5963results about "Central heating system" patented technology

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.

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 intelligent heat supply pipe network optimization control method and system based on the Internet of Things, and relates to the technical field of heat supply pipe network optimization control, and the system comprises environmental parameter collection modules which are distributed in different areas covered by a heat supply pipe network and are used for collecting environmental data of each area in real time; and the Internet of Things control platform is used for receiving the environmental data from the environmental parameter acquisition module. According to the intelligent heat supply pipe network optimization control method and system based on the Internet of Things, flow and temperature data are monitored in real time, the valve opening degree is accurately controlled according to a PID control algorithm, a closed-loop control system is formed, heat supply demand changes are quickly responded, energy waste is reduced, heat supply quality is guaranteed, meanwhile, energy consumption is reduced, and the energy utilization efficiency is improved; environmental parameters and user heat using habit changes are monitored in real time, a heat supply strategy is adjusted in time, heat supply demand fluctuation is quickly responded, and it is ensured that heat supply always meets the actual demand of a user.

The invention relates to the technical field of heat supply system temperature control, and discloses a temperature control method of an air source heat pump heat supply system. The method comprises the following steps: establishing a temperature field model according to temperature-pressure-flow data of the head and tail ends of a building and each region, and obtaining distribution characteristics; analyzing and predicting a thermal load based on the temperature rise and drop rate and the heat storage coefficient; determining a water supply temperature correction coefficient and a valve opening degree through fuzzy control in combination with an outdoor temperature trend; generating a partition start-stop sequence according to the partition start-stop sequence; and finally calculating heat pump operation frequency and water pump rotation speed. According to the method, the partition control strategy based on temperature field modeling is established, the technical means such as unit pressure compensation and partition alternate starting are combined, accurate temperature control over all the heat supply areas is achieved, and the problem of uneven heat supply in the prior art is effectively solved.

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 discloses a remote wireless heat supply valve control system based on Internet of Things, which relates to heat supply system application and comprises an intelligent communication management module, a real-time environment sensing module, a self-adaptive frequency distribution and spectrum management module, an equipment control and execution module and a system monitoring and maintenance module. According to the remote wireless heat supply valve control system based on the Internet of Things, the problem that control instruction transmission of a heat supply system is unstable due to wireless interference in a complex environment is effectively solved through the self-adaptive multi-band dynamic switching and intelligent interference recognition technology; the system not only improves the reliability and stability of heat supply control data transmission, but also optimizes heat energy distribution and resource utilization, reduces the operation cost of the system, and obviously enhances the competitiveness and adaptability of the system in the fields of central heating, commercial mansions and large residential areas.

The invention discloses an intelligent heat supply two-network balance control method and system based on fuzzy reinforcement learning, and the method comprises the steps: obtaining real-time operation parameters which comprise the return water temperature, the heat load and the environment temperature; based on the real-time operation parameters, preliminarily generating a preliminary adjusting quantity of the opening degree of the valve; operation data are analyzed in real time, fuzzy rule parameters are adjusted according to feedback of the reinforcement learning module, and the fuzzy rule parameters comprise priority weights and membership functions of return water temperature deviation; and according to the preliminary adjustment effect, a deep learning algorithm is utilized to construct a state-action space equation, an energy consumption design reward function is considered, the priority weight is further optimized and fed back to a dynamic optimization module of a fuzzy rule, and secondary adjustment of the valve opening degree is output to an execution mechanism. The problems that a traditional fuzzy control method cannot adapt to complex working condition changes in the operation of the heat supply two-network, the adjusting strategy lags behind, the adjusting effect is not accurate enough, and the adjusting efficiency and energy consumption are affected are solved.

The invention discloses a heat supply system multi-working-condition scheduling control method based on a large model and multiple agents. The method comprises the steps that a heat supply system digital twinborn model is established; the method comprises the following steps: setting a scheduling control center management agent, predicting a thermal load demand by adopting a large language model and combining a thermal load prediction model, dividing a system into a plurality of operation conditions, and setting a plurality of condition sub-agents corresponding to the operation conditions; the dispatching control center management agent judges whether the working condition sub-agents under the corresponding operation working conditions are triggered to start working or not, and meanwhile heat load demand values, predicted through a large language model, of all time periods in the future and the heat production amount of the new energy heat supply unit are transmitted to the corresponding working condition sub-agents; and the working condition sub-agent adopts a large language model to establish a system optimization scheduling control knowledge graph based on the large language model, obtains a scheduling control strategy of each device on the source network load side of the heat supply system, and feeds back the scheduling control strategy to the digital twinborn model for guiding the actual operation management of the heat supply system.

The invention discloses a sewage waste heat recovery heating control method and system based on a digital twinning heat pump. The method comprises the steps that a digital twinning platform of a sewage waste heat recovery heating system is constructed; based on the operation parameters of the entity equipment of the sewage waste heat recovery heating system, establishing a digital twinborn model of the sewage waste heat recovery heating system; deploying the established digital twinborn model of the sewage waste heat recovery heating system in the established digital twinborn platform of the sewage waste heat recovery heating system; based on the physical equipment operation parameters of the sewage waste heat recovery heating system and the virtual data of the digital twinborn model, an optimization target model of the sewage waste heat recovery heating system is constructed, and an optimal control strategy is obtained; dynamic optimization control is carried out on the heating system entity equipment according to the optimal control strategy; the prediction precision is improved through real-time data calibration, model prediction control is fused, second-level response and multivariable cooperative regulation and control are achieved, and the problems that a traditional sewage waste heat recovery heating system is low in energy efficiency, slow in response and high in maintenance cost are effectively solved.

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 discloses a heat supply system capable of storing energy across seasons based on renewable energy sources, relates to the technical field of renewable energy source utilization, and solves the problems of response lag, energy efficiency reduction and poor system stability caused by reaction kinetics nonlinearity and long-term operation uncertainty in an existing heat supply system. The system comprises an environmental data acquisition module, a dynamic load prediction module, a heat storage reaction control module, a nonlinear compensation module, a material performance monitoring module and a long-term operation management module. Real-time response to external environment changes, accurate prediction of heat supply requirements, dynamic adjustment of reaction conditions, continuous monitoring of material performance and optimization adjustment of operation strategies are achieved; through cooperative work of multiple modules, the quick response capacity and energy conversion efficiency of the system on external load changes are remarkably improved, the side reaction risk is effectively restrained, the service life of the system is prolonged, and long-term stable and efficient operation of the energy storage and heat supply system in a complex environment is guaranteed.

The invention relates to a networking control method, device and system based on an internet-of-things intelligent heat supply technology, and is suitable for the technical field of intelligent heat supply. According to the method, user heat supply related parameters are input into a prediction model obtained through training under theoretical conditions, a prediction value is output, and environment offset parameters capable of performing sum representation on all complex parameters influencing the indoor heat supply temperature of a user are obtained through difference comparison between the prediction value and a corresponding actual value. The subsequent heating demand temperature value of the user is accurately compensated and corrected based on the obtained environment offset parameter, so that the corresponding heating compensation amount can be accurately reserved according to the heating temperature deviation caused by the influence of the environment parameter of the user when the heating primary side supplies heat, the heating is more accurate and reasonable, and the user experience is improved. The heat supply demand of the user is successfully guaranteed, heat supply resources are not wasted, and the intelligent heat supply efficiency is improved.

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.

The invention provides an air source heat pump heating method and system, and relates to the technical field of heat pump system thermal control, and the method comprises the steps: collecting evaporator surface frost layer thickness, environment temperature and humidity parameters in real time, inputting the parameters to a pre-trained neural network algorithm model, and identifying a frosting rate by analyzing a multi-factor nonlinear relation; predicting a future frosting development trend, and outputting frosting thickness increase prediction and severity degree results; and according to the frosting thickness increase prediction and the severity degree result, reverse circulation defrosting is started in advance when the frost layer does not reach the critical thickness, and optimal control over the defrosting process is achieved by adjusting the opening degree of the four-way valve and the rotating speed of the draught fan, so that the system state after defrosting is obtained. The energy efficiency of the system is improved, and the heating comfort is optimized.

The invention provides a waste heat efficient recovery and intelligent distribution method and system, and belongs to the technical field of waste heat recycling. The method comprises the steps that operation parameters of a compressor, heat supply medium temperature data and the heat supply requirement of a target area are obtained; inputting the operation parameters and the heat supply medium temperature data into a pre-trained waste heat prediction model to generate a waste heat prediction value of recoverable waste heat; calculating a heat supply demand value of the target area according to the heat supply demand of the target area and a thermodynamic transfer model; and determining a corresponding distribution strategy according to the waste heat prediction value and the heat supply demand value. According to the waste heat efficient recovery and intelligent distribution method and system, accurate prediction and reasonable distribution of waste heat are achieved by collecting and analyzing the operation parameters of the compressor, the temperature data of the heat supply medium and the heat supply demand of the target area, the waste heat recovery and utilization efficiency is improved, and the heat supply demand of the target area is met.

The invention provides an optimization method of a heat supply system, which comprises the following steps: acquiring a real-time heat source operation state, a power grid load, a user demand and an environment constraint condition of the heat supply system, extracting characteristic information of sudden cooling and equipment fault information, and determining a heat source priority and a response speed initial value; acquiring parameters required by a cost function corresponding to the adjusted heat source priority sequence, determining cost weights of different heat sources after adjustment through a linear weighting method, and outputting a cost optimization sequence; identifying a power grid load according to the cost optimization sorting and the environment constraint condition, and if the power grid load is lower than a preset threshold value, calculating a heat source output distribution initial scheme through a dynamic programming algorithm to obtain a preliminary output matrix; and according to the optimized output distribution scheme, summarizing the total heat source emission amount, and if the total heat source emission amount exceeds a preset threshold value, reducing the high-emission heat source output proportion through an iterative adjustment algorithm, and outputting a target output distribution matrix.

The invention relates to the technical field of power supply, and particularly discloses a peak and frequency regulation heat supply method for a multi-element energy storage coupling coal power unit, which is used for solving the problems that only active frequency is optimized, electric heating coordination and charge state constraint are lacked and time delay compensation is insufficient in the prior art. The method provided by the invention comprises the steps of system modeling, controller design, coupling constraint, adaptive parameter adjustment, delay compensation and distributed consensus. According to the method, a dual-domain electric power-thermal interconnection model is constructed, a cooperative controller based on delay compensation distributed model prediction control is designed, electric-thermal cooperative penalty and charge state constraint are introduced into a target function, a prediction window and weight parameters are adaptively adjusted, and network delay is identified and compensated online. And a consensus algorithm is adopted to realize multi-region power-heat collaborative optimization.

A water supply device for supplying water for consumption includes a hot-water tank and a pipeline for conveying water with a temperature below 65° C. to a draw-off point. The water supply device further includes an inlet valve configured for allowing or blocking inflow of fresh water into the hot-water tank feed, a return pipe that connects the line for conveying water with a temperature below 65° C. to the hot-water tank for returning water, and a return pipe valve fitted in the return pipe to allow or to block flow of water through the return pipe, in such a way that by blocking the inlet valve and opening the return pipe valve, a circulation circuit is formed for the at least partial sterilization of the line for conveying water with a temperature below 65° C.

The invention relates to the technical field of smart home environment control, in particular to a temperature and humidity control method and system for smart home, and the method comprises the following steps: S1, monitoring the indoor temperature and humidity and the VOC concentration in the air in real time; s2, on the basis of the VOC concentration data collected in the S1, identifying and analyzing peculiar smell components; s3, constructing a temperature and humidity and peculiar smell interaction relation model; s4, generating an adjusting scheme for the current environment according to the temperature and humidity and peculiar smell relation model constructed in the S3; s5, executing corresponding adjustment operation according to the adjustment scheme generated in the step S4; and S6, dynamically updating and optimizing the temperature and humidity and peculiar smell interaction model. According to the method, the interactive relation model of the temperature, the humidity and the VOC concentration is constructed, the adaptive optimization technology is combined, the temperature, the humidity and the air quality are dynamically adjusted, and the accuracy of indoor peculiar smell control and the intelligent level of environment adjustment are improved.

The invention belongs to the technical field of building heat supply and intelligent power grid demand response control, and particularly relates to an intelligent power grid-oriented heat pump dynamic preheating and demand response control method, which comprises the following steps of: identifying building thermal characteristic parameters, introducing real-time environment variables such as external meteorological parameters and the like and thermal resistance and thermal capacity characteristics of a building, excessive or insufficient preheating caused by only relying on a timer is effectively avoided, and the balance of accurate heat supply and energy conservation is achieved; by adjusting the preheating starting time and the peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or the power grid demand signal, and the peak load is translated or reduced, so that the dual purposes of economic benefits, energy conservation and emission reduction are achieved; and formulating a peak period control strategy and formulating a non-peak period control strategy. According to the control method, actual thermal characteristics and external meteorological conditions of a building are taken as cores, load reduction in the peak period of a power grid and indoor thermal comfort are taken as double targets, and preheating time self-adaption, demand response flexibility and system operation reliability are achieved at the same time.

The invention discloses a secondary network heat supply temperature control method and device based on a negative pressure water mixing technology, and relates to the technical field of secondary network heat supply control. The secondary network heat supply temperature control method and device based on the negative pressure water mixing technology comprises the steps that S1, operation data, structure steady state data and thermal environment data in a current heat supply task are collected, and a standardized heat supply working condition data set is constructed after preprocessing; s2, local hydraulic fluctuation characteristics are evaluated, and the water mixing proportion, the water supplementing flow and bypass path configuration are dynamically adjusted; s3, jointly analyzing the temperature difference of supply water and return water, the heat deviation and the valve state, and driving water mixing adjustment and path optimization; s4, evaluating adjustment intensity and heat response deviation, and realizing abnormal disturbance response and adjustment strategy correction; and S5, recording negative pressure, uneven heat and cold and response data for subsequent diagnosis and steady state identification. The problems that a traditional water mixing control means is difficult to effectively inhibit pressure fluctuation of the water return end, local negative pressure is easily formed, and local energy waste and uneven heat distribution are caused are solved.

The invention relates to the technical field of intelligent buildings and energy management, and particularly discloses an intelligent building heating intelligent optimization operation method and system based on deep learning, and the method comprises the steps: collecting building environment parameters and equipment operation data in real time through a distributed optical fiber sensing network and an infrared thermal imaging system; extracting minute-level fluid transmission and distribution parameters and hour-level building thermal inertia characteristics by adopting wavelet packet transformation and a graph neural network; then, constructing a neural differential equation prediction model fused with physical constraints, and outputting high-precision thermal load demand prediction through a differentiable heat conduction operator coupling multi-time scale feature; then, a mixed integer optimization model considering equipment life loss is established, and boiler start-stop combination and pipe network flow distribution are synchronously optimized by adopting a hierarchical decision-making mechanism; and finally, closed-loop control is realized through a multi-mode actuator network, and model parameters are dynamically adjusted in combination with an online learning mechanism.

The multi-energy complementary heat supply system comprises an external basic heat source, a middle-grade synergistic heat source, a high-grade energy storage heat source, an intelligent heating power mixing center and a central controller, and the intelligent heating power mixing center is used for heating heat source equipment of all levels and operation instructions of associated assemblies according to the operation instructions of the heat source equipment of all the levels and the operation instructions of the associated assemblies. Heat energy of different grades is mixed and allocated, and heat loads meeting the grade requirements of heat consumers are output to the heat consumers; the central controller is used for establishing a multi-target optimization model taking the minimum total operation cost of the system as a core target based on the electricity price prediction data and the different-grade thermal load prediction data, solving the multi-target optimization model and generating a system operation strategy under the condition that preset basic guarantee logic, economic operation logic and peak response and power grid interaction logic are met; and the generated system operation strategy is converted into an operation instruction for the heat source equipment of each level and the associated assembly, so that the reliability and the adaptability of operation control of the heat supply system are effectively improved.

The invention relates to the technical field of temperature control, and provides an indoor temperature control method and device, electronic equipment and a readable storage medium. The method comprises the steps that operation state data, corresponding to current time, of temperature control equipment is obtained according to the current time, indoor and outdoor environment characteristic data corresponding to the current time are obtained, and the temperature control equipment is cold station equipment or hot station equipment; predicting an indoor load demand in the next time period according to the indoor and outdoor environment characteristic data to obtain an indoor target load in the next time period; wherein the load demand is a cooling capacity demand or a heat demand; predicting operation parameters of the temperature control equipment according to the target load, the operation state data and the indoor and outdoor environment characteristic data, and generating target operation state data of the temperature control equipment; and the temperature control equipment is controlled according to the target operation state data, so that the performance of the temperature control equipment is remarkably improved, and more refined and personalized temperature control is realized.

The invention discloses an intelligent building heating and ventilation automatic control method and system based on digital twinning, and relates to the technical field of intelligent heat supply. The method comprises the specific steps that entity data of a building and a heat supply system are obtained, a building heat supply system twinborn model is constructed, and a digital twinborn automatic control system is constructed based on the building heat supply system twinborn model; deploying a multi-modal sensing network, and constructing a distributed data monitoring system; real-time data collected by the distributed data monitoring system is input into the digital twin automatic control system, and a dynamic control strategy is generated in combination with historical data; and the heat supply system is controlled to execute actions based on the dynamic control strategy, and feedback data are sent to the digital twin automatic control system for control strategy optimization. According to the invention, the response speed of the system is obviously improved, and the adaptability to complex and changeable environments is enhanced.

The invention relates to the technical field of temperature control, in particular to a heating and ventilation heating power constant-temperature variable-flow control method, and provides the following scheme that circulating pump frequency instructions and water temperature response data of multiple historical regulation and control periods are collected, and a dynamic track of the current water temperature in a temperature inertia field is constructed; calculating an attraction potential energy state between the current water temperature and a target constant temperature value in combination with the disturbance residual matrix; and outputting the frequency bandwidth interval of the circulating pump based on the state to realize dynamic adjustment. An adjacency matrix and a Laplacian matrix are constructed by introducing a hydraulic communication relation, spatial convolution and weighted updating are performed on disturbance residues in combination with a topological diffusion core, and accurate propagation and suppression of disturbance are realized. The method can effectively solve the problems of nonlinear response, delay effect and disturbance coupling of the water temperature to the pump frequency, and the temperature stability and the energy utilization efficiency of a heating system are improved.

An on-demand water heater (12) includes a heat exchanger (25). Water is supplied to the water heater from a water source such as a well pump (16) or other source of water. The water supplied to the water heater is treated by at least one water conditioner 18. A plurality of electrical heating elements (26, 28, 30, 32) are supplied with electrical power from rechargeable battery pack (46). At least one circuit (48) is operative to control the delivery electrical power from the battery pack to the heating elements to provide for the delivery of heated water at a set temperature from the water heater. The battery pack of the water heater is recharged from an available supply of house current electrical power. The rechargeable battery pack of the exemplary water heater also provides an auxiliary source of electrical power for other devices in circumstances when electrical power from a usual supply source becomes unavailable.

The invention discloses a heat supply system multi-stage emergency disposal scheduling method containing a large model. The method comprises the steps of forming a heat supply system emergency disposal scheduling large model; in the emergency prevention preparation stage, a large model is used for building a heat supply system multi-type emergency event question and answer database, and emergency knowledge intelligent question and answer, emergency event reason analysis, risk hidden danger query and emergency disposal scheduling measure recommendation are conducted; in the emergency response stage, the large model is guided to output an optimal feature combination representing various types of emergency events, and machine learning is adopted to identify the various types of emergency events; heat supply loss caused by an emergency event is evaluated by using the large model, a multi-level emergency disposal scheduling strategy is set, and an optimal emergency disposal scheduling scheme is obtained; and in an emergency disposal post-stage, analyzing the emergency disposal scheduling process by using the large model, establishing an emergency disposal scheduling plan library, and performing plan simulation, identifying potential vulnerabilities and strengthening the emergency disposal scheduling process by using the intelligent agent.

The invention discloses an analogue simulation system for operation of a ground source heat pump and a gas boiler in a severe cold area. The analogue simulation system comprises a heat supply correction model building module, a ground source / gas heat supply estimation module, a heat supply dispatching distribution module and a correction model optimization module. According to historical operation data of a ground source heat pump subsystem, heat supply correction models associated with soil geology and environmental climate are constructed based on a single variable principle, a heat supply amount estimated value of the ground source heat pump subsystem is dynamically corrected, heat supply amount of a gas boiler subsystem and heat load required by a building group are estimated, heat supply output of the two subsystems is intelligently distributed, and heat supply efficiency is improved. And the heat supply amount correction model is optimized through the actual heat supply amount deviation. The method can effectively overcome the problem of ground source heat pump heat supply amount deviation caused by soil heat unbalance and climate fluctuation in a severe cold area, further improves the energy efficiency estimation precision of a heat supply system, facilitates optimization of a scheduling strategy, has dynamic compensation and model self-evolution capabilities, and can adapt to complex environmental changes.