569 results about "Thermal comfort" patented technology

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention provides an energy-saving and carbon-reducing multi-objective optimization method and device for a cold source system of a high-speed rail station, and the method comprises the steps: building an air-conditioning cooler system load prediction model and a human body comfort model based on the historical data of an air-conditioning cooler system, the historical data of the internal and external environments of the high-speed rail station, and the thermal comfort evaluation historical data of the internal environment of the high-speed rail station; a multi-objective optimization algorithm is established with minimization of the sum of the air conditioner refrigerator system load and the human body comfort as an optimization objective, and the weight of the air conditioner refrigerator system load and the human body comfort can be adjusted according to real-time data of the internal environment and the external environment of the high-speed rail station; and a multi-objective optimization algorithm is solved, and cold machine optimization control is carried out according to the air conditioner cold machine system load solution value and the air conditioner cold machine system load current value, so that the reliability of energy-saving and carbon-reducing multi-objective optimization of the high-speed rail station cold source system is effectively improved.

The invention relates to an intelligent energy-saving method for a large building refrigerating system based on a large language model, and belongs to the field of intelligence of large building refrigerating systems. A reinforcement learning framework is constructed, a multi-dimensional state space formed by building thermal loads, equipment operation states and thermal environment parameters is defined, and a dynamic reward mechanism is designed by taking improvement of energy efficiency, guarantee of thermal safety constraints and maintenance of thermal comfort as optimization objectives; developing a word embedding model special for the refrigeration field and a cross-modal decision conversion model, and realizing bidirectional analysis of a natural language instruction and a physical control parameter through feature coding and embedding mapping; and performing low-rank adaptive fine tuning on the pre-trained large language model based on the reinforcement learning experience set, and constructing a closed-loop verification system. Generalized migration and multi-target collaborative decision-making of equipment control strategies are achieved, the self-adaptive regulation and control capacity of a refrigeration system under dynamic loads is improved, and a universal intelligent solution for energy efficiency optimization, thermal safety and comfort is provided for a large building.

The invention relates to the technical field of constructional engineering, and discloses an intelligent ventilation and purification method and system for constructional engineering, and the method comprises the steps: 1, determining the concentration of pollutants in a building based on real-time air quality data, and calculating the required ventilation quantity meeting a preset standard; secondly, the fresh air introduction proportion is adjusted according to the indoor and outdoor temperature and humidity difference value so as to optimize energy consumption and thermal comfort balance; 3, according to the personnel distribution density, the local area air supply intensity is set, and the air circulation uniformity is ensured; fourthly, the system operation mode is regulated and controlled based on the working state of the air purification equipment, so that energy consumption is reduced, and the service life of the equipment is prolonged; the fresh air introduction proportion is intelligently adjusted by comparing indoor and outdoor temperature and humidity differences. For example, when the outdoor temperature and humidity are close to the indoor comfortable interval, the fresh air volume is increased to utilize natural cold and heat sources; when the outdoor environment is extreme, the fresh air volume is reduced, the heat recovery device is started, the air conditioner load is reduced, and energy waste is reduced.

The invention provides an intelligent adjustment method for distributed air treatment equipment, and belongs to the technical field of air treatment equipment, and the method comprises the steps: inputting a personnel distribution image into an airflow field reconstruction model based on a graph neural network, outputting three-dimensional space flow field distribution data, calculating a personnel intensity index, and predicting an average thermal sensation index value; an air supply adjustment double-layer game model is established based on the data, the air supply angle and the air supply speed of an electric side supply nozzle and the air supply quantity of a lower air supply outlet are solved, an actuator is controlled to adjust air supply parameters, and the target air outlet temperature and the refrigerant flow of the heat exchanger are calculated through a thermodynamic entropy production minimization heat exchange optimization algorithm; and feedback correction is performed according to the actually predicted average thermal sensation index deviation to form closed-loop control, so that the technical problem that the distributed air treatment equipment is difficult to adjust the air supply parameters in real time according to the personnel distribution dynamic change so as to ensure the thermal comfort and the energy efficiency at the same time is solved.

The invention relates to the crossing field of biothermodynamics and dynamic environment control, and discloses a space thermal microclimate multi-target regulation and control method, device and system, a medium and a product. The method comprises the following steps: acquiring physiological parameters (including effective dressing thermal resistance and local sweat gland density of posture correction) of a passenger, environment space-time parameters and light environment parameters through a biological characteristic sensor, an environment sensor and an optical sensor; constructing a dynamic human body thermal comfort model library, and adopting a feature coding-parameter decoding mechanism to match a target thermal comfort model; generating a hierarchical regulation strategy based on the difference between the local comfortable temperature and humidity interval and the real-time thermal environment sensing parameter; and the refrigerant circulation system, the fluid circulation system and the optical radiation system are cooperatively controlled to realize local microclimate regulation and control. According to the method, the problems of inaccurate thermal comfort modeling and low multi-system cooperation efficiency in a transient environment are solved, the energy efficiency ratio is improved, and the comfort of passengers in a cabin is remarkably improved.

The invention discloses a thermal comfort monitoring robot system and method based on multi-modal body language feature recognition and fusion, and aims to realize individual thermal comfort monitoring, regulation and control in a dynamic indoor environment. The intelligent mobile platform carries multiple types of environment sensors and visual acquisition equipment, and performs autonomous navigation and acquires environment parameters and human body image data; the multi-modal feature recognition module performs human body detection, posture recognition and identity maintenance on a video stream frame by frame based on a deep learning network, and extracts multi-modal human body features; the data processing and fusion module realizes cross-frame identity consistency through an algorithm and fuses multi-modal human body features and environmental parameters; the thermal comfort calculation module dynamically adjusts a metabolic rate parameter based on a predicted average voting model, incorporates the metabolic rate parameter into a psychological correction factor and calculates an individual thermal comfort index; the three-dimensional visualization and regulation and control module generates a comfort distribution map, is in butt joint with a building environment automatic control system to achieve dynamic regulation and control, and provides support for intelligent building environment management.

The invention provides an intelligent control system and method for a natural cooling heat exchange ventilation unit, and relates to the technical field of energy-saving fans. The optimal operation mode and the natural cooling availability of a target ventilation unit are determined firstly; the expected heat exchange amount of the natural cold side is determined according to the current indoor heat load demand characteristics and the natural cold available capacity, and then a static adjusting instruction of a natural cold side actuator is determined according to the expected heat exchange amount; according to the heat load demand characteristics and the expected heat exchange amount, the mechanical refrigeration demand cold capacity is determined, and then a dynamic adjusting instruction of a mechanical refrigeration side actuator is determined according to the heat exchange efficiency and the mechanical refrigeration demand cold capacity of the mechanical refrigeration unit under the current working condition; and performing coordination control on the actuators on the natural cooling side and the mechanical refrigeration side based on the static adjustment instruction and the dynamic adjustment instruction. According to the scheme, dynamic cooperative control over natural cooling and mechanical cooling of the natural cooling heat exchange ventilation unit under the variable working conditions can be achieved, and therefore the energy efficiency is optimized on the premise that the requirement for indoor thermal comfort is met.

The invention relates to an electrochromic window multi-scene control method, system and device based on indoor human body thermal comfort and a medium. The method comprises the steps that a perception data set is constructed; based on the sensing data set, performing multi-parameter coupling calculation of solar incidence azimuth deviation constraint and dynamic projection overlapping to obtain a direct radiation component, performing window space coupling analysis and double space compensation calculation to obtain a scattered radiation component, and performing fusion to obtain an individual thermal radiation influence value; determining a scene category based on the perception data set, and establishing a thermal radiation evaluation benchmark by adopting a mode matched with the scene category; and if the thermal radiation evaluation benchmark exceeds a preset threshold value, the required transmissivity of the window faces is obtained through a full-transmissivity radiation distribution method, the corresponding window faces are adjusted to the target transmission gradient according to the required transmissivity, and coupling control over the wind direction and / or power of the air conditioning system is selectively triggered. According to the invention, by constructing a multi-dimensional dynamic sensing system and an intelligent control framework, collaborative optimization of human body thermal comfort and building energy efficiency is realized.

The invention discloses an optimal control method and system for a central air conditioner. The method comprises the steps that a starting instruction is responded; selecting an operation parameter combination from a preset database and sending a test request to the central air-conditioning system; energy efficiency detection results are received, wherein the energy efficiency detection results comprise energy consumption, equipment efficiency and user thermal comfort indexes; determining a next round of test combination based on the detection result and the mapping relation table; and selecting the combination with the highest energy efficiency from the tested combinations as a final result and configuring the final result to the system. Energy efficiency self-adaptive optimization is achieved by establishing the preset database and the mapping relation table in combination with a multi-round test mechanism, the test is intelligently guided according to the divided energy efficiency value interval, blind traversal is avoided, and the search efficiency is improved. A termination test strategy is set to effectively control a period and reduce interference, a load prediction model is introduced to make optimization more targeted, energy waste caused by lagging response is avoided, key parameters are collaboratively optimized, comfort is improved, and energy consumption is reduced.

The invention discloses a multi-objective optimization method for the form of an urban block, and the method comprises the following steps: S100, constructing a parameterized model, carrying out the experimental design, defining a group of geometric parameters used for describing the form of the urban block, and generating a plurality of sample points in the value range of the geometric parameters through employing a sampling method; s200, bidirectional coupling numerical simulation is executed, a data set is generated, at least one thermal comfort evaluation index and one air quality evaluation index are output, and therefore the data set containing input parameters and output indexes is formed; step S300, constructing and verifying a machine learning agent model to obtain an agent model capable of rapidly predicting the evaluation index; step S400, executing multi-objective optimization search, and automatically searching to enable the thermal comfort evaluation index and the air quality evaluation index to simultaneously achieve a better Pareto optimal solution set; and step S500, analyzing and outputting an optimization result, extracting an optimization value range of the geometric parameters, and generating a design guide.

According to the data center cluster two-stage energy sharing method based on the multi-element load comprehensive demand response, the first stage is a day-ahead optimization stage, uncertainty probability distribution of wind power, photovoltaic power generation and loads is fully considered in the stage, and a shared energy storage (SES) optimization scheduling method based on a conditional worst-at-risk value (WCVaR) is constructed. The second stage is a real-time optimization stage: firstly, designing a novel point-to-point (P2P) transaction mechanism based on an electricity and heat supply-demand ratio so as to realize joint sharing of electricity and heat between DCPs; then, a refined IDR model is provided, the model considers the space-time transferability of data loads and the flexibility, the thermal hysteresis effect and the thermal comfort of the household appliances, and indexes such as the efficiency improvement ratio are introduced to evaluate the IDR model; and finally, constructing a benefit function of the DCCO and the DCPs.

The invention provides a photovoltaic air conditioner and a control method and system thereof, and belongs to the technical field of photovoltaic air conditioners. The control method comprises the steps that the thermal comfort state in a building, the real-time operation power of the photovoltaic air conditioner and outdoor state parameters are monitored; calculating predicted power generation power and predicted photovoltaic air-conditioning power; calculating an absolute value of a difference value between the predicted power generation power and the predicted photovoltaic air-conditioning power, and judging whether the absolute value exceeds a preset source load power error threshold value or not; if the source load power error threshold value is exceeded, grading adjustment is carried out according to the size relation between the predicted power generation power and the predicted photovoltaic air conditioner power; and when the energy storage equipment is configured, the energy storage equipment is controlled to store or release energy. According to the method, the thermal comfort state, the photovoltaic power generation power and the air conditioner operation power are monitored in real time, graded adjustment or energy storage control is conducted on the basis of the prediction difference value, the problem of source load mismatching caused by photovoltaic output fluctuation is effectively solved with low cost, and self-adaptive tracking and efficient on-site consumption of photovoltaic fluctuation are achieved.

This invention addresses the challenge of thermal comfort in hot-humid tropical climates, where factors like solar radiation, humidity, and air temperature significantly affect indoor conditions. The patented hybrid attic ventilation technique operates continuously over 24 hours to improve ventilation in attics, which are crucial for maintaining overall building thermal performance. Field studies comparing a traditional attic setup with one utilizing the hybrid ventilation system were conducted in two typical one-storey terrace houses. Using Building Information Modeling (BIM) for energy consumption analysis, the results indicated that the hybrid system reduced indoor temperature by 1°C and relative humidity by 7%, leading to a notable 10% reduction in overall energy consumption. This innovative hybrid technique demonstrates enhanced thermal comfort and energy efficiency by leveraging both nighttime and daytime ventilation, making it a sustainable solution for residential buildings in tropical regions.

The invention provides a central air conditioner installation method and system based on an intelligent sensing mechanism, and the method mainly comprises an installation scheme set acquisition step, a heat source prediction model construction step, an installation scheme dynamic adjustment step and a scheme verification and calibration step. Cooperative optimization of a central air conditioner installation scheme on the aspects of thermal comfort, balance, energy consumption and response speed is achieved, and an efficient solution is provided for accurate deployment of an intelligent air conditioner system of a building.

The invention provides a vehicle comfort control method and device and a storage medium. Firstly, the target air outlet temperature of an air conditioner is determined; according to the human body comfort model, the radiation temperature when the fireplace or the seat is not started for heating is calculated, the thermal comfort evaluation index is calculated, the thermal comfort evaluation index is in the human body thermal comfort interval by adjusting the air conditioner outlet air temperature, and under the condition that the human body thermal comfort interval is maintained, the thermal comfort evaluation index is obtained. The corrected in-vehicle target head temperature after fireplace heating or seat heating is started is calculated based on the influence of heating of different gears of an in-vehicle fireplace or / and seats on the comfort degree of the human body, then the corrected air conditioner target air outlet temperature is determined, and the mapping relation between the different gears of the in-vehicle fireplace or seats and the corrected air conditioner target air outlet temperature is established. And the comfort level of the vehicle is controlled. Through cooperative control of the air conditioner and the fireplace or the seat, energy consumption of the air conditioner is reduced, and meanwhile the body surface temperature can be rapidly increased through fireplace heating or seat heating.

The invention discloses a central air conditioner control system which comprises at least one outdoor unit and at least one indoor unit in communication connection with the outdoor units through communication buses. The controllers can communicate with one another and are respectively connected with the communication bus; the temperature and humidity sensor is used for collecting indoor temperature data and humidity data; wherein the controller is used for storing current regional information, weather information and equipment operation records; the controller is also used for establishing a thermal comfort evaluation / recommendation model and an intelligent prediction schedule model by using a machine learning algorithm; the controller is further used for forming recommended control parameters according to the set requirements of the user and sending the recommended control parameters to the outdoor unit or the indoor unit. According to the invention, based on the information of the area where the user is located, the weather information, the indoor environment information and the user information, a machine learning algorithm is utilized to establish a thermal comfort evaluation recommendation model and an intelligent prediction schedule model, the models are combined to form a recommendation control strategy according to a scene set by the user, and optimal control parameters are provided for the user. And in cooperation with the energy-saving effect of the central air conditioning equipment, the purpose of efficient and energy-saving operation is achieved on the premise that the comfort level of a user is met.

The invention provides a predictive maintenance and energy-saving linkage method and device of an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the technical field of data processing. Core operation and environment parameters such as the start-stop frequency of the compressor are collected; monitoring the state of a key component by means of the Internet of Things and edge calculation, constructing an equipment degradation and environment association model, and generating a preliminary maintenance demand and energy consumption early warning; designing a hierarchical maintenance strategy by using a digital twinning and cost model, and outputting a target maintenance instruction and an energy-saving report; dynamically adjusting parameters to generate a linkage control scheme in combination with the thermal comfort and the peak-valley electricity price; further, a multi-device global collaboration strategy is expanded; and finally, generating the reliability level and the energy-saving benefit value of the equipment through multi-dimensional evaluation, forming a closed-loop technical system, and guaranteeing the safety and energy conservation of the equipment.

The invention belongs to the technical field of building construction, and particularly discloses a building performance multi-objective optimization method and equipment based on an intelligent algorithm. Comprising the steps of performing parametric simulation on building performance to obtain a sample data set; constructing a BO-CatBoost prediction model, predicting the building lighting performance, the building energy consumption, the building thermal comfort and the building carbon emission by adopting the BO-CatBoost prediction model, and establishing a nonlinear mapping relationship between each influence factor and the building energy consumption, the carbon emission, the lighting and the thermal comfort; and constructing a multi-target prediction function by taking different climate zone variables as constraint conditions. According to the method, efficient collaborative optimization of performance in multiple aspects of energy consumption, carbon emission, lighting, thermal comfort and the like of the building is realized, so that the building can effectively adapt to different climate zone environments while meeting multi-target performance requirements, and an efficient, universal and widely applicable optimization strategy is provided for sustainable development of the building industry.

The invention relates to an off-grid photovoltaic air conditioner self-adaptive control system and method based on rolling self-learning, and aims to solve the problems of low power supply reliability, large battery loss, inflexible temperature control, weak adaptability and the like caused by poor matching between an existing off-grid photovoltaic system and an air conditioner load. The method mainly comprises the steps that parameters are collected in real time, loads are calculated, and air conditioning unit operation frequency bands of the off-grid system are dynamically divided based on the temperature difference; constructing a multi-target model for guaranteeing power supply, prolonging the service life of the battery and optimizing the thermal comfort of the off-grid system, and adaptively adjusting the target by utilizing a dynamic weight coefficient; the optimal sequence is solved through rolling time window optimization, and the off-grid regulation and control strategy is adjusted in real time in combination with temperature gradient feedback. According to the method, the energy dynamic matching efficiency in the off-grid supply and demand system can be improved, power supply is guaranteed, fine adjustment of temperature control is achieved, harmful charging and discharging are limited based on state optimization, loss is reduced, the service life is prolonged, and compared with a traditional regulation and control strategy, the method has remarkable advantages in reliability, comfort and economical efficiency.

The invention belongs to the technical field of intelligent wearing, and particularly relates to a hollow-structure S-shaped electrode flexible wearable thermoelectric device and a preparation method and application thereof. The invention discloses a hollow-structure S-shaped electrode flexible wearable thermoelectric device array which can be self-powered, and belongs to the field of intelligent wearing. Comprising a miniature temperature monitoring array component and a local temperature adjusting array component. Each of the miniature temperature monitoring array component and the local temperature adjusting array component is composed of a self-designed block thermoelectric arm, a flexible insulation heat insulation substrate layer, a flexible insulation heat conduction covering layer, an elastic wire, a flexible electrode and the like. The thermoelectric arm is made of a block-shaped thermoelectric material pressed by a self-designed die. The array component has a hollow structure, so that the thermoelectric performance is improved. The elastic wire is an S-shaped elastic wire. According to the flexible wearable thermoelectric device array, a new method is provided for realizing synchronous temperature monitoring and regulation under the condition of self power supply and meeting the local thermal comfort requirement.

The invention discloses an air conditioner control method, device and equipment in a sleep application scenario and a medium, and the air conditioner control method in the sleep application scenario comprises the steps that point cloud data of a user sleep area is continuously obtained through a millimeter wave radar, and the covering states of covering objects of all parts of a body are accurately recognized; the defects that a traditional infrared or temperature and humidity sensor cannot penetrate through the fabric, and local exposure is difficult to judge are overcome. Based on the recognition result, the system generates wind direction, wind speed and other wind feeling adjusting instructions in real time, air supply of the air conditioner is prevented from directly blowing exposed parts, and the thermal comfort is improved. The self-adaptive optimization of the strategy is realized by continuously monitoring the body movement frequency and the turn-over times of the user before and after adjustment, evaluating the adjustment effect and carrying out positive or negative weight adjustment on the control rule. The problems that in the prior art, due to the fact that the coverage state cannot be accurately perceived, air supply is uncomfortable, cold catching is likely to happen, and adjustment lags are solved, and the air conditioner use experience in the sleep scene is remarkably improved.

The invention provides an airflow planning method and system for an intelligent air conditioner wind shield. The method comprises the steps that S1, environment information of an indoor environment where an air conditioner is located and position information of at least one target user are obtained; and S2, based on a pre-stored airflow trajectory model library and the environment and position information, airflow motion trajectory planning is carried out, an airflow planning scheme capable of avoiding an obstacle area and reaching a target temperature area is generated, and the airflow trajectory model library defines a mapping relation between a wind shield state and an airflow trajectory. By establishing a high-precision airflow track model library and fusing real-time environment information, active intelligent planning and precise control over air conditioner airflow are achieved, users and obstacle areas can be automatically avoided, the airflow is precisely guided to the most suitable target temperature area (the airflow is conveyed to the upper portion during refrigeration and conveyed to the lower portion during heating), and the control precision of the air conditioner airflow is improved. Therefore, the uniformity of an indoor temperature field and the overall energy efficiency of an air conditioning system are remarkably improved while cold air is completely prevented from directly blowing a user, and the problems that air supplied by a traditional air conditioner is too cold and too hot, and comfort is poor are solved.

The invention provides a building energy system optimization scheduling method based on active and passive energy storage efficient utilization, and belongs to the technical field of energy management and intelligent control. By integrating various flexible resources, efficient utilization of energy, cost optimization and thermal comfort guarantee are realized. And establishing a four-end prediction model, and accurately predicting energy supply and demand in future 24 hours by combining weather forecast and operation data. Double-stage optimization scheduling is adopted, the heat pump heating water temperature is optimized in the first stage, and equipment such as an energy storage battery is finely scheduled in the second stage. According to the method, multi-objective collaborative optimization is realized, flexible resource potential is fully excavated, calculation complexity is reduced, practical application is facilitated, energy utilization efficiency is effectively improved, operation cost is reduced, thermal comfort is guaranteed, intelligent and efficient development of a building energy management system is promoted, and the method has remarkable economic and social benefits.

The invention discloses an air conditioner control method, device and equipment based on a neural network particle swarm and a medium. The method comprises the following steps: predicting an indoor environment parameter at a next moment through a pre-trained first neural network model according to a current indoor environment parameter; calculating a thermal comfort value at the next moment by combining the human body metabolic rate, the clothing thermal resistance and the parameter; determining a particle swarm constraint condition containing a target thermal comfort value and an air conditioner control parameter range according to the target thermal comfort value and a preset thermal comfort interval; under the constraint, a pre-trained second neural network model is reversely solved through a particle swarm optimization algorithm, and a target air conditioner control parameter combination meeting the target thermal comfort value and with the minimum operation power is obtained; and air conditioner operation is controlled according to the target air conditioner control parameter combination, so that energy consumption is effectively reduced while human body comfort is guaranteed, and the effects of adapting to environment changes in real time and balancing comfort and energy consumption are achieved.

The invention belongs to the field of building environment control, and provides a thermal comfort dynamic adaptive control method based on multi-modal wave analysis and AI driving. According to the method, environmental parameters, user physiological signals (HRV) and subjective thermal comfort data are synchronously collected in real time, time-frequency characteristics of the physiological signals are extracted through continuous Morlet wavelet transform, high-order dynamic characteristics are constructed, the thermal comfort state is predicted in combination with deep learning, a deep reinforcement learning strategy is optimized through an NSGA-II algorithm, a multi-target optimal strategy set is generated, and the optimal thermal comfort state is obtained. And finally, a multi-arm bandit model with Bayesian inference is utilized to realize strategy online adaptive updating. By applying the method, the thermal comfort prediction precision can be expected to be greater than or equal to 90%, the net energy conservation is greater than or equal to 32%, the limitation of the existing thermal comfort regulation and control method in a dynamic situation is effectively solved, and the dynamic balance of personalized thermal comfort and system energy conservation is realized.

The invention discloses an air conditioner energy-saving optimization method and system based on intelligent control, and the method comprises the following steps: 1, collecting temperature and humidity data and air conditioner setting parameters, carrying out the training through a residual error connection attention recurrent neural network model, and outputting a thermal inertia prediction value; 2, constructing a thermal behavior joint feature vector; 3, performing clustering operation by adopting a Dirichlet process mean value clustering algorithm, and generating a space-time microcell by setting a clustering penalty threshold value; 4, constructing a thermal comfort model by adopting an improved PMV model based on a Fanger heat balance theory, and obtaining a disturbance index value of each space-time microcell; 5, constructing a joint optimization objective function, and generating optimal control action parameters of each space-time microcell; and step 6, carrying out fusion calculation on the optimal control action parameters of all the space-time microcells to generate an overall control instruction. According to the method, thermal inertia prediction and dynamic clustering optimization methods are fused, and intelligent energy-saving control over the air conditioner is achieved.

A gas-fired heat pump unit that directly utilizes engine waste heat and features adjustable air supply is disclosed. The condenser and water supply and return lines are combined with refrigerant and coolant heat exchange lines. Refrigerant flows from the quasi-two-stage compressor outlet through the refrigerant heat exchange line, dividing it into a main path and an auxiliary path. The auxiliary path connects to the quasi-two-stage compressor air supply port after passing through a first electronic expansion valve and a flue gas heat exchanger. The main path, switched by a four-way reversing valve, can control forward and reverse circulation between the second electronic expansion valve and the outdoor evaporator. The outlet is connected to the quasi-two-stage compressor inlet. Coolant flows from the gas engine outlet through a coolant circulation pump and the coolant heat exchange line back to the inlet. The gas engine exhaust port is connected to the flue gas heat exchanger. Compressor air supply is simple and highly adjustable. The condenser utilizes stepped heating to decouple engine waste heat from compressor power. Heating is continuous during reverse cycle defrosting, improving indoor thermal comfort.

The invention discloses an outdoor thermal comfort intelligent evaluation method and system taking a psychological state as a medium. The method specifically comprises the following steps: acquiring psychological index data and multi-scale parameters, wherein the multi-scale parameters comprise environmental physical parameters and physiological response parameters; inputting the environmental physical parameters and the physiological response parameters into a dynamic feature extraction module, and performing cross-modal feature fusion through environmental physical parameter coding and physiological time sequence feature extraction to generate a fusion feature vector; constructing a psychological intermediary prediction module according to the psychological index data, inputting the fusion feature vector into the psychological intermediary prediction module, and generating a dynamic adjustment coefficient; and inputting the fusion feature vector and the dynamic adjustment coefficient into a thermal comfort evaluation module, and outputting a multi-scale prediction result. According to the method, the problem of space-time alignment of environmental parameter transient and physiological response hysteresis is solved, and the individual adaptability and prediction reliability of outdoor thermal comfort evaluation are remarkably improved.

The invention provides a building comprehensive flexible potential quantification method considering dynamic priority, and belongs to the technical field of building demand response and flexible energy consumption. The method comprises the following steps: inputting each flexible resource time window and working time, analyzing a relationship between a demand response interval and each flexible resource time window and working time, and determining a flexible resource type which can participate in adjustment; a priority evaluation index is constructed from five dimensions of the flexibility amount, the energy consumption income, the indoor thermal comfort, the indoor light environment and the flexibility change rate; calculating the weight by using an entropy method, and determining the priority of each flexible resource based on a TOPSIS algorithm; and according to the priority order, accumulating the flexibility quantity, comparing the baseline load with the power grid adjustment instruction in real time, and realizing positive and negative comprehensive flexibility potential quantification. The method can accurately quantify the comprehensive flexibility potential of the building, provides a basis for resource scheduling and strategy making, and enhances the economy, balance and reliability of a power system.