3244results 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 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 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 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.

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, 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 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.

The invention relates to the technical field of data processing, and discloses a heat supply network heat supply system control method based on a prediction model, and the method comprises the steps: S1, collecting historical operation data and real-time operation data of a heat supply network heat supply system, S2, carrying out the preprocessing of the historical operation data and the real-time operation data, and generating a standardized heat supply data set; by constructing a prediction model based on historical data and real-time data, the thermal load demand change in a future time period can be dynamically predicted, the problem of adjustment lag caused by dependence on a static model in a traditional control method is solved, the response ability of a heat supply system to extreme weather and user demand sudden change is improved, and the heat supply efficiency is improved. The stability and reliability of heat supply quality are guaranteed, the time hysteresis between a regulation and control instruction and the actual heat supply effect is reduced by analyzing the heat transmission characteristic and the delay effect of each node of the pipe network in real time, the phenomena of insufficient heat supply and energy waste caused by untimely regulation of the system are avoided, and the energy consumption is reduced. And the operation efficiency and the energy utilization rate of the heat supply system are improved.

The invention discloses a multi-mode heat supply energy consumption evaluation method and system, and relates to the technical field of heat supply energy consumption evaluation and dynamic thermal scheduling control, and the method comprises the steps: arranging a multi-source sensor in a high-temperature flue gas path to collect thermal parameters, and building a time sequence mapping model between a unit load and flue gas thermal characteristics; and according to the thermal power data output by the time sequence mapping model and the heat exchange characteristics of all the heat supply modes, a heat energy availability map of the target time period is generated, and heat demand adaptation data is constructed. And selecting a matching path according to energy consumption and switching constraints based on the heat energy availability map and the heat demand adaptive data, and executing mode dynamic switching control. According to the method, a complete technical closed loop from data perception to state evaluation to action execution is formed. In the multi-mode heat supply system, system fusion of three-layer logic of heat load prediction, map expression and dynamic scheduling is realized, and the intelligence, self-adaption and energy efficiency optimization capabilities of the heat supply process are remarkably improved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention discloses an unattended intelligent control method and system for a heat exchange station, and relates to the technical field of heat energy automatic control, and the method comprises the steps: collecting multi-source environment parameter data, constructing an environment parameter sequence set, constructing a heat load prediction model, and outputting a predicted heat load value through extracting a change relation between local temperature trend characteristics and a time sequence. Carrying out residual comparison on an actual thermal load value and the predicted thermal load value, constructing a residual fluctuation measurement sequence, carrying out multi-dimensional state judgment in combination with a preset threshold range, outputting an operation state identifier, carrying out linkage adjustment on a water pump frequency parameter and a water mixing valve opening parameter, calling an optimization control strategy set, and carrying out optimization control on the water pump frequency parameter and the water mixing valve opening parameter. And generating an updated water pump adjusting instruction and an updated valve adjusting instruction. The operation reliability and the energy utilization efficiency of the heat exchange station are improved, unattended intelligent control is achieved, and the technical problems that in a traditional control method, prediction precision is low, abnormal recognition is not accurate, and control parameter coordination is poor are solved.

The invention relates to a heat supply credible intelligent control method and system based on full trust creativity, and the method comprises the steps: collecting heat supply parameters in real time through Internet of Things equipment, and carrying out the preprocessing of the heat supply parameters through an edge calculation node, and obtaining the preprocessing data; performing thermal load prediction on the pre-processed data through a bidirectional LSTM neural network model based on a full trust creation platform, and outputting a thermal load prediction curve in a future time period; generating a dynamic scheduling instruction based on the thermal load prediction curve in combination with a real-time equipment state and an energy consumption optimization strategy; and the dynamic scheduling instruction is issued to the edge computing node, the operation parameters of the heat source, the pipe network and the user side are automatically adjusted, and when it is detected that the equipment is abnormal, standby equipment is automatically switched or a heat supply path is adjusted, and unmanned regulation and control of the heat supply system are achieved. According to the application, edge computing, the Internet of Things, trusted transmission and industrial control safety capabilities are integrated, so that full-flow coverage of heat supply scene data acquisition and analysis is realized, and the unattended scheduling capability of heat supply operation is improved.

The invention discloses a circulating water waste heat recovery and air cooling collaborative optimization method which is used for solving the problems that an existing air cooling system, an existing waste heat recovery system and an existing water treatment system normally operate independently and lack a unified coordination mechanism, when environment parameters or operation working conditions change, all the systems cannot respond collaboratively, and the overall energy efficiency is difficult to achieve. The method comprises the following steps: generating a system state data set according to acquired environment parameters, unit operation parameters, heat supply network parameters and whole-plant multi-water-source water quality parameters; according to the system state data set, a nozzle array operation strategy is determined through a preset total spraying amount calculation model; according to the unit operation parameters and the heat supply network parameters, waste heat recovery power is determined; determining a water source distribution strategy according to the multi-water-source water quality parameters; according to the nozzle array operation strategy, the waste heat recovery power and the water source distribution strategy, a waste heat recovery-air cooling collaborative optimization strategy is generated; and a cooperative control instruction set is generated according to the waste heat recovery-air cooling cooperative optimization strategy.

The invention discloses an optimal scheduling method for a ground source heat pump system, and relates to the technical field of new energy utilization and intelligent control of a heat supply system.The method comprises the steps that the ground source heat pump system is divided into a tail end heat exchange layer, a water pump transmission and distribution layer and a source side heat pump coupling layer on the basis of a heat energy conversion and transmission path; constructing a response coupling relation and a mapping model; operating data of three layers of equipment are collected, a time sequence of each layer is established, a dynamic delay structure matching coefficient is calculated, and a multi-layer linkage response lag risk is identified based on a first threshold value; further monitoring a second derivative of the source side temperature change, calculating a thermal inertia interference index, and judging that the thermal response of the source side is abnormal in combination with a second threshold value; and the water supply flow of each branch is extracted, the target water supply value and the total flow of the system are combined, a flow deviation factor is calculated, flow distribution deviation is recognized according to a third threshold value, accurate strategy generation and intelligent scheduling control based on multi-parameter dynamic characteristics are achieved, and the response coordination and operation stability of the system are improved.

The invention relates to a centralized heating balanced regulation and control method, device and equipment and a medium. The method comprises the steps that firstly, the heat supply area of a user and the heat use state of a neighbor are collected, the basic flow of the jet device is calculated according to an industrial standard formula, and final flow containing resistance characteristic data is obtained through secondary correction by combining a neighbor heat use state correction coefficient and an environment temperature influence factor. Extracting flow and inlet and outlet pressure difference from jet device parameters, adjusting a valve to enable the resistance proportion to reach a preset interval, calculating parameters such as unit area heat consumption by combining real-time flow and supply and return water temperature difference, generating regulation and control basic data through coupling analysis, adjusting the jet device when the regulation and control basic data reach the standard, and generating a dynamic scheme by combining a thermal load prediction model. And finally, capturing the flow pulse signal according to a fixed period to calculate a difference value, generating a valve control signal according to a threshold value, acquiring the water supply and return temperature after execution, and coupling with a dynamic scheme to generate a regulation feedback signal. By adopting the method, accurate regulation and control can be realized through multi-dimensional analysis, and the flow distribution accuracy and the heat supply balance are improved.

The utility model provides an air conditioner and hot water integrated system with temperature control and dehumidification functions, which comprises a compressor, an outdoor heat exchanger, a first indoor heat exchanger, a second indoor heat exchanger, a water tank and a liquid storage device, the other end of the second indoor heat exchanger communicates with one end of the first indoor heat exchanger, and the other end of the outdoor heat exchanger, the other end of the first indoor heat exchanger and the other end of the water tank communicate with the interior of the liquid storage device. The air suction end of the compressor can communicate with one end of the outdoor heat exchanger or one end of the second indoor heat exchanger or one end of the water tank. According to the utility model, the modes of refrigeration and water heating, heating and water heating and dehumidification and water heating can be realized at the same time, the system can also adapt to the refrigerant circulation amount in different modes, the problem of high power consumption is solved, the problem that the requirement of comfort cannot be met is solved, and the system runs efficiently.

The invention relates to the technical field of household appliances, and discloses an air outlet device and a household appliance, the air outlet device comprises a shell and an airflow adjusting device arranged in the shell, the shell is provided with an air inlet and at least two air outlets, and the at least two air outlets discharge air in different directions; the airflow adjusting device can generate airflow and convey the airflow to the at least two air outlets. Through the at least two air outlets formed in the shell, air can be discharged in different directions, the air supply range of the air outlet device is expanded, the whole house can be rapidly heated or cooled, the use requirement of a user for rapidly reaching the set temperature is met, and the problems that in the prior art, an air outlet device is single in air outlet direction, limited in air supply range and poor in practicability are solved. And the use experience is not good.

The invention provides an indoor temperature field prediction method and system considering dynamic change of solar radiation, and belongs to the field of indoor temperature field prediction.The method comprises the steps that an equivalent surface heat source intensity model of direct solar radiation and an equivalent surface heat source intensity model of scattered solar radiation are constructed respectively; respectively constructing a sub-temperature field prediction model of direct solar radiation and a sub-temperature field prediction model of scattered solar radiation; and performing linear superposition on the sub-temperature field prediction model of the direct solar radiation and the sub-temperature field prediction model of the scattered solar radiation according to moments to obtain an indoor temperature field space-time distribution prediction result under the influence of dynamic solar radiation. The method fully considers the influence of the dynamic change of the solar radiation, can predict the influence rule of the solar radiation on the indoor temperature distribution in the time and space dimensions, and further obtains an accurate prediction result.

The utility model provides a rotary hot-blast stove with an adjustable air outlet angle, which relates to the technical field of rotary hot-blast stoves and comprises a hot-blast stove body, an adjusting mechanism is arranged at the top of the hot-blast stove body and comprises a fixing pipe, a sleeve block is sleeved on the outer surface of the fixing pipe and is fixedly connected with the fixing pipe, and the air outlet angle of the hot-blast stove is adjustable. A rotating pipe is connected into the sleeve block in a sleeved mode, the rotating pipe is rotationally connected with the sleeve block, and a ratchet wheel is arranged in the sleeve block. According to the hot-blast stove, by arranging the adjusting mechanism, the blowing angle of the hot-blast stove can be adjusted, so that the blowing angle cannot be blocked by part of objects in actual use, it is guaranteed that hot air can be conveyed to the maximum extent, meanwhile, the air outlet angle of the rotating pipe can be adjusted, and the installation limitation of the hot-blast stove is smaller; and the ratchet wheel and the pawl are arranged, and the pawl can limit the ratchet wheel under the action of the first spring.

The invention relates to the technical field of graphene electric heating floor heating, and discloses a graphene electric heating floor heating intelligent temperature control system and method.The system comprises an area temperature recognition module, an instruction analysis module, an energy consumption division module and the like; the thermal field monitoring module collects basic temperature data, a real-time power curve and thermal imaging data, the energy consumption division module judges the temperature and energy consumption grade, the temperature control strategy module generates a dynamic adjustment strategy according to the grade, and the temperature difference judgment module and the path correction module achieve power correction and path optimization. And the thermal field analysis module is combined with the user terminal to early warn abnormity and visualize thermal imaging data. According to the scheme, regional refined temperature control, dynamic energy consumption management and intelligent path planning are achieved, the heating comfort level and the energy utilization rate are improved, and the method is suitable for intelligent temperature control scenes of graphene electric floor heating.

According to the PLC thermal control system of the heat exchange station, various high-precision sensors, such as a temperature sensor, a pressure sensor, a flow sensor and an outdoor environment sensor, are arranged at key positions of the heat exchange station, and operation parameters in the station and external environment data are collected in real time. The data can be quickly transmitted to the PLC control unit, the PLC preliminarily processes and analyzes the data, and multiple energy devices of the heat exchange station are intelligently dispatched in combination with a multi-energy complementary cooperative regulation and control mechanism. For example, when it is predicted that the heat load requirement is high, the system can preferentially start the gas-fired boiler with the high efficiency and coordinate auxiliary energy equipment such as the ground source heat pump and the air source heat pump to work cooperatively, so that it is ensured that the heat load requirement is met, and meanwhile the energy utilization efficiency is improved to the maximum extent.

The invention provides a centralized heating method and system based on room temperature feedback regulation, and the method comprises the steps: collecting and verifying real-time room temperature data, and generating a room temperature feedback signal; the signals are transmitted to a central control unit for sequential processing, and a temperature-time two-dimensional data matrix is generated; a preset dynamic compensation algorithm is used for calculating a heat demand correction value of each heat supply pipe network key node; the heat supply pipeline flow is dynamically adjusted according to the correction value; and verifying the effect through a closed-loop feedback mechanism and optimizing algorithm parameters. According to the method, a network is constructed to realize multi-node synchronous acquisition and verification, and heat distribution heterogeneity and fluctuation characteristics are captured in combination with matrix processing and interpolation completion. Compared with a traditional single-point sensor, according to the scheme, through two-dimensional mapping and anomaly detection, the adjustment response time is shortened to the sampling period level, the temperature deviation calculation precision is improved to + / -0.1 DEG C level, the overheating-underheating alternating phenomenon is solved, and accurate dynamic matching of the heat demand correction value is achieved.

The invention discloses a heat pump waste heat recovery cooperative heat supply method and system, and relates to the technical field of intelligent heat supply control, and the method comprises the steps: collecting environment data and heat pump operation data through a sensor, and carrying out the data preprocessing; modeling the operation state of the heat pump system by using a gradient lifting tree algorithm, constructing a heat pump load prediction model, and outputting load prediction; in combination with the load prediction result and the heat pump operation data, a collaborative heat supply strategy is obtained through an optimization algorithm; and waste heat recovery scheduling is carried out according to the load prediction result and the current waste heat recovery condition. According to the method, the accuracy and the real-time performance of load prediction can be remarkably improved, heat source configuration and heat supply requirements are highly matched, the waste heat utilization rate is improved to the maximum extent, power consumption is reduced, meanwhile, the indoor comfort degree and the system operation stability are guaranteed, finally, the operation cost is remarkably reduced, and the service life of equipment is prolonged.

The application provides a heat supply network adjusting method and device, which comprises the following steps: determining the proportion of resident users in a heat supply area; when the proportion of resident users is less than a proportion threshold, predicting the heat supply load of the heat supply area to obtain the predicted heat supply load of the heat supply area; when the proportion of resident users is greater than or equal to the proportion threshold, determining the area type of the heat supply area; determining the heat supply satisfaction degree based on the area type of the heat supply area; predicting the load of resident users and industrial users to obtain the predicted resident load and the predicted industrial load; correcting the predicted resident load by the heat supply satisfaction degree, and determining the predicted heat supply load of the heat supply area by the predicted industrial load and the corrected predicted resident load; comparing the predicted heat supply load with the actual heat supply load of the heat supply network, and controlling the distributed energy storage system to adjust the heat supply network according to the comparison result, so that the time lag of the heat supply network regulation is shortened, and the response capacity of the heat supply network is improved.