579 results about "Building environment" patented technology

The invention relates to the technical field of building energy consumption analysis, and discloses a building energy consumption analysis method and system based on artificial intelligence. The method comprises the following steps: collecting building environment data to form an energy consumption basic data set; processing the data set to generate an energy consumption feature vector; constructing a prediction model to obtain an energy consumption predictor; a predictor is used for comparing actual data to identify abnormity and generate a report; formulating an optimization scheme based on the report to generate a control instruction; and executing instruction record change data to update the feature library to complete a closed loop. According to the invention, closed-loop management of accurate prediction, anomaly detection, optimization control and effect evaluation of building energy consumption is realized, so that the building energy utilization efficiency is improved, and energy waste is reduced.

The invention relates to the technical field of intelligent building energy-saving control, and discloses a green building equipment intelligent energy-saving control method and system based on artificial intelligence, and the method comprises the steps: collecting building environment data, and carrying out the preprocessing; and simulating the energy consumption of the building equipment by using the digital twin model, and training the baseline neural network model to obtain an initial control strategy. And adopting a genetic algorithm to optimize hyper-parameters of the baseline model, and generating a plurality of candidate energy-saving control strategies. And inputting the optimized strategy into the deep reinforcement learning model, carrying out strategy iteration, calculating the energy-saving efficiency and comfort score after each iteration, screening the optimal strategy through a reward mechanism, and improving the accuracy of energy consumption prediction of the building equipment. The hyper-parameter of the neural network is optimized by using the genetic algorithm, and the intelligent degree of the energy-saving control strategy is improved. On the basis of deep reinforcement learning, the intelligent agent can adaptively adjust an energy-saving strategy under different environmental conditions, and the user comfort is guaranteed while the optimal building energy consumption is ensured.

The invention discloses an intelligent building energy-saving management method and system based on a digital twinning technology, and relates to the field of digital twinning technologies, and the method comprises the steps: building a three-dimensional building model based on a building information model and three-dimensional laser scanning point cloud data, and generating a twinning database through multi-source equipment data fusion; performing energy consumption simulation modeling according to the lamp dimming parameters, the air conditioner performance curve and the elevator operation log in the twin database to generate an energy consumption reference report; acquiring personnel density and environment parameters, and generating a building environment state matrix mapped with the twin database after space-time calibration; and establishing a hierarchical threshold value control strategy library based on the building energy consumption reference report and the building environment state matrix, and generating an optimization control strategy set through the equipment linkage relation matrix. According to the method, by embedding a Pareto optimal solution screening mechanism and equipment mutual exclusion rule verification, the daily average energy consumption is reduced on the premise that the comfort reaching rate of the optimal control parameter packet is ensured.

The invention relates to the technical field of continuous integration and continuous delivery, and discloses a Jenkins continuous integration method based on Kubernetes. According to the method, a Jenkins server is deployed in a Kubernetes cluster to serve as a control plane, a construction environment is created through a preset multi-container Pod template, and a Pod containing a plurality of containers is dynamically created based on the Pod template, so that each container executes different construction tasks. Containers in the Pod realize collaboration through sharing a network namespace and a storage volume, and a Jenkins server monitors task execution states of the containers in the Pod and performs dynamic scheduling according to task priorities and cluster load states. And after the task is completed, automatically destroying the Pod and releasing cluster resources. According to the method and the device, dynamic expansion and contraction of a construction environment and efficient parallel execution of tasks are realized, the problem that a traditional Jenkins static resource pool is difficult to deal with load changes is solved, the resource utilization rate is improved, the expandability of the system is enhanced, and an efficient and reliable solution is provided for an enterprise-level continuous integration system.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention discloses a central air conditioner energy-saving control method based on AI self-adaptive adjustment, and relates to the technical field of intelligent control, and the method comprises the following steps: collecting environmental data, equipment operation data and energy consumption data of a central air conditioner in real time, preprocessing multi-modal data, constructing a physical constraint equation in combination with a thermodynamic law, and generating a multi-modal data set; generating a multi-target optimization control instruction according to the optimization weight, solving an optimal equipment parameter combination through a Pareto frontier algorithm, and transmitting the optimal equipment parameter combination to a central air conditioner actuator; actual data after instruction execution are collected, the deviation degree of the energy-saving efficiency and the comfort degree is calculated, physical information neural network parameters are updated through a meta-learning framework, and thermodynamic partial differential equation coefficients are adjusted. According to the method, by constructing a multi-modal physical information fusion framework and a dynamic closed-loop optimization system, the comprehensive regulation and control capability of the central air conditioner in a complex building environment is improved.

The invention discloses a metro station non-commuting passenger proportion calculation method based on a random forest model, and the method specifically comprises the steps: selecting a research region, and collecting metro-related built environment data and transportation data; important built-up environment variables are determined through preliminary analysis, and variables influencing non-commuter are added; establishing a prediction model by adopting a random forest algorithm, and dividing a training set and a test set; analyzing the output of the random forest model by applying an SHAP technology, and determining the importance sequence of the influence of each built-up environment factor on the proportion of non-commuting passengers and the action mechanism of the influence of each built-up environment factor; according to the size of the SHAP value and in combination with an actual scene, a numerical matrix of all built-up environment factors around the subway station in the scene is determined; and substituting a model coefficient result into the established environment factor matrix, and calculating the subway station non-commuting travel proportion. According to the method, the prediction precision of the non-commuting passenger quantity of the subway station is remarkably improved, the understanding of non-commuting travel behaviors is deepened, and powerful support is provided for urban traffic management and planning.

The invention discloses a building sound wave reflection path analysis and noise source positioning method and system, and the method comprises the steps: S1, arranging a plurality of sound sensors according to the geometric structure of a target building, collecting sound signals, carrying out the signal sampling of the sound signals, and storing the sound signals in the form of a time sequence; s2, preprocessing the sound signal, extracting a time difference matrix, a frequency spectrum feature and an energy feature, merging into a three-dimensional time sequence feature, and normalizing to form a three-dimensional time sequence feature matrix; s3, establishing an LSTM network, and predicting a three-dimensional position and a sound wave reflection path of an output noise source; s4, optimizing and calculating a sound wave reflection path by using the geometric structure data of the target building, and generating a three-dimensional reflection path diagram; and S5, dynamically displaying a noise source position and a sound wave reflection path according to the three-dimensional reflection path diagram. According to the invention, high-precision three-dimensional positioning of a noise source can be realized in a complex building environment, accurate modeling is realized, and propagation and reflection paths of sound waves in a building are analyzed.

The invention discloses a BIM model-based digital twinning and panoramic camera mapping method and system, and relates to the technical field of BIM and digital twinning, and the system is mainly composed of a data acquisition module, a data preprocessing module, a feature extraction and analysis module, a coordinate conversion and matching module, a digital twinning model module and a display module. Real-time and efficient integration of the video data and the BIM model is realized, and then the digital twin model capable of accurately reflecting the current state of the building is constructed. The model is of great significance to comprehensive monitoring and management of the building environment, and the intelligent level and the operation and maintenance efficiency of the building are remarkably improved.

The invention provides a short message issuing processing method and system based on scene self-adaption. The method comprises the steps that a three-dimensional communication characteristic spectrum is constructed through a building information model and infrared thermal imaging data, short message request distribution is analyzed and predicted in combination with a signal attenuation field, cross-operator network quality data is integrated to form a converged communication quality field, and finally short message issuing is intelligently scheduled according to the network state and service requirements. According to the method, accurate short message routing optimization based on real-time communication quality and crowd distribution in a complex building environment is realized. According to the technical scheme provided by the invention, the short message delivery rate and timeliness in a complex building environment can be remarkably improved, and particularly, the communication reliability in a crowded area and a signal blind area is remarkably improved.

A system for assessing the ecological sensitivity and development potential of areas within ecologically sensitive mountain towns, consisting of: a data acquisition module configured to extract various data types, including multi-source geospatial data, environmental data, built environment data, socio-economic data and community participation data, with the extracted data being validated and stored in a database; a central processing unit comprising one or more processors connected to a memory, the processor communicatively connected to the database in which the extracted data is stored, the memory storing instructions that, when executed, configure the processor to implement the following: a data input module configured to receive data corresponding to multiple indicators from different assessment dimensions, including ecology and environment, built environment, economy, heritage and culture, mobility, location, community participation, visual aspects and safety from hazards; an indicator evaluation module configured to evaluate each indicator according to predefined sensitivity categories and assign corresponding sensitivity index values in the range of 0.1 to 1; a dimension weighting module configured to apply dimension-specific weights to calculate weighted scores for each indicator; a development potential calculation module that aggregates the weighted assessments to determine an overall sensitivity assessment and assigns this overall sensitivity assessment to a development potential rating scale; and a visualization module configured to generate visual representations and reports of the assessment results; and an output module having a user interface operatively connected to the visualization module of the central processing unit, the output module configuring the user interface to display the visualized results and the final index value obtained from the central processing unit.

According to the remote sensing image green roof intelligent identification method and system based on the improved Mask RCNN algorithm, the feature extraction capability of the algorithm is enhanced by introducing an attention mechanism, and the edge segmentation precision of a target is improved by constructing a joint loss function, so that precise detection of a green roof in a complex built environment is realized. According to the invention, the morphological parameter analysis module is synchronously integrated, geometric feature parameters such as the area, the perimeter and the shape index of the green roof can be automatically extracted, and integrated processing of green roof identification and feature parameter extraction is realized. Compared with a traditional method, the method can effectively solve the problems that target examples are difficult to distinguish, and environmental adaptability is poor, and provides a reliable technical means for green roof monitoring and ecological environment benefit evaluation.

The invention discloses a green building microclimate regulation emission reduction and convergence increase method based on park biodiversity, and the method comprises the steps: obtaining biodiversity data and building environment parameters, constructing a three-dimensional ecological space digital twinborn model, and carrying out the multi-physics coupling analysis; loading boundary conditions to generate a comprehensive model, then carrying out microclimate dynamic simulation, training an ecological regulation model to generate initial regulation parameters, constructing a multi-objective optimization model in combination with dynamic regulation simulation information and building design parameters to obtain optimal parameters, and finally adjusting a regulation strategy based on real-time prediction. According to the method, by integrating biodiversity and building environment data and utilizing digital twinning, multi-field coupling simulation and intelligent optimization algorithms, collaborative optimization of microclimate adjustment, carbon emission reduction and carbon sink capacity improvement is achieved, a technical scheme is provided for deep fusion of green buildings and ecological systems, and the method is suitable for popularization and application. The method is suitable for the fields of urban building energy conservation and ecological environment protection.

The invention relates to the technical field of building environment intelligent control, provides an air conditioner distributed regulation and control system based on a neural network and a method thereof, and is suitable for energy efficiency optimization and comfort guarantee of multi-area buildings. The system comprises a coordination control module, an optimization decomposition module, a region control module, a model evolution module and a strategy self-learning module, and region-level scheduling and parallel solving of a control task are realized by constructing a multi-objective optimization model and performing decomposition based on an alternating direction multiplier method; constructing a control model by using a hypergraph neural network, and supporting online updating, migration deployment and adaptive adjustment; strategy self-learning is realized through a strategy evaluation and aggregation mechanism driven by control errors, and a control closed loop is formed. The system improves the precision, flexibility and energy efficiency performance of air conditioner regulation and control, and is suitable for building scenes such as intelligent buildings and green parks.

The invention relates to the technical field of monitoring systems, in particular to an electromechanical equipment control method and system for building intelligent system integration. Comprising the following steps: collecting operation parameters of electromechanical equipment and building environment parameters in real time; the operation parameters and the building environment parameters are transmitted to a central control unit through a communication network; the operation parameters of the central control unit and the building environment parameters are input into a preset evaluation model, and the operation state and abnormality diagnosis information of the equipment are output; generating a corresponding control strategy through an intelligent algorithm; the control command is converted into a control command through the central control unit and is sent to the execution mechanism through the communication network, and the execution mechanism regulates and controls the electromechanical equipment and sends a feedback signal to the central control unit. The multi-source sensor is used for collecting operation parameters and environment parameters in real time, and the data precision is guaranteed in combination with a self-calibration mechanism; meanwhile, data interference and leakage risks are reduced through encryption processing, and a high-quality data basis is provided for subsequent analysis.

The invention relates to the technical field of building environment temperature control, and discloses a temperature control equipment intelligent adjusting method and system based on machine learning, and the method comprises the steps: building a space thermal field sensing network, obtaining multi-dimensional temperature field data, and generating a temperature gradient distribution matrix; and the positions of cold and hot spots are calibrated by combining infrared temperature measurement data, an airflow path is predicted, and air supply parameters are optimized. Meanwhile, building heat capacity characteristics are analyzed, and a thermal response dynamic model is constructed. And the air supply direction is adjusted for the local supercooling area. And based on the temperature change rate and the air supply parameters, the equipment start-stop time is optimized. Through real-time temperature sampling, continuous updating of temperature gradient distribution, cyclic execution of cold and hot spot calibration, airflow prediction and air supply optimization, accurate control of space temperature is realized. According to the invention, the accuracy and energy efficiency of building space temperature adjustment can be obviously improved, and an innovative solution is provided for an intelligent temperature control system.

The invention relates to the technical field of intelligent building environment control and energy management, in particular to a thermal comfort regulation and control method and device based on visual perception and global sensitivity analysis, and constructs an attribute recognition model based on collaborative optimization of a lightweight visual perception network and an attention mechanism. High-precision dynamic capture of human body attributes is realized through the model; human body metabolic rate information is calculated based on the recognized human body attributes, then a thermal comfort model based on global sensitivity analysis and a hybrid intelligent algorithm is constructed, human body thermal comfort is calculated through the model, and finally working parameters of an air conditioner or a heat pump are adjusted based on the human body thermal comfort. The contradiction between environment parameter real-time response and energy consumption optimization is effectively solved, and an innovative solution is provided for building energy conservation and personalized thermal comfort management.

The invention discloses a full-equipment energy consumption optimization management system in a building, and relates to the technical field of intelligent control, and the system is provided with a multi-modal data collection module which collects building environment parameters and equipment operation data through a distributed sensor network, and generates a multi-modal sensing matrix; an environment state prediction module is set to construct a dynamic knowledge graph based on a multi-modal sensing matrix, a graph attention network is used, an energy flow matrix of equipment topology is output, micro-environment state prediction data is generated through prediction of a finite element solver, a scheduling scheme solving module is set, a mixed integer programming model is constructed based on the micro-environment state prediction data, and a scheduling scheme is set. And solving the mixed integer programming model to obtain a scheduling scheme of the energy consumption equipment, setting a control instruction optimization module, and driving the edge execution equipment to operate based on the scheduling scheme. The global scheduling optimization of the whole building energy consumption equipment is realized, the energy consumption is effectively reduced, and the environmental comfort is improved.

The invention discloses a fire-fighting effect evaluation method and platform under digital modeling, and relates to the technical field of fire-fighting evaluation, and the method comprises the steps: carrying out the structural information collection of a target building, and obtaining the target structural information; performing digital modeling by adopting the target structure information to obtain a building three-dimensional model of the target building; performing interaction to obtain fire-fighting configuration data of the target building; performing fire-fighting equipment fitting on the building three-dimensional model to obtain a fire-fighting layout model; presetting a multi-stage fire risk scene, and obtaining a multi-stage fire protection simulation result; and performing fire-fighting response quantitative evaluation to obtain a fire-fighting effect evaluation result. The technical problems that in the prior art, the fire emergency capacity in a complex building environment is difficult to comprehensively evaluate, and the overall effect of a fire-fighting plan cannot be effectively verified are solved, and the technical effects that the spreading path and the smoke diffusion condition of a fire are accurately simulated, and the timeliness in an actual fire is effectively evaluated are achieved.

The invention discloses a roof radiant cooling and independent fresh air dehumidification system mixed operation comprehensive control method, and belongs to the technical field of building environment control and heating ventilation air conditioning. The method comprises the steps that the radiant cooling capacity and the dehumidification capacity of a surface air cooler are calculated through a heat balance equation, and an optimization objective that a top plate radiation system bears 70% sensible heat load and a fresh air system bears 90% humidity load is set; an anti-condensation strategy is triggered by combining dew point temperature monitoring, a control variable is adjusted by taking minimization of energy consumption as a target in a rolling time domain, and it is ensured that the indoor temperature ranges from 23 DEG C to 26 DEG C, the humidity ranges from 40% to 60%, the black ball temperature difference is smaller than or equal to 2 DEG C, and the condensation risk is smaller than or equal to 6%. The method is suitable for energy-saving temperature and humidity regulation and control of large public buildings, and efficient cooperative processing of sensible heat and latent heat loads is achieved.

The invention relates to the technical field of computer aided design, in particular to an energy-saving building design method based on digital simulation, which comprises the following steps: analyzing air velocity, temperature gradient, density and pressure distribution based on indoor aerodynamic parameters, identifying air vortex structure characteristics, and generating an energy-saving building environment data set. According to the method, aerodynamic characteristics in a building are accurately recognized, the vortex structure analysis capacity is improved, the air flowing path is optimized, aerodynamic parameters are combined, thermal resistance and heat conduction materials are matched, the building envelope layer is adjusted, heat flow transmission is guided, heat loss is reduced, the heat energy utilization rate is increased, and the spectral characteristics of the materials are adjusted according to illumination redundancy; the lighting efficiency is optimized, the heat load is reduced, outer wall heat transfer is optimized in combination with airflow characteristics, the ventilation rate is dynamically regulated and controlled, airflow short circuit is avoided, heat flow, illumination and ventilation parameters are integrated, precise optimization of building energy consumption is achieved, and the energy efficiency regulation and control level is improved.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention relates to the technical field of electrical equipment management, and particularly discloses a building electrical equipment intelligent monitoring method and system based on the Internet of Things, and the method comprises the steps: laying a sensor network in a building, and enabling the sensor network to be installed at the electrical equipment and corresponding environment monitoring points in the building, the data acquisition module is used for acquiring related data of electrical equipment and building environment in real time; an edge computing node is established in a sensor network or at an equipment end and is used for carrying out primary processing on data acquired in real time and transmitting the processed data to a cloud platform through a wireless communication technology; by deploying the sensor network and the edge computing node, real-time data acquisition, processing and analysis of the electrical equipment and the building environment are realized, and by adopting the adaptive data fusion algorithm, the deep neural network model and the global optimization decision engine, the running state of the electrical equipment can be dynamically monitored, and the real-time monitoring of the building environment can be realized. And intelligent regulation and control and energy efficiency optimization management can be carried out according to the equipment state and the building energy efficiency target.

The invention discloses an urban building group energy flexible management digital platform and method, and the method comprises the steps: managing multi-source data through a data layer, including building environment, energy consumption, user willingness and power grid signals; the front-end module constructs a city visual model (CVM) and a building agent component based on an unreal engine, and realizes visual display and interactive operation of the energy system; the CVM covers four layers, namely a basic environment layer, a building layer, a mobile resource layer and an energy infrastructure layer; the building agent component realizes fine management of energy production, consumption, energy storage and power grid supply; the rear-end module is responsible for data processing, logical operation and algorithm execution, real-time data storage, resource scheduling and decision planning; the business function module comprises resource real-time monitoring, demand response management and resource configuration and planning deduction, and meets the requirements of short-term management and long-term planning. According to the invention, multi-scale interaction and refined operation are realized, the management granularity and the visualization effect are improved, and powerful support is provided for flexible management and low-carbon transformation of urban building groups.

The invention relates to the technical field of intelligent building environment control and energy management, and provides a building ventilation regulation and control method and equipment based on multi-target space-time analysis in order to solve the problems of high energy consumption, uncontrollable infection risk and response lag in the prior art. Original data and the spatial-temporal features are combined into a multi-modal fusion data set, an air quality change rule is learned based on an LSTM model, a future air quality state matrix is output, a multi-target dynamic optimization function is constructed, an optimal regulation and control strategy of a fan, fresh air and purification equipment is generated, and the optimal regulation and control strategy of the fan, the fresh air and the purification equipment is obtained. The response speed is improved while energy consumption is reduced and infection risks are controlled, and the system is particularly suitable for public places such as hospitals, schools and nursing homes with high requirements for indoor gas environments.

The invention relates to the technical field of smart home, in particular to an intelligent control system for a temperature and humidity controller, and the system comprises a data collection module which is used for collecting building data, environment data, equipment data and meteorological data, carrying out the preprocessing of the collected data, and carrying out the fusion of the collected data, and constructing a basic data set; the data acquisition module comprises at least one sensor node used for acquiring environmental parameters. Through multi-dimensional data acquisition including building data, environment data, equipment data and meteorological data and three-dimensional grid division of the internal space of the building, the problem that deployment of sensor nodes in an old building is limited is solved, an interpolation method is combined with virtual modeling, a data blind area is made up, and comprehensiveness and accuracy of temperature and humidity adjustment are ensured; and the influence of temperature and humidity propagation is reflected more accurately in combination with a building layout correction factor and a dynamic environment correction factor, a dynamic adjustment mechanism enables the system to adapt to a complex building environment, and the accuracy of an adjustment effect is improved.

The invention relates to the technical field of urban fire safety rescue, in particular to a low-altitude fire fighting system and method based on urban CIM and Beidou positioning. According to the system and the method, full space-time accurate positioning is realized through the Beidou differential base station and the UWB indoor positioning equipment; integrating the urban information model system and unmanned aerial vehicle cluster data, and constructing a multi-source heterogeneous data set; a coordinate system is unified based on a space-time reference calibration module, and fire behavior prediction, path planning and resource scheduling are realized through AI computing nodes; the unmanned aerial vehicle task distribution module is used for realizing multi-vehicle cooperative rescue; and iteratively optimizing system parameters through a closed-loop adaptive optimization module. The method is suitable for fire emergency rescue in an urban complex building environment, and the rescue response speed, the decision-making precision and the cooperative combat capability are remarkably improved.

The invention discloses a building fresh air linkage control system based on the Internet of Things, and relates to the technical field of intelligent control linkage, a sensing acquisition module is used for acquiring building state data, analyzing the building state data according to a priority strategy, analyzing building spaces in different regions, generating region information priorities, and sending the region information priorities to a central processing unit; focusing the key change area according to the area information priority to obtain a building environment potential field graph; the cooperative control module is used for performing multi-region cooperative decision on the building environment potential field graph to obtain an expected environment parameter sequence, and adjusting the expected environment parameter sequence through an optimization algorithm to obtain a fresh air coordination instruction set; and the execution decision module is used for performing multi-objective optimization on the fresh air coordination instruction set, generating an equipment-level control instruction, issuing the control instruction to a building equipment executor and completing accurate control of the fresh air system. According to the system, an optimized and convergent fresh air closed-loop control mechanism is established, operation of cold and heat source equipment is effectively coordinated, the energy-saving effect is achieved, and energy waste is avoided.

The invention discloses a building energy management method based on big data, and relates to the technical field of building energy management, and the method comprises the steps: collecting building environment, room cold and hot demands and historical energy consumption data through a long and short term memory network LSTM in combination with Bayesian optimization, constructing a short-term and long-term cold load prediction model, and dynamically adjusting a cold and hot water supply strategy; the cold and hot water supply adjusting part enables cold and hot water conveying to accurately meet different room requirements on the basis of cold supply optimization parameters in combination with variable-frequency water pump regulation and control, water supply flow optimization of all floors and cold and hot water pipe network balance adjustment; an error feedback correction mechanism is introduced, the deviation between a predicted value and an actual load is monitored in real time, if the error exceeds a threshold value, an LSTM model is automatically adjusted, cooling parameters are updated, the prediction precision is improved, cooling and heating load regulation and control errors are reduced, and the influence of temperature fluctuation on user experience is reduced; aiming at different room temperature control requirements, personalized energy supply is carried out based on real-time temperature and humidity, air velocity and user preference.

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.