293 results about "Building environment" patented technology

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 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 fire-fighting unmanned aerial vehicle autonomous navigation method and system oriented to a complex building environment, and relates to the technical field of unmanned aerial vehicle navigation.The method comprises the steps that firstly, obstacle distribution point cloud data and thermodynamic data are obtained based on a laser radar and other sensors, and a spatial constraint coefficient and a thermal disturbance intensity factor are calculated respectively; therefore, the space trafficability and stability of the flight environment are comprehensively reflected. And then, fusing the global path planning and the local obstacle avoidance to generate a comprehensive environment evaluation parameter, adaptively configuring a weight ratio of the global path planning to the local obstacle avoidance according to the comprehensive environment evaluation parameter, and carrying out weighted fusion on instructions output by different algorithms so as to generate a smooth and safe flight control instruction. According to the invention, the dynamic adjustment of the navigation strategy can be realized according to the change of the building environment, the obstacle avoidance safety in the complex environment is ensured, the continuity and efficiency of the overall path planning are also considered, and the autonomous flight stability and task execution reliability of the fire-fighting unmanned aerial vehicle in the high-risk building environment are significantly improved.

The invention relates to an urban low-altitude unmanned aerial vehicle flight path optimization method and system, and relates to the technical field of unmanned aerial vehicle path optimization, and the method comprises the steps: obtaining a three-dimensional building map of a target flight region, marking the positions of a flight starting point and a flight ending point, and determining an initial flight path; constructing a fast airflow simulator, and combining with the macroscopic airflow data to obtain microscopic airflow data; calculating the path cost for executing the initial flight path based on the microscopic airflow data at the current moment in combination with the performance parameters of the target model unmanned aerial vehicle; and performing iterative optimization on the initial flight path by taking the three-dimensional building map and the target model unmanned aerial vehicle as constraints and taking the lowest path cost as a target to obtain an optimal flight path. The problems that in traditional unmanned aerial vehicle path planning, the wind direction and the wind speed on the air route are difficult to pre-judge due to the urban low-altitude complex building environment, the path is planned only from the single distance dimension, the urban low-altitude airflow difference cannot be adapted, and the flight safety of the unmanned aerial vehicle is affected are solved.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

This invention discloses a full lifecycle management system and method for scientific computing programs. The system includes a build environment subsystem and a production environment subsystem. The former provides computer resources for the build process of the scientific computing program throughout its lifecycle, while the latter provides computer resources for the testing and deployment processes. This invention, through a full lifecycle management method for scientific computing programs, operates on corresponding computing resources, encompassing a series of steps including querying, triggering scheduling, build execution, result distribution, and test deployment. It automatically generates the executable file of the scientific computing program and configures its runtime dependencies. Simultaneously, it automatically generates a corresponding description file recording the entire lifecycle process. Furthermore, based on version management of these description files and their sets, it achieves full lifecycle traceability and cross-platform migration and deployment of scientific computing programs in a high-performance computing environment.

The invention discloses a multi-unmanned aerial vehicle networking building elevation inspection path planning method, and relates to the technical field of unmanned aerial vehicle inspection path planning, and the method comprises the steps: collecting building environment sensing data through a multi-source sensor; carrying out fusion noise reduction processing on the building environment sensing data, and constructing a three-dimensional model containing structure attributes and environment information; based on the three-dimensional model, multi-target optimization calculation is carried out through an NSGA-II algorithm with preset parameters, an unmanned aerial vehicle inspection scheme is generated, and the unmanned aerial vehicle inspection scheme comprises task allocation and a global path; based on the multi-unmanned aerial vehicle inspection scheme, performing local path search and smoothing processing by adopting a Chebyshev A * algorithm of a preset heuristic function to obtain an initial inspection path; and performing local adjustment or global re-planning on the initial inspection path based on environment dynamic data monitored in real time, and outputting a final inspection path.

A comprehensive spatial AI platform for neural 3D reconstruction and built environment analysis integrates foundation models, deep learning methods, and spatial reasoning capabilities to provide expert knowledge of the physical world. The platform combines symbolic AI and machine learning to facilitate 3D semantics for insurance, real estate, construction, robotics, and other business applications. The system processes captured images, videos, or point clouds through neural networks with low compute requirements, incorporating device-agnostic advanced spatial intelligence that delivers geometric, semantic, and relational data. The platform includes proprietary training innovations, comprehensive measurement and semantic understanding, external sensor integration, human-in-the-loop quality assurance, and API integration for programmatic access. Advanced spatial reasoning capabilities enable property damage assessments, construction progress tracking, robotics navigation, and real-time applications including room dimension validation and automated repair estimates, supporting enterprise workflows across various industry segments while enabling productivity improvements and new value-added spatial AI use cases.

The invention discloses a landscaping landscape design method based on big data, and belongs to the technical field of landscape simulation design, and the method specifically comprises the steps: obtaining microclimate data of a target area and surrounding building environment data, and inputting the data into a dynamic microclimate simulation model to obtain a microclimate dynamic distribution diagram; identifying the position and the type of a microclimate discomfort area based on the distribution map, and generating a landscape design element set; carrying out overlay analysis on the element set and the distribution map, and adjusting the attribute and position of each element to form a preliminary design scheme; the preliminary scheme is input into a model to simulate microclimate distribution after implementation, and the difference degree of new and old distribution diagrams is calculated; and when the difference degree exceeds a preset range, iteratively adjusting the design elements and repeating the optimization process, and outputting a final scheme until the difference degree reaches the standard. According to the invention, dynamic coordination of landscape design and microclimate environment is realized.

The invention discloses a buried pipe temperature response calculation method considering underground water seepage and ground temperature gradient, and belongs to the field of building environment and energy application engineering.The buried pipe temperature response calculation method comprises the steps that a shallow buried pipe heat exchanger is regarded as a finite long-line heat source model, and when underground water seepage passes, the shallow buried pipe heat exchanger is regarded as a finite long-line heat source seepage model; an energy control equation containing a soil heat conduction item, an underground water seepage convection item and a ground temperature gradient correlation item at the same time is established, and initial temperature distribution based on ground temperature gradient, ground surface fixed wall temperature and boundary conditions of an underground limited boundary are set; and finally, obtaining an analytical solution expression of the change of the temperature at any position of the underground soil along with the time through analytical solution. According to the method, the defect that a traditional model ignores key environmental factors is overcome, the heat transfer process of heat conduction and convection coupling can be accurately simulated, the accuracy of temperature field prediction and the reliability of long-term performance evaluation are remarkably improved, meanwhile, the complexity of numerical calculation is avoided, and the calculation efficiency is improved.

The invention discloses a college dormitory noise multi-dimensional evaluation method based on psychoacoustic and sound field simulation, and belongs to the field of building environment and noise control. The objective of the invention is to solve the problem that an existing dormitory noise evaluation method is single in index and neglects subjective perception and spatial distribution heterogeneity. The method comprises the following steps: acquiring physical data and original audio of dormitory noise through multi-point synchronous acquisition; calculating loudness, sharpness, roughness, fluctuation intensity and comprehensive psychoacoustic annoyance (PA) by using a psychoacoustic model, and scientifically quantifying subjective interference of noise; meanwhile, sound field simulation is carried out based on the accurately established three-dimensional dormitory model, a sound pressure level distribution cloud picture is generated, and a standing wave phenomenon and a sound energy accumulation area are visually revealed; and finally, generating a targeted noise reduction optimization strategy by integrating subjective and objective and spatial analysis results. According to the method, full-chain diagnosis of physical characteristics-subjective perception-spatial distribution of dormitory noise is realized, and a scientific basis is provided for accurate and efficient noise treatment.

The invention provides a smart building environment adaptive regulation and control method based on digital twinning and AI, and relates to the technical field of artificial intelligence, and the method comprises the following steps: extracting dynamic features based on multi-source system data after time alignment, so as to construct a dynamic feature set; inputting multi-source system data into the digital twin, performing iterative simulation on key environment parameters of a plurality of time steps in the future by taking a current building equipment state as a boundary condition, and outputting a multi-step prediction sequence; inputting into a model prediction controller, and solving to obtain an equipment regulation and control instruction sequence in a future first preset duration by taking the lowest total energy consumption in a prediction period as an optimization target and taking the condition that the key environment parameters do not exceed a comfort interval as a constraint condition; and the first equipment regulation and control instruction in the equipment regulation and control instruction sequence is issued to the field execution mechanism, so that self-adaptive regulation and control of the building environment are realized, the defect that passive response is performed after environmental parameters exceed the standard is overcome, and the quick response capability of the system to sudden environmental changes is remarkably improved.

The invention discloses a building air conditioner energy consumption-electric charge-comfort level intelligent control method based on MMOE and ParetoMTL, and the method comprises the following steps: (1) collecting the cooling load data of a large commercial building air conditioner, carrying out the abnormal value detection, and completing the normalization preprocessing; (2) constructing an environment-price-behavior multi-source time sequence data set and performing grading standardization; (3) dividing a training set, a verification set and a test set according to building grouping; (4) completing feature fusion and realizing task decoupling under cooperative constraint by using an MMOE mechanism; (5) jointly training a three-branch expert network, an MLP gating network and a PCGrad shared parameter optimization model; (6) generating a multi-task prediction and Pareto solution set on the test set; and (7) evaluating prediction performance and trade-off efficiency by using a plurality of indexes. According to the method, multi-target conflict, physical consistency and Pareto solution set missing difficulty can be effectively solved, and a new scheme is provided for demand response HVAC intelligent control.

The invention discloses an intelligent fire-fighting hidden danger identification method based on multi-modal fusion, and the method comprises the steps: collecting multi-source heterogeneous data in a building environment, and carrying out the unified processing of the data, and obtaining standardized multi-modal original input data; event anchor point detection is carried out on the multi-modal input, key events are extracted, and a corresponding multi-modal event sequence is generated; constructing an event graph containing time and causal edges, and outputting an alignment event stream by using a space-time coupling causal alignment module; importing BIM and air duct structure information to establish a spatial topological graph, and fusing event streams to generate spatial constraint features; constructing a self-evolution semantic decision map based on the fusion features, and dynamically adjusting node weights and edge connection relationships; and carrying out hidden danger identification and grade division on the real-time data by using the decision map, and outputting an early warning signal and positioning information. According to the method, high-precision identification and intelligent early warning of fire-fighting hidden dangers are realized through space-time causal alignment, spatial topology fusion and self-evolution semantic decision of multi-modal data.

The invention discloses a space-time knowledge graph-based facility layout scheme generation method. The method comprises the steps of S1, constructing an urban facility layout knowledge base; s2, acquiring a reference case place set of the target grid to be optimized; s3, extracting a facility significant co-occurrence mode in the reference case place set, and taking the facility significant co-occurrence mode as a recommended facility configuration mode of the target grid to be optimized; and S4, according to the feature vector of the city built environment of the target to-be-optimized grid obtained in the S1, inputting the feature vector and the recommended facility configuration mode of the target to-be-optimized grid obtained in the S3 into the constructed facility layout generation model, and obtaining an output result which is a facility layout scheme. According to the method, the facility distribution rule knowledge and the facility layout optimization intelligence are integrated, and the facility configuration recommendation and the facility layout optimization scheme can be automatically output for a specific land parcel.

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 provides an operation data analysis method and system of a heat supply system, and relates to the technical field of building environment and energy application engineering.The method comprises the steps that if a diagnosis result shows that no abnormity exists, differential operation strategies are generated based on building historical room temperature, building characteristics and real-time meteorological parameters in a multi-source fusion data set; obtaining meteorological prediction data in a preset time period; based on the differentiated operation strategy and meteorological prediction data, predicting heat load demand change through a long short-term memory network; and calculating a target output parameter set value by taking the room temperature reaching the standard and the lowest energy consumption as an optimization target based on the change of the thermal load demand, and sending the set value to a boiler control system for execution. According to the invention, through multi-source data fusion and real-time analysis, closed-loop intelligent operation from abnormal accurate diagnosis to demand prediction regulation is realized, so that the reliability, energy efficiency and comfort level of the heat supply system are comprehensively improved.

The invention discloses a building environment real-time regulation and control method, device and equipment based on the Internet of Things and a medium, and relates to the technical field of intelligent building control. Light, temperature and humidity data of a building environment are collected in real time through Internet of Things sensing equipment, frequency domain features of an environment data set are extracted through Fourier transform, classification and recognition are carried out in combination with a preset environment regulation and control model, and a dynamic weight algorithm is adopted to optimize and generate an environment control signal. And through a self-adaptive PID control algorithm of Kalman filtering denoising and reinforcement learning optimization, a deviation integral is calculated in real time to generate a precise regulation and control instruction, and environment regulation and control equipment is synchronously driven. Frequency domain feature analysis, dynamic weight adjustment and adaptive control technologies are fused, multi-parameter coordinated regulation and control of the building environment are realized, and the real-time performance, the stability and the energy efficiency ratio of environment regulation and control are effectively improved.

The application provides a kind of intelligent building environment adaptive regulation and control method based on digital twinning and AI, it is related to artificial intelligence technical field, including the following steps: based on time alignment after multi-source system data, extract dynamic characteristics to construct dynamic feature set;Multi-source system data is input to digital twinning, with the current building equipment state as boundary condition, iteratively simulates the key environmental parameters of multiple time steps in the future, and outputs multi-step prediction sequence;Again input model predictive controller, with the minimum overall energy consumption in the prediction period as the optimization goal, with the key environmental parameters not exceeding the comfort interval as the constraint condition, the equipment control instruction sequence in the first preset time in the future is solved;The first equipment control instruction in the equipment control instruction sequence is issued to the on-site actuator to realize the adaptive regulation and control of building environment, solve the disadvantage that the environment parameter is over-standard and then responds passively, significantly improve the rapid response capability of the system to sudden environmental changes.

A comprehensive spatial AI platform for neural 3D reconstruction and built environment analysis integrates foundation models, deep learning methods, and spatial reasoning capabilities to provide expert knowledge of the physical world. The platform combines symbolic AI and machine learning to facilitate 3D semantics for insurance, real estate, construction, robotics, and other business applications. The system processes captured images, videos, or point clouds through neural networks with low compute requirements, incorporating device-agnostic advanced spatial intelligence that delivers geometric, semantic, and relational data. The platform includes proprietary training innovations, comprehensive measurement and semantic understanding, external sensor integration, human-in-the-loop quality assurance, and API integration for programmatic access. Advanced spatial reasoning capabilities enable property damage assessments, construction progress tracking, robotics navigation, and real-time applications including room dimension validation and automated repair estimates, supporting enterprise workflows across various industry segments while enabling productivity improvements and new value-added spatial AI use cases.

The invention relates to the technical field of city construction, in particular to a city built-up environment intelligent evaluation system based on multi-source data fusion, which comprises multi-source data acquisition and processing, a city update potential recognition model, cross-scale quantitative evaluation system construction and built-up environment and city vitality nonlinear interaction mechanism analysis. The construction of the cross-scale quantitative evaluation system specifically comprises the step of establishing a comprehensive evaluation model of facility density, population matching degree and traffic network structure in combination with an AHP-entropy weight method and spatial syntactic analysis. According to the invention, through multi-source data fusion, cross-scale quantitative evaluation, machine learning modeling and interpretability analysis, an accurate, efficient and generalizable city update decision support system is constructed.

The invention relates to the technical field of urban building energy consumption calculation, in particular to an urban building energy consumption calculation method and device based on local weather data, and the method comprises the steps: obtaining local built environment information data of a local climate region, and calculating local weather data through the local built environment information data; the two-dimensional building boundary vector graph in the local climate area is converted into a three-dimensional building model, and an urban building energy consumption model with building prototype information is constructed based on building functions and years; and distributing the local weather data to a subdivided local climate grid corresponding to the urban building energy consumption model to calculate the urban building energy consumption of the building group. Therefore, the problem that the calculation efficiency and precision of the urban building energy consumption cannot be effectively guaranteed due to the fact that the resolution ratio of a local weather grid cannot be freely defined, the local built environment cannot be subdivided, and the difference of different local climate heat island effects cannot be represented in the prior art is solved.

The invention relates to the technical field of green building environment monitoring, and discloses a green building environment monitoring system and method and a storage medium. The method comprises the steps that environmental parameters such as thermodynamic parameters, humidity parameters and gas concentration parameters are continuously collected through intelligent sensing nodes at multiple positions in a green building; the environment parameters are transmitted to a central processing unit, and the central processing unit combines a pre-stored environment standard value and a real-time data stream and generates an environment control command by adopting a self-adaptive learning algorithm; the environment execution device carries out an environment regulation activity according to the command; and monitoring and adjusting an activity execution sequence in real time, calculating a performance evaluation index, generating a normal or abnormal performance state signal, and automatically triggering an alarm process when the performance state signal is abnormal. According to the method, a complete environment management and control closed loop is constructed, all-directional dynamic monitoring and accurate adjustment of the environment of the green building are achieved, the core requirements for environment protection and comfort of the green building are met, and it is guaranteed that the internal environment of the building is in a proper state.

The invention relates to a thermal exposure risk calculation method based on urban trip people flow and built environment dynamics, and the method comprises the steps: obtaining the average radiation temperature calculation input data of an urban environment, and calculating the average radiation temperature of the urban environment according to the average radiation temperature calculation input data; calculating general thermal climate index distribution of the urban environment based on the average radiation temperature, and determining a thermal exposure time period based on the general thermal climate index distribution so as to obtain thermal exposure duration according to the thermal exposure time period; and acquiring the hourly travel pedestrian flow of the urban environment, counting the accumulated travel pedestrian flow in the thermal exposure time period based on the hourly travel pedestrian flow and the thermal exposure time period, and calculating the thermal exposure risk according to the accumulated travel pedestrian flow. Therefore, the problems that in the related technology, due to dependence on static population data, the people flow dynamic state of urban day and night travel with high temporal-spatial resolution is difficult to reflect, and a thermal risk calculation result based on the static population data cannot meet the requirements of urban space environment exposure refined analysis and early warning are solved.

The invention discloses a method for acquiring optimal measurement conditions of building room temperature monitoring. According to the method, a discrete coordinate radiation model is set, a sun tracking function is started at the same time, and wall mixed thermal boundary conditions are considered. Factors influencing the accuracy and stability of room temperature monitoring, such as temperature measurement distance, time, sunshade condition, temperature measurement position, weather condition, window opening and closing, outdoor temperature and room characteristic factors, are explored, so that the influence rule of different factors on the room temperature monitoring of the building is provided. According to the method, accurate modeling is performed according to thermodynamic behaviors in a complex building environment, and complex factors, including thermal conductivity of building materials, indoor air flow and heat source distribution, suffered by the room temperature of a building are considered; under different environmental conditions, dynamic characteristics of a building room temperature change rule are considered, a monitoring scheme is dynamically adjusted based on real-time simulation data to obtain an optimal measurement condition, and the complexity of the system is reduced in a real-time feedback process.

A mixture includes carbon-coated oil-treated plastic particles and particles that include geomaterial. Treating the soil at a building site includes combining carbon-coated oil-treated plastic particles and geomaterial particles to yield a mixture and providing the mixture in a built environment to enhance soil stability under freezing conditions.

The walking assembly comprises a walking wheel body arranged on the photovoltaic cleaning robot, the walking wheel body comprises a first wheel body and a second wheel body which are coaxially and fixedly arranged, the end, away from the second wheel body, of the first wheel body is used for being in transmission connection with the photovoltaic cleaning robot, and the second wheel body is constructed into a conical wheel; the large-diameter end of the conical wheel faces the first wheel body, and the diameter of the large-diameter end of the conical wheel does not exceed the diameter of the first wheel body. Grooves which are arranged in the circumferential direction are formed in the walking outer ring of the walking wheel body, and the arrangement positions of the grooves in the walking wheel body correspond to the installation positions of the obstacle pieces so that the grooves can avoid the obstacle pieces. Therefore, the situation that the service life of the photovoltaic cleaning robot is affected due to the fact that the walking assembly collides with the obstacle piece is avoided, and the adaptability of the photovoltaic cleaning robot to the building environment of the photovoltaic assembly is greatly improved.

The application discloses a kind of deep learning crowd flow prediction methods of fusion built environment dynamic influence, comprising the following steps: S1, data acquisition and processing;S2, built environment dynamic influence modeling, the extraction of built environment dynamic influence effect is carried out;S3, construct spatiotemporal graph neural network fusion model;S4, the evaluation of model performance and the diagnosis of error are carried out;S5, output multi-time scale people flow prediction by spatiotemporal graph neural network fusion model.According to the present application, higher precision, stronger interpretability, crowd activity prediction can be adapted to multiple regions and multiple time periods.

This invention belongs to the field of fan control technology. It provides a laboratory exhaust gas-induced high-altitude jet fan control system, comprising: a multi-source data acquisition module for real-time acquisition of multi-dimensional time-series data related to laboratory exhaust gas emission safety, including fan operating parameters, building environment parameters, exhaust gas source equipment status parameters, and meteorological environment parameters; and a predictive maintenance and early warning module based on the multi-dimensional time-series data. By integrating multi-source data perception, chronic risk trend prediction, system vulnerability assessment based on digital twins, and graded resilience control strategies, a closed loop of "perception-prediction-assessment-decision-execution" is constructed. This achieves early identification, proactive early warning, and adaptive control of coupled risks between "equipment-building-environment-personnel," thereby enhancing the proactive safety capability to prevent toxic exhaust gases from entering the breathing zone of personnel.

The invention discloses a non-metering residential area water demand prediction method and device based on an adversarial domain adaptive neural network, and the method comprises the following steps: 1), obtaining the water consumption data of a source domain and a target domain, and the water consumption data comprises the building environment and social classification index (BESS index) of a residential area and the corresponding water consumption data; 2) constructing an initial model, wherein the initial model comprises an encoder, a decoder and a domain adversarial device; and 3) training an initial model by using the water consumption data, and during training, performing 0-value masking on the water consumption mode characteristics of the metering residential area in the target domain to simulate a non-metering scene so as to obtain a prediction model. The invention further provides a device for predicting the water demand of the non-metering residential area. According to the method, accurate estimation of the water demand of the non-metering residential area can be realized only by depending on the BESS index of the residential area, and the prediction problem caused by water consumption data missing in a non-metering scene is solved.