105741results about "Lighting and heating apparatus" patented technology

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

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

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

The invention discloses an integrated cold station intelligent control system based on multi-modal data fusion, and relates to the technical field of data fusion, an intelligent decision and optimization module adopts reinforcement learning and model prediction control based on fused data to realize dynamic optimization of start and stop of a water chilling unit, chilled water temperature setting and water pump frequency adjustment; the fault detection and adaptive optimization module evaluates the data fusion accuracy by calculating an environment entropy value and cold station dynamic response delay time, and dynamically adjusts a control strategy based on a prediction deviation rate and the data fusion accuracy; the closed-loop feedback module analyzes and adjusts control parameters through operation data, intelligent control models between different cold stations are optimized through transfer learning, intelligent deployment of a new cold station is accelerated, and the generalization ability of the models is improved. More accurate, more energy-saving and more stable intelligent control of the cold station is realized, and the operation efficiency and reliability of the cold station system are improved.

The invention discloses a central air conditioner load self-adaptive regulation and control method based on artificial intelligence, which relates to the technical field of heating ventilation air conditioner automatic regulation and control, and comprises the steps of edge end data preprocessing, deep learning load prediction and self-adaptive updating, intelligent control strategy, closed-loop feedback and continuous optimization and fault diagnosis and remote operation and maintenance. Through edge end denoising and feature labeling, the data transmission pressure is remarkably reduced, and the response speed is increased; the deep learning model is combined with online iteration and reinforcement learning, key parameters such as chilled water temperature and water pump frequency are accurately regulated and controlled, and energy consumption and comfort are considered; according to the method, prediction errors and equipment states are monitored, a model and a control strategy are synchronously updated when abnormity is detected, fault early warning is triggered in combination with health assessment, energy consumption is effectively reduced, the service life of equipment is prolonged, and finally an efficient and stable self-adaptive regulation and control system capable of being operated and maintained online in a long period is constructed.

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 intelligent linkage control management system based on air conditioner application, and relates to the technical field of intelligent control and energy management, and the system comprises a load prediction module which obtains an air conditioner load demand prediction value through an intelligent load prediction model based on preprocessed environment energy consumption data; the optimization scheduling module is used for optimizing the current air conditioner operation mode through the building energy management system based on the air conditioner load demand prediction value, and obtaining an optimized air conditioner operation scheduling instruction; the intelligent adjusting module is used for intelligently adjusting the operation parameters of the air conditioner system based on the optimized air conditioner operation scheduling instruction, adaptively adjusting the energy efficiency of the air conditioner and obtaining operation feedback data; and the fault management module is used for identifying potential fault risks of the air conditioner and formulating fault risk prevention measures through a time sequence analysis method based on the operation feedback data. According to the invention, through innovation of an intelligent load prediction and fault prevention mechanism, the energy utilization efficiency and the system stability are effectively improved.

A system and method for HVAC optimization is disclosed. The system may include a digital twin (112) of an HVAC system (114) installed in a premises (108). The digital twin (112) is based on design parameters, operational data, and PMV-based thermal comfort analysis. The system further includes an RL agent (202) that is configured to receive and process at least real-time environmental conditions, occupancy patterns, and PMV value associated with the premises (108) of the HVAC system (114), identifying one or more optimal actions to adjust one or more HVAC parameters. The digital twin (112), driven by the identified optimal actions, may dynamically simulate the HVAC system (114) and the RL agent (202) may optimize operations of the HVAC system (114) based on the identified optimal actions, ensuring seamless and adaptive thermal comfort and energy efficiency management within the premises (108).

A modular box assembly includes a box defining a box cavity within the box, the box further defining a top end and a bottom end, the top end defining a box opening connected to the box cavity, the box including a side panel extending from the top end to the bottom end; a shoulder attached to the side panel, the shoulder extending inward from the side panel and into the box cavity, the shoulder including a first sub-shoulder and a second sub-shoulder, the first sub-shoulder, the second sub-shoulder, and the side panel defining a shoulder channel; and an inner box disposed in the box cavity, the inner box including a channel tab, the channel tab frictionally engaging the shoulder channel to secure and suspend the inner box within the box cavity between the top end and the bottom end.

An insulated box assembly comprising an outer box comprising a plurality of outer lateral sidewalls, the outer lateral sidewalls defining an inner surface, the inner surface defining an inner cavity; an inner box comprising a plurality of inner lateral sidewalls, the inner box received in the inner cavity, the inner lateral sidewalls defining an outer surface; a sidewall liner received in the inner cavity between the outer lateral sidewalls and the inner lateral sidewalls, the sidewall liner defining a liner outer surface and a liner inner surface opposite the liner outer surface; and a lid assembly disposed within the inner cavity, the lid assembly comprising a lid box and a lid liner.

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 an intelligent monitoring method and system for heating and ventilation of a thousand-level dust-free workshop based on digital twinning. The method specifically comprises the steps that SolidWorks software is used for conducting three-dimensional modeling on heating and ventilation equipment and environment of the thousand-level dust-free workshop; a physical device, an IoT sensor network, a PLC controller and a data transmission module are configured to realize real-time acquisition, processing and transmission of a device operation state and environmental parameters; dynamically optimizing the operation state of the equipment and the environment parameters of the workshop; a three-dimensional visual interface is developed based on an OpenUSD platform, and heating and ventilation equipment and environmental parameters in a workshop are visually presented in a dynamic and real-time mode; an equipment health state curve is generated through a time sequence prediction algorithm, and the equipment health score and trend change are visually displayed, so that a user can grasp the equipment operation state in real time. Accurate control over the temperature and humidity, the cleanliness and the pressure difference in the workshop is achieved, the cleanliness and reliability of the production process are improved, and good adaptability and expansibility are achieved.

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.

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

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

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

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

A modular box assembly includes a box defining a box cavity within the box, the box further defining a top end and a bottom end, the top end defining a box opening connected to the box cavity, the box including a side panel extending from the top end to the bottom end; a shoulder attached to the side panel, the shoulder extending inward from the side panel and into the box cavity, the shoulder including a first sub-shoulder and a second sub-shoulder, the first sub-shoulder, the second sub-shoulder, and the side panel defining a shoulder channel; and an inner box disposed in the box cavity, the inner box including a channel tab, the channel tab frictionally engaging the shoulder channel to secure and suspend the inner box within the box cavity between the top end and the bottom end.

An insulated box assembly can include a box including a pair of opposing side box panels, a pair of opposing main box panels, and a bottom box panel, the box defining an internal box cavity with a box opening defined opposite from the bottom box panel; and an insulated liner positioned within the internal box cavity, the insulated liner including a pair of opposing main liner panels and a pair of opposing side liner panels, the pair of opposing main liner panels and the pair of opposing side liner panels defined by a single blank liner panel including a blank sheet and an insulation batt, the insulation batt positioned between the blank sheet and the box.

The invention provides a subway station central air conditioner full-link energy-saving system and method based on wind balance. Belongs to the technical field of air-conditioning energy conservation. The method comprises the steps that environmental data inside and outside a subway station are collected in real time through a sensor network, and the collected environmental data are uploaded to a central air conditioner full-link energy-saving control platform; the central air conditioner full-link energy-saving control platform dynamically adjusts the air volume of the air supply pipe and the air return pipe through fan control according to the collected data; by collecting environment data in real time and combining an advanced control algorithm, the system can dynamically adjust the rotating speed of a fan, the load of a refrigerating unit, the ratio of fresh air to return air and the like, so that the running state of the air conditioning system is optimized, energy consumption is effectively reduced, and the overall energy-saving effect of the system is 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 energy-saving control method and system for an airplane ground air conditioning unit, and belongs to the technical field of air conditioning system energy saving.The method comprises the steps that internal and external environment parameters of a cabin are collected in real time, temperature prediction is conducted based on the internal and external environment parameters of the cabin, and a predicted temperature change curve is constructed; running state parameters of the aircraft ground air conditioning unit are called, dynamic energy-saving control is carried out in combination with the predicted temperature change curve, and an energy-saving control strategy is formulated; cabin internal variable parameters are introduced to optimize the energy-saving control strategy, and an optimized energy-saving control instruction set is generated; and the optimized energy-saving control instruction set is executed for cooperative driving, cooling circulation is conducted according to the cooperative driving result, and self-adaptive energy-saving control over the aircraft ground air conditioning unit is achieved. The technical problems of high energy consumption, unstable temperature control and low equipment operation efficiency caused by the lack of precise response capability of an air conditioning unit to dynamic changes of internal and external environments of a cabin in the prior art are solved.

A box assembly can include an exterior piece including a first middle portion, a second middle portion, a third middle portion, and a fourth middle portion; the first middle portion and the third middle portion positioned perpendicular to the second middle portion and the fourth middle portion; a plurality of insulator pads including a first side insulator pad, a second side insulator pad, and a third side insulator pad; the first side insulator pad contacting the first middle portion; the second side insulator pad contacting the first middle portion, the second middle portion, and the third middle portion; the third side insulator pad contacting the third middle portion; and an interior piece positioned within the exterior piece, the interior piece including a first side panel and a second side panel; the first side insulator pad positioned between the first side panel and the first middle portion.

A box assembly can include an exterior piece including a first middle portion, a second middle portion, a third middle portion, and a fourth middle portion; the first middle portion and the third middle portion positioned perpendicular to the second middle portion and the fourth middle portion; a plurality of insulator pads including a first side insulator pad, a second side insulator pad, and a third side insulator pad; the first side insulator pad contacting the first middle portion; the second side insulator pad contacting the first middle portion, the second middle portion, and the third middle portion; the third side insulator pad contacting the third middle portion; and an interior piece positioned within the exterior piece, the interior piece including a first side panel and a second side panel; the first side insulator pad positioned between the first side panel and the first middle portion.

The present disclosure provides a neck fan. The neck fan includes a hanging shell and a first fan assembly. The hanging shell includes an inner end portion that is in contact with the neck. A first air guide chamber having a first central axis is arranged on the hanging shell in a protruding manner. The first air guide chamber includes a first air inlet arranged at a lower end portion of the hanging shell and a first air outlet opposite to the first air inlet. The first air outlet is oriented towards the inner end portion. Both the first air inlet and the first air outlet are located on the first central axis. The first fan assembly is located in the first air guide chamber. And the first fan assembly is configured to drive an air flow to flow from the first air inlet to the first air outlet.

A repulpable insulated container assembly having a container formed of paper such as corrugated cardboard or varying paper materials and defining an interior; and a repulpable insert placed within the interior of the container and formed of a first paper layer; and a paper fiber pad coupled to the first paper layer.

The invention discloses an electronic nose device which comprises a shell, a detection assembly, an air curtain cover and a power assembly. The shell is provided with an air inlet channel and an air outlet channel surrounding the periphery of the air inlet channel. The detection assembly is arranged in the air inlet channel and is used for qualitatively and quantitatively detecting the components of the passing to-be-detected gas. The air curtain cover is installed on the shell, an air inlet of the air curtain cover communicates with the air inlet channel, and an air outlet surrounds the air inlet and communicates with the air outlet channel. The power assembly is divided into two parts, one part is located in the air outlet channel and used for sucking in external air, the external air is exhausted through an air outlet of the air curtain cover, and a hollow cylindrical or conical air curtain is formed. And the other part is positioned in the air inlet channel and is responsible for introducing the to-be-detected gas wrapped by the air curtain into the air inlet channel from the front of the air outlet of the air curtain cover. The electronic nose device can effectively improve the detection efficiency of the to-be-detected gas, ensures that the detection process is quicker and more accurate, and meets the practical requirements of adjustable orientation and more accurate positioning for gas detection in practical application.

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.