higher than that when the outdoor fan runs in the second working mode. The control method comprises the following steps that S1, the air conditioning system is controlled to enter arefrigeration mode; S2, according to the outdoor environment temperature and / or preset hours, the outdoor fan is controlled to enter the first working mode or the second working mode. According to the control method of the air conditioning system, the air conditioning system can control the outdoor fan to enter the first working mode or the second working mode according to the outdoor environmenttemperature and / or the preset hours, and when the outdoor fan runs in the second working mode, the wind gear of the outdoor fan is lowered, so that the use reliability the air conditioning system cannot be affected, and the requirement of a user on comfortableness can be met.

The invention relates to the technical field of heat load prediction, and discloses a heat supply system load prediction method and system, and the method comprises the steps: collecting multi-source sensing data in real time, and collecting outdoor meteorological parameters and building structure information; based on building distribution, a pipe network structure and user load characteristics of a heat supply area, a multi-stage heat supply load prediction model is constructed. And constructing a thermal topological graph model of the heat supply area based on the graph neural network. And periodically collecting parameters of the building-level edge prediction model, the heat exchange station-level aggregation prediction model and the thermal topological graph model, performing global aggregation optimization, and updating and optimizing each edge node model. And obtaining an edge prediction result according to the optimized model, and jointly controlling the heat source output power, the main pump rotating speed and the area valve opening according to the edge prediction result and the heat source level scheduling prediction model. According to the method, the depiction capability of the system on the dynamic load change and the space heat conduction path is improved, and the generalization capability of model updating and the real-time responsiveness of edge deployment are ensured.

The invention provides an automatic energy-saving control method and device for a ring cooling fan based on load feedback, relates to the field of ring cooling fans, and solves the technical problem of delay of regulation and control in an energy-saving working state. The method comprises the steps that working condition data are input into a preset powder box model, and a feed-forward air volume instruction is obtained through calculation; and inputting the working condition data into a state observer to obtain an optimal estimated temperature value. And the deviation between the optimal estimated temperature value and a preset temperature set value is calculated, and a feedback air volume compensation instruction is obtained through calculation of a feedback controller according to the deviation. And fusing the feed-forward air volume instruction and the feedback air volume compensation instruction to obtain a final air volume control instruction, and issuing the final air volume control instruction to a fan frequency converter for execution. And continuously monitoring the numerical value and the change trend of the feedback air volume compensation instruction, taking the feedback air volume compensation instruction as a prediction error signal of the powder box model, and adaptively adjusting key thermal parameters in the powder box model. The method is used in the control process of the ring cooling fan.

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 an energy-saving control method and system based on a large refrigeration house, and the method comprises the steps: S1, obtaining cargo attribute information in real time through a cargo label, and inputting a dynamic load prediction model to generate a cooling capacity demand prediction signal in a future time period; s2, generating a multi-device cooperative control signal based on the cooling capacity demand prediction signal in the future period; s3, a shelf-level cooling capacity demand distribution signal is generated in combination with the cooling capacity demand prediction signal; s4, generating a directional cold airflow path signal matched with goods shelf distribution according to the goods shelf level cold capacity demand distribution signal; and S5, closed-loop feedback adjustment is conducted on the compressor frequency, the refrigerant flow and the air valve opening through an edge calculation module, and a dynamic balance control instruction of cooling capacity supply and space distribution is generated. The intelligent air quality monitoring method and system based on sensing data feedback can solve the problems of excessive refrigeration energy consumption waste caused by inaccurate cold capacity demand prediction of a large refrigeration house and extra energy loss caused by low cooperative efficiency of multiple devices.

The present invention discloses a handheld spray fan, the handheld spray fan includes a housing assembly, fan assembly, and spray device. The housing assembly features a handheld part, an air duct at one end, and an air outlet cover at the end of the air duct with a through-hole and multiple air outlet holes. The fan assembly is installed within the air duct, and the spray device comprises a water tank, water pipe, mounting seat, fiber water guide, and atomizer. The water tank is positioned within the housing assembly, the water pipe connects the water tank and the mounting seat, the mounting seat is located at the through-hole, and the atomizer is installed on the mounting seat and exposed through the through-hole. The fiber water guide extends from the water tank to the atomizer, ensuring continuous water supply for stable misting.

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 an energy management method, device and equipment based on an air conditioning system and a medium. The method comprises the steps that in a preset period, a target cold storage capacity calculation model matched with the current working condition is selected from a plurality of candidate cold storage capacity calculation models according to the pre-obtained photovoltaic power generation power, the air conditioner system load and the change trend of the environment temperature; the environment temperature and the electric energy are input into the target cold storage capacity calculation model, and the cold storage capacity, output by the target cold storage capacity calculation model, of a phase change cold storage material in the air conditioner system is obtained; based on the cold storage capacity, the photovoltaic power generation power and pre-obtained planned electricity consumption probability distribution, analysis processing is conducted, and an energy management strategy of the air conditioning system is obtained; the energy management strategy is used for guiding the air conditioning system to adopt commercial power supply or photovoltaic power supply, whether to utilize the phase change cold storage material for cold storage or not and whether to start a compressor to assist in executing the cold storage process or not. By adopting the method, the power consumption cost can be reduced.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

A method of assembling a box from a blank and an insulator pad includes obtaining a blank comprising an outer side panel comprising an outer fold line, a connecting strip joined to the outer side panel by a fold line, and an inner side panel connected to the connecting strip by a fold line; obtaining an insulator pad comprising an angled cut defining an edge; folding the inner side panel such that the inner side panel faces the outer side panel; and positioning the insulator pad between the outer side panel and the inner side panel such that the edge of the insulator pad is positioned at the inner fold line.

The invention discloses an airport ground air conditioning unit multi-system energy-saving control method and system, and the method comprises the steps: collecting refrigerant flow data, temperature monitoring data and load demand data of an airport air conditioning system, recognizing a phase change boundary, extracting a gas-liquid conversion point, and carrying out enthalpy value coupling in combination with superheat degree distribution to generate a phase change energy field; identifying heat transfer bottleneck points based on the phase change energy field, generating an equivalent thermal resistance network, and extracting heat capacity characteristic parameters to construct a thermal response path diagram; refrigerant shunting analysis is carried out to extract primary and secondary loops, and an optimal distribution point is identified to generate a load balance matrix; reconstructing a compressor adjusting curve, and identifying a high-efficiency operation interval to generate a frequency conversion adjusting sequence; determining an energy storage window period, and obtaining a dynamic buffer threshold to construct a peak clipping and valley filling instruction set; and an execution deviation track is monitored, the optimal convergence path is recognized, self-adaptive correction is conducted, an energy-saving control instruction is generated, and intelligent multi-system coordination efficient energy-saving control over the airport ground air conditioning unit is achieved.

The invention discloses an automatic control method and system for a variable-frequency range hood, and belongs to the technical field of intelligent control of household appliances, and the method comprises the following steps: collecting oil smoke concentration data, temperature data and airflow disturbance data in a kitchen environment in real time by using a non-contact multi-mode sensor; constructing a multivariable fusion model, and generating a comprehensive environment state vector through feature extraction and weight distribution; an LSTM prediction model is constructed to abstract the current lampblack distribution state and surrounding environment characteristics, and an optimal fan rotating speed control strategy is generated in combination with an air volume-energy consumption-noise multi-target optimization model and a genetic algorithm; and the fan rotating speed is adjusted in real time according to the optimal fan rotating speed control strategy. The non-contact multi-mode sensor is used for collecting oil smoke concentration, temperature and airflow disturbance data in the kitchen environment in real time, the multivariable fusion algorithm and the time sequence prediction model are operated in combination with the edge calculation chip, and efficient, energy-saving and low-noise air volume control is achieved.

The invention relates to the technical field of air-cooled air conditioner control, in particular to an air-cooled air conditioner control method and system based on deep learning, and the method comprises the following steps: obtaining sensor readings of indoor temperature, outdoor temperature and humidity and Wi-Fi channel state information, judging the position number and the activity state of personnel, calculating the position distribution of the personnel in combination with a time window, and calculating the position distribution of the personnel. And integrating to obtain a multi-dimensional environment and load state vector. According to the method, indoor and outdoor temperature, humidity, Wi-Fi channel state information and personnel position distribution and activity states are integrated through a multi-dimensional environment and load state vector, the personnel density change trend is dynamically calculated through a time window, the comprehensiveness and real-time performance of environment perception are enhanced, and high-dimensional data support is provided for control decision making. And a basic comfort degree deviation index is constructed based on the temperature deviation value and the humidity deviation value, and a multi-target performance index set is generated in combination with the wind speed value, the compressor start-stop frequency and the energy consumption power.

The invention relates to the field of household appliances, and discloses an air purifier efficient filter screen replacement prompting method and system.The method comprises the steps that firstly, air adsorption data are obtained, and the concentration of pollution particles is recognized to determine a filter screen load mode; analyzing the use state of the filter screen, and performing attenuation detection in combination with historical replacement records to obtain attenuation data; calculating the service residual life according to the attenuation data, evaluating a purification index, generating a filter screen efficiency index in combination with the environment air quality, and dividing replacement levels; then configuring multi-order prompt information according to levels, determining a filter screen edge area, calculating a penetration rate and positioning a weak part; and finally, generating a local failure graph, extracting failure interference factors, constructing adaptive replacement logic and formulating a replacement prompt scheme. The filter screen utilization rate can be increased, and the purification efficiency can be guaranteed.

The invention discloses a multi-modal data fusion air conditioner optimization control method and system, and belongs to the technical field of intelligent building equipment control. According to the method, temperature, humidity, energy consumption, user behaviors and meteorological data are collected through a multi-source sensor, the data are subjected to dynamic window standardization processing and then input into a gated convolution LSTM network to predict the system state, an optimization strategy is generated in combination with an online reinforcement learning algorithm, multi-modal instructions are dynamically weighted and fused, and execution parameters are adjusted in real time through a feedback correction mechanism. The system comprises a multi-source sensing array, an edge computing unit, a strategy optimization engine and an intelligent execution controller. According to the method, through collaborative optimization of multi-modal spatial-temporal feature fusion and deep reinforcement learning, the problem of unbalance of energy efficiency and comfort is solved, the energy efficiency level and the user comfort of the air conditioning system are remarkably improved, and the method has the characteristics of real-time response and high stability, is suitable for intelligent air conditioning control of modern buildings and has wide application prospects.

The invention discloses a low-vacuum circulating water heating dynamic load matching method based on AI regulation and control, and the method comprises the following steps: step S100, multi-source data real-time collection: building thermal environment parameters are synchronously collected through a temperature sensor array, a vacuum pressure transmitter, a flowmeter and an environment monitoring unit which are deployed in a distributed manner; according to the method, the control period is compressed to the minute level, compared with traditional PID control, the response speed is increased by several times, the indoor temperature can be controlled more accurately, and meanwhile the failure rate of equipment is reduced. According to the sudden weather coping mechanism, heat supply strengthening can be started 30 minutes ahead of time under extreme weather such as snowstorm weather, and the stability of the system is ensured. The method is characterized in that the prediction advantage of deep learning and the optimization capability of model prediction control are deeply combined, and the control problem of multivariable strong coupling of the low vacuum system is solved through a verification system of multi-source data fusion and virtual-real combination.

The invention relates to the technical field of industrial control, and discloses an energy-saving air conditioner intelligent control system which comprises an edge computing node, a dynamic load prediction module, a self-adaptive control module, an energy consumption optimization evaluation module and a fault diagnosis module. A data acquisition module, a real-time control module and a communication interface are integrated; based on the historical environment data and the air conditioner operation log, a long-short-term memory network model is adopted to predict the expected load demand in 2-4 hours in the future; dynamically adjusting the operation number of chilled water units, the frequency conversion frequency of water pumps and the opening degree of fresh air valves according to the expected load requirement; the unit cold energy consumption of a single air conditioner is calculated in real time, and a global optimization instruction is generated in combination with a time-of-use electricity price strategy; motor current harmonic characteristics are collected through the edge calculation nodes, the abnormity of the air conditioner is recognized, graded response is conducted, the intelligent control efficiency of the energy-saving air conditioner is improved, and the overall energy consumption cost of the air conditioner is reduced.

A fault detection method for refrigeration units based on an improved deep learning model is provided, including the following steps: S1: obtaining operating parameters of a refrigeration unit in a normal operating state and in states with different fault types as data sets; S2: detecting local outliers in the data set by using a local outlier factor algorithm and removing the local outliers, and then expanding the data set by using adaptive synthetic sampling; S3: normalizing the data set; S4: constructing a fault detection model; and S5: inputting the parameters of the tested refrigeration unit into the fault detection model, and judging whether the tested refrigeration unit has a fault and the type of the fault.

The invention belongs to the technical field of intelligent building energy management, and particularly discloses and provides a dual-mode cold storage air conditioning system based on human flow dynamic cooperative regulation, which comprises the following steps: acquiring dynamic human flow thermodynamic diagram data in real time, and accurately calculating a multi-region linkage cold load demand value based on a density change correlation coefficient and a heat conduction model; the real-time sensing of the flow density fluctuation and the dynamic refrigeration demand matching are realized; the phase change state of the coolant of the cold storage tank and the power consumption data of the refrigerating unit are collected in real time, the cold storage release proportion, the refrigerating power increment and other key parameters are dynamically adjusted, and the energy utilization rate is increased; the inherent defect of non-uniform cold and heat in zone control is overcome by calculating inter-zone cooling capacity migration parameters; temperature feedback data and a people flow thermodynamic diagram are collected in real time, the proportionality coefficient of the fan rotating speed and the air valve opening degree is dynamically adjusted, a closed-loop learning mechanism is formed, and the matching precision of a temperature control strategy and people flow distribution is continuously optimized.

The invention relates to the technical field of intelligent regulation and control of a heat supply system, in particular to a heat supply system hydraulic unbalance real-time correction method and system based on federated learning, and the method comprises the steps: constructing a temperature prediction model based on an LSTM neural network, deploying federated nodes at each user side, collecting multi-dimensional heat supply data in real time, and carrying out the preprocessing; local model parameters are encrypted and uploaded to a central server through a federated learning framework, global model parameters are generated based on verification set error dynamic weighted aggregation, and nodes are reversely updated; the updated model is used for predicting the heat load in the next one hour, the valve opening degree is dynamically adjusted in combination with a hydraulic equilibrium algorithm, and whole-network flow and heat demand matching is achieved. According to the method, data privacy is guaranteed through federated learning and a block chain technology, edge calculation and LSTM time sequence prediction are combined, the problems that a traditional method is high in data transmission delay, high in privacy risk and poor in model generalization are solved, and the real-time response capability and the energy utilization efficiency of a heat supply system are remarkably improved.