184 results about "Built environment" patented technology

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 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 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 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 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 relates to the technical field of illumination design layout, in particular to an illumination design layout method and system based on a BIM model. The method comprises the following steps: collecting building environment data and basic illumination data, carrying out geometric topology analysis to determine a building environment structure, fusing the building environment structure based on a BIM model, constructing an environment structure semantic framework, building a simulation building environment field, analyzing a basic illumination condition through illumination simulation, identifying an illumination weak area, and determining the illumination weak area. The method comprises the following steps: firstly, building environment structures are constructed, then, lighting requirements of all environment areas are analyzed, lighting areas needing to be strengthened are screened out, then, light data in the areas are detected, light refraction traceability is carried out to identify light shielding objects, finally, lighting design optimization is carried out according to the light shielding objects and the building environment structures, and the final optimized lighting design layout is formed. According to the invention, the lighting design layout optimization is realized, the uniformity and effectiveness of lighting are ensured, and the comfort and functionality of the internal space of the building are improved.

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 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 relates to the technical field of building environment monitoring, and discloses an intelligent building environment monitoring system and method, and the method comprises the steps: S1, collecting the related environment information of each preset monitoring point in a warehouse through a first environment collection module; s2, comparing each acquired environment information parameter with a corresponding preset early warning threshold value, and if any environment information parameter exceeds the corresponding preset early warning threshold value, generating an abnormal early warning signal of the monitoring point; according to the method, warehouse monitoring point environment parameters are collected in real time through distributed sensors, single-point threshold comparison is performed firstly to trigger preliminary early warning, then environment characteristic values are calculated through multi-parameter weighting and compared with a historical abnormal range, high-density sampling analysis is performed on a characteristic value abnormal region through an inspection robot, and the abnormal region is subjected to early warning. The method overcomes the defects that a traditional warehouse environment monitoring mode is high in false alarm rate, lacks comprehensive evaluation, cannot perform dynamic inspection and the like.

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 discloses a street safety perception evaluation method and system based on electroencephalogram characteristic waves, and relates to the technical field of construction of environment safety sensing degree, and the method comprises the steps: obtaining an electroencephalogram signal induced by a street scene, extracting a power spectrum value of the electroencephalogram signal, and calculating the power spectrum value of the electroencephalogram signal; based on the power spectrum value of the electroencephalogram signal, calculating the asymmetry of an electroencephalogram prefrontal lobe alpha frequency band and a cognitive load and pressure balance index, and converting the asymmetry and the cognitive load and pressure balance index into electroencephalogram safety perceptibility of a street scene; acquiring safety perception element feature data of the street scene, and inputting the safety perception element feature data of the street scene and the electroencephalogram safety perceptibility of the street scene into a pre-established street safety perceptibility evaluation model for iterative training to obtain a trained street safety perceptibility evaluation model; and based on the trained street safety perceptibility evaluation model, quantifying the feature contribution of the street environment elements to the safety feeling score predicted value by adopting an SHAP method, and obtaining a street safety perception evaluation result.

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.

The invention relates to an intelligent building sampling detection control system based on Internet of Things sensing, which belongs to the technical field of building environment control and comprises an environment state sensing unit, a predictive analysis unit, a flow field preprocessing unit, a path planning unit and a cooperative control unit. The environment state sensing unit is used for collecting environment state data of a target monitoring area to generate a real-time data vector; the predictive analysis unit is used for receiving the real-time data vector and performing diffusion trajectory deduction according to a preset state space normal form so as to generate a predictive state vector; the flow field preprocessing unit is used for responding to the prediction state vector and carrying out vortex kinetic energy deconstruction processing on the thermal plume so as to generate a weakened flow field environment, precious decision-making advance is won for follow-up accurate removal, and due to the fact that a pre-trained model is adopted, the real-time performance of prediction is guaranteed.

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 discloses an Internet of Things building environment terminal low-delay time slot control method. The method comprises the following steps: acquiring a baseline sequence time slot control table; receiving a guard proof record from a terminal node, only updating guard items in the baseline table according to the record, and generating a post-authentication sequential time slot control table capable of reflecting the real state of the node; scheduling opportunistic uplink according to a time bond mechanism in an unallocated gap of the authenticated time slot control table to generate an opportunistic uplink scheduling result; and combining the authenticated sequential time slot control table with the opportunity uplink scheduling result, and synthesizing and issuing an execution-level scheduling plan. According to the invention, the dynamic self-adaption to the terminal state is realized while the deterministic scheduling is guaranteed, the channel utilization rate is improved, and the access service with fairness and predictable time delay guarantee is provided for opportunistic uplink.

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.

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 invention provides a building environment interaction analysis method and device based on virtual-real space synchronous mapping and multi-modal perception, and the method comprises the steps: carrying out the preprocessing of a target region panoramic image set, and obtaining a static target region panoramic image; mapping the panoramic image of the static target area to a double-sphere coordinate system to obtain virtual scene space mapping; obtaining a thermodynamic distribution diagram according to the user eye movement data and the virtual scene space; inputting the panoramic image of the static target area into the trained feature recognition model to obtain all elements contained in the thermodynamic distribution diagram; and generating a visual behavior analysis report according to the staring duration time, the staring frequency and the spatial weight matrix of each element in the thermal distribution diagram in combination with the staring duration time, the staring frequency and the spatial weight matrix of each element.

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.

To provide a system and method for manufacturing affordable carbon fiber insulation as an alternative to glass fiber insulation to improve energy efficiency in a building environment.SOLUTION: An embodiment disclosed herein relate to a system and a method for processing coal. A direct air capture system includes a voltage source and a coal-derived electroactive polymer electrically connected to the voltage source, in which applying voltage to the coal-derived electroactive polymer causes carbon dioxide to bind to the coal-derived electroactive polymer. The method for capturing carbon dioxide includes introducing the electroactive polymer into a gas stream containing carbon dioxide, applying voltage to the electroactive polymer to bind the carbon dioxide to the electroactive polymer, and reversing the polarity of the voltage applied to the electroactive polymer to release the carbon dioxide.SELECTED DRAWING: Figure 1