451 results about "Building environment" patented technology

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 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.

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

The invention 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.

The invention discloses a public building environment monitoring system based on the Internet of Things, and belongs to the technical field of safety monitoring management, and the public building environment monitoring system specifically comprises the steps that a feature extraction module obtains Wi-Fi RTT time delay features, Bluetooth RSSI features and UWB TDoA features in parallel through a multi-mode feature extractor, and marks environment quality marks for each type of features; the signal attenuation correction module performs joint correction on a Wi-Fi RTT refraction error, a Bluetooth signal absorption error and a UWB multi-path reflection error based on a physical attenuation model and an offline training environment response curve, and outputs a corrected feature vector; the filtering fusion module is used for firstly applying Kalman filtering in a time domain and then applying particle filtering in a space domain through a multilayer Bayesian filtering fusion engine, fusing the corrected feature vectors and generating a target dynamic grid cell point probability heat map; and selecting a unit with the highest posterior probability from the target dynamic grid unit grid point probability heat map as a final alarm position.

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.

The invention is suitable for the technical field of deep reinforcement learning, and provides a deep reinforcement learning method and system for building energy control, and the method comprises the steps: constructing an intelligent agent based on a target building environment; collecting common experience data when the intelligent agent interacts with the target building environment; constructing a prompt text; inputting the prompt text into the large language model to obtain an action range of the control action; performing correction processing on the common empirical data according to the action range to obtain empirical data based on LLM correction; and according to the common experience pool and the LLM experience pool, training the intelligent agent by minimizing a loss function. According to the method, the action range of the control action in different environment states is analyzed by utilizing the large language model, so that low-quality empirical data in random exploration of the intelligent agent is guided and corrected, the intelligent agent can obtain high-quality experience, and the learning efficiency and the overall performance of the building energy control strategy are improved.

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 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 building construction safety monitoring method and system based on multi-source sensing fusion, and the method comprises the steps: firstly carrying out the precise space-time reference alignment of an original video stream and a UWB positioning data package, so as to solve the inherent asynchronism and conflict problems of heterogeneous sensor data, and guarantee the accuracy of subsequent data fusion; then, parallel feature extraction and preliminary entity association are carried out on the aligned data, and scene constraint-based dynamic confidence evaluation is carried out on a preliminary fusion object by innovatively utilizing BIM semantic data, so that priori knowledge of a building environment is fully utilized, and the reliability of sensor data is dynamically optimized; therefore, the defect of information loss caused by environmental influence, shielding or signal interference of a single sensor is overcome. And finally, the system performs adaptive fusion decision and state optimization according to the obtained confidence score, and outputs a high-confidence object state. In this way, the accuracy, the real-time performance and the reliability of building construction safety monitoring are remarkably 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.