26334results about "Space heating and ventilation details" 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 discloses a heating control system based on solar energy-heat pump-building collaborative optimization and a construction method, and belongs to the technical field of renewable energy source heating. Comprising a parameter acquisition module, a dynamic prediction module and an optimization control module. The method comprises the following steps: constructing a building heat transfer dynamic prediction model, a solar energy performance prediction physical model, constructing a ground source heat pump performance prediction model and a solar energy-ground source heat pump system collaborative optimization control model, and predicting and optimizing system operation parameters in the next day. Through cooperative work of the parameter acquisition module, the dynamic prediction module and the optimization control module, intelligentization and high efficiency of building heating are achieved, solar energy and the heat pump efficiency can be utilized to the maximum extent, the ground source heat pump efficiency is improved, and energy consumption is reduced while the indoor comfort degree is guaranteed. In addition, a time-of-use electricity price mechanism is utilized to further optimize system operation parameters, the system operation cost is reduced, and the energy efficiency and the response capability of the system are 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.

A refrigeration fan with lateral heat dissipation is provided, including a fan head and a hand-held handle. A conductive sheet is laid on an outer surface of the hand-held handle, a cavity is arranged in the hand-held handle, and a cold-heat generation apparatus is mounted in the cavity. The hand-held handle is provided with an air inlet and an air outlet, a refrigeration end of the cold-heat generation apparatus is connected to the conductive sheet in a heat conduction manner, and a heat dissipation end of the cold-heat generation apparatus communicates with the air outlet.

The present disclosure relates to a system for utilizing waste heat of a data center. The system comprises a first heat exchange module, a second heat exchange module, a heat storage and extraction flow path, a heat supply flow path and a buried pipe, wherein the first heat exchange module is used for connecting to a data-center heat dissipation system for dissipating heat of a data center, and both the heat storage and extraction flow path and the heat supply flow path are connected to the first heat exchange module, so that the first heat exchange module can transfer, to the heat supply flow path and / or the heat storage and extraction flow path, heat which is generated by the data center and absorbed by the data-center heat dissipation system; the second heat exchange module is connected in the heat storage and extraction flow path and the heat supply flow path, so that heat exchange can be performed between the heat storage and extraction flow path and the heat supply flow path; and the heat storage and extraction flow path is provided with the buried pipe, which is configured to be buried below the ground surface, and is used for storing, in soil below the ground surface, the heat which is transferred from the data-center heat dissipation system to the heat storage and extraction flow path, or for transferring, to the heat supply flow path via the second heat exchange module, the heat which is stored in the soil below the ground surface.

The invention relates to the technical field of air purification and discloses an air handling unit which comprises an air return unit, a first fan is arranged in an inner cavity of the air return unit, a fresh air unit is fixedly installed on the lower surface of the air return unit, and a second fan is arranged in an inner cavity of the fresh air unit. By means of the filtering assembly, a filtering net can be automatically cleaned when the filter is blocked, dust can be collected in a centralized mode during cleaning, secondary blocking of the collected dust can be reduced, meanwhile, the filter is limited in a flexible fixing mode, and therefore the risk that the filter is damaged due to vibration is reduced, and the service life of the filter is prolonged. The filter can be quickly disassembled and assembled through the flexible fixing mechanism, the filter can be conveniently maintained, and the pressure intensity change in the air handling unit can be timely and accurately observed through the pressure detection unit, so that the air handling unit can be timely maintained when the pressure intensity of the air handling unit is changed.

The invention provides a cross-seasonal heat storage coupling solar ground source heat pump combined heating system. The cross-seasonal heat storage coupling solar ground source heat pump combined heating system mainly comprises a solar heat collector, a valve, a heat storage water tank, a heating tail end, a buried phase change heat storage water tank, a buried pipe and a heat pump. Solar radiation heat in non-heating seasons is collected and stored through cross-seasonal heat storage, and the stored heat is extracted in the heating period in winter and is used for heating a building together with a solar ground source heat pump combined heating system. The system has the beneficial effects that the system efficiency is improved, and the use of thermal insulation materials is reduced, so that the cost is reduced; the buried pipe absorbs and inhibits heat diffusion of the heat storage water tank, heat balance of a soil temperature field around the buried phase change heat storage water tank is guaranteed, and efficient operation of a heat pump unit is maintained. The invention has six control methods: solar energy-soil heat storage, solar energy direct heating, solar energy coupling buried phase change heat storage water tank heating, buried phase change heat storage water tank direct heating, solar energy ground source heat pump combined heating and ground source heat pump independent heating.

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 relates to the technical field of intelligent control, in particular to an intelligent control method and system for heat supply parameters of a thermal power plant, and the method comprises the following steps: capturing temperature sensor data of multiple key parts of the thermal power plant, combining operation time with external weather condition data, carrying out data integration, analyzing the current operation state, and obtaining heat balance basic data. According to the method, comprehensive analysis is carried out in combination with external weather conditions, dynamic adjustment of heat supply parameters is achieved, the reaction speed is increased, the fuel ratio and load are optimized, the energy utilization efficiency is improved, long-term weather prediction data are integrated, heat supply demand changes are predicted and adapted, energy waste is reduced, and the energy utilization efficiency is improved. Real-time load adjustment and fuel efficiency calculation enable the thermal power plant to accurately match the actual heat demand, the combustion efficiency is optimized, and by predicting maintenance points and potential faults, the equipment operation reliability is improved, the maintenance plan is updated in time, the production safety is ensured, the operation flexibility and economic benefits are improved, and the environmental pollution risk is reduced.

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.

Device for dehumidifying a closed casing.The device for dehumidifying comprises a box comprising an inlet communicating with the interior of the closed casing and an outlet, a heating module able to be controlled to generate heat, a desiccant configured to adsorb moisture when it is not heated by the heating module and to return adsorbed moisture by desorption when it is heated by the heating module, a mobile plug able to be moved to alternately open and close the inlet and the outlet, a bias spring configured to move the mobile plug when the heating module does not heat the desiccant and a shape memory alloy spring configured to move the mobile plug when the heating module heats the desiccant, said shape memory alloy spring being able to be heated by the heating module at the same time as the desiccant. The device for dehumidifying is suited for dehumidifying any type of closed casing, it is cost efficient both in design and in power consumption, and it is easy to implement thanks to its small size and its simple design.

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

The invention relates to 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 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 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.

According to the direct-expansion cryogenic pre-dehumidification and heat pump regeneration combined low-dew-point double-rotating-wheel dehumidification system, the control method and the terminal, accurate regulation and control of all devices in the rotating-wheel dehumidification system are achieved according to data collected by the sensor module in real time, and therefore the stability of the air supply dew-point temperature is ensured. By adopting the direct expansion cryogenic technology, the dehumidification burden of the rotating wheel is effectively relieved, and the regeneration temperature is reduced, so that the energy consumption in the regeneration process is remarkably reduced. The regeneration heat pump condenser is installed at the inlet of the rotating wheel, part of regeneration heat can be borne, and regeneration energy consumption is further reduced. A regeneration heat pump evaporator is further installed at an exhaust outlet of the rotating wheel, heat in regeneration exhaust can be recycled, and the purpose of saving energy is achieved. According to the rotating wheel dehumidification system, dual optimization of dehumidification performance and energy consumption performance is achieved, the dehumidification efficiency is remarkably improved, and the energy consumption of the system is reduced.

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 present invention is applicable to the technical field of fresh air equipment, and provides a fume removal catering fresh air equipment, including a chassis, a total heat exchanger is arranged inside the chassis, primary air filters are arranged on the left and right sides of the total heat exchanger, secondary air filters are arranged inside the old air inlet channel and the new air exhaust channel, the secondary air filter and the primary air filter are detachably installed in the chassis, the secondary air filter and the primary air filter include a filter mechanism and a lifting and adjusting mechanism for driving the filter mechanism to move up and down in the chassis: the present invention arranges a secondary air filter and a primary air filter that are detachably installed in the chassis, the secondary air filter and the primary air filter include a filter mechanism and a lifting and adjusting mechanism for driving the filter mechanism to move up and down in the chassis, when the filter is dismantled for inspection or cleaning, it is only necessary to remove the filter through the inspection port, so that the replacement and cleaning of the filter are convenient, and the use effect and service life of the fresh air equipment are improved.

The invention relates to the technical field of air purification, in particular to movable air purification equipment. The device comprises a filtering device, and a dredging device and a collecting device which are arranged in the filtering device. According to the air purifier, when the motor drives the fan blades to rotate through the transmission shaft so as to accelerate the speed of air passing through the air filter element, the control panel in reciprocating threaded connection with the transmission shaft can drive the elastic air bag to contract and stretch, the volume of the elastic air bag is increased during stretching, and negative pressure is formed in the elastic air bag to suck air purified by the air filter element; during compression, the size of the elastic air bag is reduced, air in the elastic air bag is compressed, enters the air injection structure through the exhaust pipe and then is sprayed out of the filter screen through the air flushing opening, so that blockages on the filter screen are impacted by airflow to fall off from the filter screen, the filter screen is prevented from being blocked, and the air purification efficiency of the equipment is ensured.

The present invention relates to the field of fuel cells and cogeneration, and in particular to a fuel cell waste heat utilization system and a control method thereof. The present application aims to solve the problems of low waste heat utilization efficiency and precise control of user-end heat temperature in existing fuel cell cogeneration systems. The fuel cell cogeneration system of the present application includes: a fuel cell stack, a stack heat balance device, a waste heat utilization device, an energy storage device, and a control device. By regulating the flow of the water pump in the waste heat control device and the angle of the electric control valve, the flow of heat exchange to the water tank and the heater is adjusted, thereby achieving precise control of the user-end temperature. By dynamically switching between multiple modes such as electricity demand priority, heating priority, and hot water storage priority, the efficient operation of the fuel cell system and the maximum utilization of waste heat are ensured.

The invention provides an underground heat supply system based on an air energy heat pump as a heat source and a remote monitoring method, and the system comprises a heat source dynamic tuning subsystem which carries out the dynamic parameter optimization processing of environment temperature and energy storage state data, and obtains an air energy heat pump operation instruction; first characteristic adjustment of air energy heat pump output is formed through an air energy heat pump operation instruction, and matching of the temperature of an energy storage water tank and an environment heat source is achieved. The underground hot air accurate adjusting subsystem obtains underground real-time temperature data, the underground real-time temperature data is subjected to spatial thermal field calculation processing to obtain air volume demand parameters, and dynamic balance between the air supply intensity and the underground thermal environment is formed; and the multi-stage cooperative control center subsystem performs three-stage logic verification processing on the signals of the air energy heat pump operation instruction and the fan frequency instruction to obtain an equipment regulation and control instruction set. The underground intelligent heat supply system of the air energy heat pump, which has the environment self-adaption capability, the dynamic balance capability of a thermal field and the remote monitoring characteristic, is constructed.