Urban building fire safety intelligent management platform based on BIM and GIS multi-source fusion

Abstract

The invention relates to the field of urban building fire-fighting management, and discloses an urban building fire-fighting safety intelligent management platform based on BIM and GIS multi-source fusion, and the platform comprises a multi-dimensional sensing module which is used for accessing BIM building detail data and GIS geographic space data, and collecting the multi-dimensional data of a building in real time; the data fusion module is used for performing space-time alignment and feature fusion on the BIM building detail data, the GIS geographic space data and the multi-dimensional data by adopting a space-time attention mechanism, and performing standardization processing to obtain fused data; and the coupling calculation module is used for utilizing a multi-physics field coupling algorithm. Through comprehensive acquisition and access of BIM building detail data, GIS geographic space data and real-time multi-dimensional data, space-time alignment and feature fusion of multi-source data are completed by means of a space-time attention mechanism, and a fire dynamic model including interaction of a temperature field, a structural mechanical field and a smoke diffusion field is constructed by using a multi-physics field coupling algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of urban building fire management, and specifically to an urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS. Background Art

[0002] With the acceleration of urbanization, urban buildings are becoming increasingly dense, multifunctional, and structurally complex. Building fire safety management faces multiple challenges, including difficulty predicting fire risks, inefficient emergency response, and poor multi-system coordination. Against this backdrop, intelligent management approaches that integrate Building Information Modeling (BIM), Geographic Information Systems (GIS), and IoT sensing technologies have become a key development direction for improving urban building fire safety.

[0003] Traditional intelligent management platforms for urban building fire safety mostly use BIM to achieve static modeling of internal fire protection facilities in buildings, use GIS to complete macro-labeling of urban fire protection resources, and use sensors for single-point data collection. As a result, the micro-architectural details of BIM and the macro-geographic information of GIS are separated, making it impossible to conduct collaborative analysis of the real-time status of micro-components and the impact of the macro-environment. Fire simulation can only rely on static calculations of a single physical field, and decision-making strategies are mostly generated based on preset rules, which makes it difficult to adapt to the dynamic evolution of temperature fields, structural deformation and smoke diffusion during the development of a fire. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS, which solves the problem that traditional urban building fire safety intelligent management platforms are difficult to adapt to the dynamic evolution laws of temperature fields, structural deformation and smoke diffusion during fire development.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS, including:

[0006] Multi-dimensional sensing module: used to access BIM building detail data and GIS geospatial data, and collect multi-dimensional data of the building in real time;

[0007] Data fusion module: It is used to perform spatiotemporal alignment and feature fusion of BIM building detail data, GIS geospatial data and multi-dimensional data using the spatiotemporal attention mechanism, and perform standardization processing to obtain fused data;

[0008] Coupling calculation module: Used to use the multi-physics field coupling algorithm to construct a fire dynamics model that includes the interaction of temperature field, structural mechanics field and smoke diffusion field based on the fusion data, and simulate the dynamic process of temperature field, structural deformation and smoke diffusion to obtain physical simulation results;

[0009] Intelligent decision-making module: used to generate evacuation routes and emergency strategies based on physical simulation results through dynamic causal reasoning and multi-objective optimization algorithms;

[0010] Interactive control module: used for real-time transmission and visual interaction of various data, and to link fire-fighting equipment and traffic systems to perform hierarchical control according to emergency strategies.

[0011] By adopting the above technical solutions, through the comprehensive collection and access of BIM building detail data, GIS geospatial data and real-time multi-dimensional data, the spatiotemporal alignment and feature fusion of multi-source data are completed with the help of the spatiotemporal attention mechanism to form standardized fused data. The multi-physics field coupling algorithm is used to construct a fire dynamics model that includes the interaction of temperature field, structural mechanics field and smoke diffusion field, and dynamically simulate the evolution laws of various physical fields during the development of the fire. Combined with dynamic causal reasoning and multi-objective optimization algorithms, evacuation routes and emergency strategies adapted to the dynamic changes of the fire are generated based on the real-time physical simulation results. Through real-time data transmission, visual interaction and hierarchical linkage control of fire-fighting equipment and traffic systems, a complete closed loop from multi-source data fusion to dynamic simulation, intelligent decision-making and execution control is constructed. The dynamic evolution laws of temperature field, structural deformation and smoke diffusion during the development of the fire are characterized and adapted in real time, solving the problem that traditional urban building fire safety intelligent management platforms are difficult to adapt to the dynamic evolution laws of temperature field, structural deformation and smoke diffusion during the development of the fire.

[0012] Preferably, the multi-dimensional sensing module includes a sensor unit, a data access unit and an edge preprocessing unit. The sensor unit is used to collect multi-dimensional data of the building in real time through sensing equipment. The sensing equipment includes a fiber grating temperature sensor, a distributed acoustic sensor, a temperature and humidity sensor, a smoke concentration sensor and a pressure sensor. The multi-dimensional data includes building structure status, environmental parameters and fire equipment operation data. The data access unit is used to access BIM building detail data using an IFC standard parsing tool and access GIS geospatial data using a geospatial data conversion interface. The edge preprocessing unit is used to filter abnormal data in the multi-dimensional data, BIM building detail data and GIS geospatial data using a sliding window algorithm.

[0013] Preferably, the building structure status includes structural temperature and vibration displacement, the environmental parameters include wind speed, humidity and smoke concentration, the fire-fighting equipment operation data includes sprinkler system pressure and fire door switch status, the BIM building detail data includes the geometric dimensions, material properties and fire-fighting facility locations of building components, and the GIS geographic spatial data includes the terrain around the building, road network and fire-fighting resource distribution.

[0014] Preferably, the data fusion module includes a data preprocessing unit, a spatiotemporal alignment unit and a feature fusion unit. The data preprocessing unit is used to use the Z-score standardization formula to perform format conversion and normalization processing on BIM building detail data, GIS geographic spatial data and multi-dimensional data to obtain standardized data. The spatiotemporal alignment unit is used to use a dynamic time warping algorithm to perform time alignment based on the standardized data, and use an improved iterative nearest point algorithm to perform coordinate registration to obtain spatiotemporal aligned data. The feature fusion unit is used to use a spatiotemporal attention mechanism to calculate the time attention weight and the space attention weight based on the spatiotemporal aligned data, perform feature reorganization on the spatiotemporal aligned data through weight distribution, generate a fused feature vector, and output the fused data.

[0015] Preferably, the coupling calculation module includes a model construction unit, a thermal coupling calculation unit, a smoke diffusion calculation unit and a simulation result integration unit. The model construction unit is used to extract the parameters of the building based on the fusion data, and construct a fire dynamics model including the interaction of the temperature field, structural mechanics field and smoke diffusion field based on the multi-physics field coupling algorithm. The thermal coupling calculation unit is used to solve the dynamic relationship between the temperature field and the structural deformation based on the fire dynamics model, the heat conduction equation, the structural dynamics equation and the temperature-dependent thermal conductivity formula, and output the temperature field dynamic data and the structural deformation dynamic data. The smoke diffusion calculation unit is used to simulate the smoke diffusion process based on the fractional-order diffusion equation according to the temperature field dynamic data and environmental parameters, and output the smoke diffusion dynamic data. The simulation result integration unit is used to associate and integrate the temperature field dynamic data, the structural deformation dynamic data and the smoke diffusion dynamic data with the spatial coordinates in time series, and output the physical simulation results including the temperature field, structural deformation and smoke diffusion dynamic process.

[0016] Preferably, the parameters include component thermal parameters, geometric topological relationships and environmental boundary conditions. The component thermal parameters include material density, constant pressure specific heat capacity, reference thermal conductivity and temperature coefficient. The geometric topological relationships include three-dimensional coordinates, dimensional parameters and spatial connection methods of building components. The dimensional parameters include length, width and height. The spatial connection methods include welding, bolt connection and nesting relationship between components. The environmental boundary conditions include environmental parameters and the terrain slope and road capacity around the building in GIS geographic spatial data.

[0017] Preferably, the intelligent decision-making module includes a dynamic reasoning unit, a path optimization unit and a strategy generation unit. The dynamic reasoning unit is used to construct a state node set using a dynamic Bayesian network based on the physical simulation results, update the posterior probability of each node in the state node set through a particle filtering algorithm, and use the dynamic Bayesian network to establish a causal dependency relationship between nodes based on a dynamic causal reasoning mechanism, and output a fire risk assessment result. The path optimization unit is used to construct a path optimization objective function based on a multi-objective optimization algorithm according to the fire risk assessment result, and output an evacuation path. The strategy generation unit is used to generate an emergency strategy based on the fire risk assessment result and the evacuation path, combined with the distribution of building fire protection facilities and surrounding rescue resource information.

[0018] Preferably, the state node set includes fire source intensity, temperature field, structural deformation, smoke concentration and evacuation difficulty, and the emergency strategy includes equipment control instructions and traffic scheduling plan.

[0019] Preferably, the interactive control module includes a transmission interaction unit and a linkage control unit. The transmission interaction unit is used to transmit various data in real time through the transmission equipment and perform visual interactive operations using the interactive equipment. The transmission equipment includes an Ethernet switch, a 5G industrial gateway and an optical fiber transmission equipment. The interactive equipment includes a three-dimensional visualization large screen, AR interactive glasses and a mobile smart terminal. The linkage control unit is used to link the fire-fighting equipment and the traffic system according to the emergency strategy to perform hierarchical control. The hierarchical control includes: first-level response, second-level response and third-level response. The first-level response is when the temperature field dynamic data in the physical simulation results shows that the temperature exceeds a preset threshold, triggering the early warning information to be pushed to the interactive device. The second-level response is when the smoke diffusion dynamic data shows that the smoke concentration exceeds a preset threshold, automatically triggering the sprinkler system to start, the fire shutter to close and the smoke exhaust fan to run the equipment control instructions. The third-level response is when the structural deformation dynamic data shows that the probability of structural yield exceeds a preset threshold, linking the fire rescue command system and sending a traffic scheduling plan to the traffic control platform to adjust the timing of traffic lights around the fire point to open an emergency channel.

[0020] The urban building fire safety intelligent management method based on the multi-source integration of BIM and GIS is applied to the above-mentioned urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS, and includes the following steps:

[0021] Multi-dimensional sensing: access BIM building detail data and GIS geospatial data, and collect multi-dimensional data of buildings in real time;

[0022] Data fusion: Use the spatiotemporal attention mechanism to perform spatiotemporal alignment and feature fusion on BIM building detail data, GIS geospatial data, and multi-dimensional data, and perform standardization to obtain fused data;

[0023] Coupling calculation: Using a multi-physics field coupling algorithm, a fire dynamics model that includes the interaction of temperature field, structural mechanics field, and smoke diffusion field is constructed based on the fusion data. The dynamic process of temperature field, structural deformation, and smoke diffusion is simulated to obtain physical simulation results.

[0024] Intelligent decision-making: Generate evacuation routes and emergency strategies based on physical simulation results through dynamic causal reasoning and multi-objective optimization algorithms;

[0025] Interactive control: Real-time transmission and visual interaction of various data, and linkage of fire-fighting equipment and traffic systems to implement hierarchical control according to emergency strategies.

[0026] This invention provides an intelligent management platform for urban building fire safety based on the multi-source integration of BIM and GIS. It has the following beneficial effects:

[0027] 1. The present invention comprehensively collects and accesses BIM building detail data, GIS geographic space data and real-time multi-dimensional data, and uses the spatiotemporal attention mechanism to complete the spatiotemporal alignment and feature fusion of multi-source data to form standardized fused data. It uses the multi-physics field coupling algorithm to construct a fire dynamics model that includes the interaction of temperature field, structural mechanics field and smoke diffusion field, dynamically simulates the evolution law of each physical field during the development of the fire, combines dynamic causal reasoning and multi-objective optimization algorithm, and generates evacuation routes and emergency strategies adapted to the dynamic changes of the fire based on the real-time physical simulation results, realizing the characterization and real-time adaptation of the dynamic evolution law of temperature field, structural deformation and smoke diffusion during the development of the fire.

[0028] 2. This invention adopts the spatiotemporal attention mechanism to perform spatiotemporal alignment and feature reorganization, which not only retains the BIM model's accurate depiction of the microscopic features of building components, but also integrates GIS's global description of the macroscopic geographical environment. At the same time, it incorporates the dynamic changes of real-time sensor data, so that subsequent simulations and decisions are based on more comprehensive and accurate data, effectively improving the information fragmentation problem caused by heterogeneous multi-source data formats and spatiotemporal dislocation, and improving data utilization and consistency.

[0029] 3. The present invention solves the heat conduction equation, structural dynamics equation, and temperature-dependent thermal conductivity formula through a thermal-mechanical coupling calculation unit to characterize the dynamic relationship between the temperature field and structural deformation. With the help of the fractional-order diffusion equation of the smoke diffusion calculation unit, the non-classical influence of the complex building structure on smoke diffusion is considered, so that the interaction between the temperature field, structural mechanics field, and smoke diffusion field is realistically restored, thereby being closer to the actual evolution process of the fire and providing a more reliable physical basis for risk assessment.

[0030] 4. The present invention constructs a state node set based on a dynamic Bayesian network through a dynamic reasoning unit, and uses a particle filter algorithm and a dynamic causal reasoning mechanism to explore the evolution law of fire risk, so that the output fire risk assessment results are more timely and accurate; the multi-objective optimization function constructed by the path optimization unit comprehensively considers the path length, heat exposure and personnel density, and the generated evacuation path takes into account both safety and efficiency; the strategy generation unit combines the building fire protection facilities and the surrounding rescue resources to generate a hierarchical emergency strategy, so that the decision-making is shifted from experience-driven to data and model-driven, which improves the scientificity and pertinence of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is the architecture diagram of the urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS proposed in this invention;

[0032] Figure 2 This is a diagram of the multi-dimensional sensing module architecture of the urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS proposed by the present invention;

[0033] Figure 3 This is a diagram of the data fusion module architecture of the urban building fire safety intelligent management platform based on BIM and GIS multi-source fusion proposed by the present invention;

[0034] Figure 4 This is the architecture diagram of the coupled calculation module of the urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS proposed in this invention;

[0035] Figure 5 This is the intelligent decision-making module architecture diagram of the urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS proposed by the present invention;

[0036] Figure 6 This is the interactive control module architecture diagram of the urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS proposed by the present invention;

[0037] Figure 7 This is a flow chart of the intelligent management method for urban building fire safety based on multi-source fusion of BIM and GIS proposed in this invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Please see the attached Figure 1 -Attached Figure 6 The embodiment of the present invention provides an urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS, including:

[0040] Multi-dimensional sensing module: used to access BIM building detail data and GIS geospatial data, and collect multi-dimensional data of the building in real time;

[0041] Furthermore, the multi-dimensional sensing module includes a sensor unit, a data access unit and an edge preprocessing unit. The sensor unit is used to collect multi-dimensional data of the building in real time through sensing equipment. The sensing equipment includes a fiber grating temperature sensor, a distributed acoustic sensor, a temperature and humidity sensor, a smoke concentration sensor and a pressure sensor. The multi-dimensional data includes the building structure status, environmental parameters and fire-fighting equipment operation data. The data access unit is used to access BIM building detail data using an IFC standard parsing tool and access GIS geospatial data using a geospatial data conversion interface. The edge preprocessing unit is used to filter out abnormal data in the multi-dimensional data, BIM building detail data and GIS geospatial data using a sliding window algorithm.

[0042] Furthermore, the building structure status includes structural temperature and vibration displacement, environmental parameters include wind speed, humidity and smoke concentration, fire equipment operation data includes sprinkler system pressure and fire door switch status, BIM building detail data includes the geometric dimensions, material properties and location of fire protection facilities of building components, and GIS geospatial data includes the terrain around the building, road network and distribution of fire protection resources.

[0043] Specifically, the multi-dimensional sensing module, as the platform's data collection and access module, provides foundational data for subsequent modules. By integrating BIM building detail data, GIS geospatial data, and real-time field data, it builds a multi-source, multi-dimensional raw data source, laying the foundation for the platform's intelligent analysis and decision-making. Generally speaking, a multi-dimensional sensing module includes a sensor unit, a data access unit, and an edge preprocessing unit. These units work together to implement the entire process from data collection and access to preliminary processing.

[0044] Specifically, the sensor unit is primarily used to collect multi-dimensional data about a building in real time through various sensing devices to reflect its real-time status. In some embodiments, the sensing devices include fiber Bragg grating temperature sensors, distributed acoustic sensors, temperature and humidity sensors, smoke concentration sensors, and pressure sensors. Fiber Bragg grating temperature sensors are typically deployed on the surfaces of key building components, such as steel beams and columns and concrete slabs, to collect structural temperature data related to the building's structural status. Distributed acoustic sensors, via optical fiber cables laid along evacuation corridors and pipe shafts, collect vibration and displacement data of the building structure to reflect its dynamic deformation. Temperature and humidity sensors are often installed indoors and outdoors, as well as at ventilation openings, to collect environmental parameters such as wind speed and humidity. Smoke concentration sensors are deployed in public areas, such as corridors and halls, to monitor smoke concentration. Pressure sensors, connected to sprinkler system piping and fire door control circuits, collect sprinkler system pressure and fire door switch status data related to firefighting equipment operation. Thus, the multi-dimensional data collected by the sensor unit encompasses the building's structural status, environmental parameters, and firefighting equipment operation data, comprehensively reflecting the building's real-time fire safety status.

[0045] As an option, the data access unit is used to connect BIM building detail data with GIS geospatial data, so as to incorporate building micro-details and macro-geographic information into the platform data system. Generally, the data access unit uses IFC standard parsing tools to connect to BIM building detail data. By parsing the IFC format file, it extracts information such as the geometric dimensions, material properties, and location of fire protection facilities of building components. In one embodiment, the geometric dimensions include the length of beams and the cross-sectional dimensions of columns, the material properties include the density of steel and the fire resistance limit of concrete, and the location of fire protection facilities includes the three-dimensional coordinates of sprinkler heads and the distribution of fire hydrants. At the same time, the data access unit uses a geospatial data conversion interface to access GIS geospatial data. By converting and reading GIS vector data and raster data, it obtains macro-geographic information such as the terrain around the building, the road network, and the distribution of fire protection resources, thereby achieving preliminary access and integration of BIM and GIS data.

[0046] Specifically, the edge preprocessing unit receives the multi-dimensional data collected by the sensor unit and the BIM building detail data and GIS geospatial data accessed by the data access unit, and performs preliminary processing on this data to filter out abnormal data, improve data quality, and provide reliable input for the subsequent data fusion module. Generally, the edge preprocessing unit uses a sliding window algorithm to filter abnormal data. Its core principle is to set a certain time window, perform statistical analysis on the data within the window, and determine whether the data is within the normal fluctuation range. If it exceeds the range, it is judged as abnormal data and filtered.

[0047] As a possible implementation, the sliding window algorithm uses a 10-second window size, analyzing consecutive data points every 10 seconds. For structural temperature data in building structure status, the abnormality judgment condition is a temperature fluctuation exceeding 5°C / s, which can be expressed as: ΔT / Δt>5°C / s, where ΔT is the temperature difference between two adjacent moments within the window, and Δt is the time interval between the two adjacent moments. When these conditions are met, the temperature data is considered abnormal. For sprinkler system pressure data in firefighting equipment operation data, the abnormality judgment condition is a pressure fluctuation exceeding 20% ​​of the normal operating pressure. Similarly, the fluctuation rate is calculated by combining the pressure difference within the sliding window with the time interval. If it exceeds the threshold, it is considered abnormal. Through this processing, the edge preprocessing unit can effectively filter abnormal data generated by sensor failure or external interference, ensuring the high accuracy and reliability of the data output to the data fusion module, providing a solid data foundation for subsequent deep integration of BIM and GIS data and multi-physics field coupling calculations.

[0048] In some embodiments, the edge preprocessing unit not only filters abnormal data but also converts the data format to initially unify the formats of multi-dimensional data, BIM building detail data, and GIS geographic spatial data, so as to facilitate further processing by the data fusion module, thereby achieving initial collaboration of multi-source data in the access stage and providing initial support for the platform's full-process intelligent management.

[0049] Data fusion module: It is used to perform spatiotemporal alignment and feature fusion of BIM building detail data, GIS geospatial data and multi-dimensional data using the spatiotemporal attention mechanism, and perform standardization processing to obtain fused data;

[0050] Furthermore, the data fusion module includes a data preprocessing unit, a spatiotemporal alignment unit and a feature fusion unit. The data preprocessing unit is used to use the Z-score standardization formula to perform format conversion and normalization processing on BIM building detail data, GIS geographic spatial data and multi-dimensional data to obtain standardized data. The spatiotemporal alignment unit is used to use the dynamic time warping algorithm to perform time alignment based on the standardized data, and use the improved iterative nearest point algorithm to perform coordinate alignment to obtain spatiotemporal aligned data. The feature fusion unit is used to use the spatiotemporal attention mechanism to calculate the time attention weight and the space attention weight based on the spatiotemporal aligned data, perform feature reorganization on the spatiotemporal aligned data through weight distribution, generate a fused feature vector, and output the fused data.

[0051] Specifically, the data fusion module, as the platform's data processing core, is responsible for integrating and optimizing the multi-source data output by the multi-dimensional sensing module. Through spatiotemporal alignment, feature fusion, and standardization, it transforms dispersed BIM building detail data, GIS geospatial data, and multi-dimensional data into unified, fused data, providing high-quality input data for subsequent coupled calculation modules. Generally, the data fusion module includes a data preprocessing unit, a spatiotemporal alignment unit, and a feature fusion unit. These units work together to gradually achieve deep fusion of multi-source data.

[0052] Specifically, the data preprocessing unit is used to receive the multi-dimensional data, BIM building detail data, and GIS geospatial data output by the multi-dimensional sensing module, and perform format conversion and normalization on these data to eliminate data format differences and dimensional effects. In some embodiments, the data preprocessing unit uses the Z-score normalization formula to normalize the data, which is: ,in, For original data, such as component length in BIM building detail data, terrain elevation in GIS geospatial data, temperature value in multi-dimensional data, etc. is the mean of this type of data, is the standard deviation of this type of data, This is standardized data. Through the above processing, BIM component dimension data of different magnitudes, GIS coordinate data, and temperature data collected by sensors are converted into standardized data with a mean of 0 and a standard deviation of 1, avoiding interference in the subsequent fusion process caused by differences in data magnitude. At the same time, the data preprocessing unit also uniformly converts data formats, such as converting the IFC format of BIM data, the SHP format of GIS data, and the CSV format of sensor data into a JSON format that the platform can recognize, thus obtaining standardized data.

[0053] As an option, the spatiotemporal alignment unit is used to receive the standardized data output by the data preprocessing unit, and eliminate the misalignment of multi-source data in the spatiotemporal dimension through time alignment and spatial registration. Generally, the spatiotemporal alignment unit uses a dynamic time warping algorithm for time alignment. In view of the timestamp differences between the multi-dimensional time series data collected by the sensor and the BIM and GIS data, in one embodiment, the multi-dimensional time series data includes the temperature change curve over time and the time series fluctuation of the smoke concentration. Dynamic planning is used to find the optimal time matching path to keep the time axis of different data consistent. For example, for temperature data with a sampling frequency of 1Hz and smoke concentration data with a sampling frequency of 0.5Hz, the dynamic time warping algorithm is used to adjust the time series of the two to the same time interval to ensure the comparability of the data in the time dimension.

[0054] In one possible implementation, the spatiotemporal alignment unit uses an improved iterative closest point algorithm for spatial coordinate registration to resolve the coordinate system differences between BIM building detail data and GIS geospatial data. The algorithm optimizes the rotation matrix through continuous iteration. With translation vector , so that the local coordinates of BIM components can be accurately matched with the geodetic coordinates of GIS. The core formula of the improved iterative closest point algorithm is: ,in, is the local coordinate point of the BIM component, is the corresponding GIS geodetic coordinate point, is a 3×3 rotation matrix, dimensionless, is a 3×1 translation vector. The sum of squared errors is minimized through iterative calculation, thereby achieving spatial alignment between BIM and GIS data and obtaining spatiotemporal alignment data.

[0055] Specifically, the feature fusion unit receives the spatiotemporal alignment data output by the spatiotemporal alignment unit and uses the spatiotemporal attention mechanism to reorganize and fuse the data features to highlight the importance of key features. Generally, the feature fusion unit first converts the spatiotemporal alignment data into a feature matrix, where row vectors represent different data features, such as component material features in BIM, road network features in GIS, and temperature features in sensors, and column vectors represent different spatiotemporal nodes.

[0056] As a possible implementation, the feature fusion unit calculates the temporal attention weight and spatial attention weights , where the temporal attention weight The calculation formula is: ,in, is the query matrix of the time dimension, which is composed of time series feature data. is the key matrix of the time dimension, which is composed of time series feature data. is the feature dimension, is the normalization function, is the time attention weight, dimensionless, reflecting the degree of influence of data at different times on the fusion result. For example, the time attention weight corresponding to the temperature data at the time of fire will be higher than that at normal times.

[0057] Spatial attention weight The calculation formula is: ,in, is the query matrix of spatial dimension, which is composed of spatial feature data. is the key matrix of spatial dimension, which is composed of spatial feature data. is the spatial attention weight, which is dimensionless and reflects the correlation strength of different spatial location features. For example, the spatial attention weight corresponding to the BIM component features and GIS road features near the fire source will be relatively high.

[0058] In some embodiments, the feature fusion unit reorganizes the spatiotemporal alignment data by weight assignment, assigning the temporal attention weight and spatial attention weights The data is then multiplied by the corresponding feature matrices and summed to generate a fused feature vector, which is then output as fused data. This fused data not only retains the microscopic features of BIM architectural details and the macroscopic features of GIS geographic space, but also integrates the real-time dynamic characteristics of multi-dimensional data, providing data support for the coupled calculation module to build an accurate fire dynamics model.

[0059] Coupling calculation module: Used to use the multi-physics field coupling algorithm to construct a fire dynamics model that includes the interaction of temperature field, structural mechanics field and smoke diffusion field based on the fusion data, and simulate the dynamic process of temperature field, structural deformation and smoke diffusion to obtain physical simulation results;

[0060] Furthermore, the coupling calculation module includes a model construction unit, a thermal coupling calculation unit, a smoke diffusion calculation unit and a simulation result integration unit. The model construction unit is used to extract the parameters of the building based on the fusion data, and construct a fire dynamics model including the interaction of the temperature field, structural mechanics field and smoke diffusion field based on the multi-physics field coupling algorithm. The thermal coupling calculation unit is used to solve the dynamic relationship between the temperature field and the structural deformation based on the heat conduction equation, the structural dynamics equation and the temperature-dependent thermal conductivity formula according to the fire dynamics model, and output the temperature field dynamic data and the structural deformation dynamic data. The smoke diffusion calculation unit is used to simulate the smoke diffusion process based on the fractional-order diffusion equation according to the temperature field dynamic data and environmental parameters, and output the smoke diffusion dynamic data. The simulation result integration unit is used to associate and integrate the temperature field dynamic data, the structural deformation dynamic data and the smoke diffusion dynamic data according to the time series and the spatial coordinates, and output the physical simulation results including the temperature field, structural deformation and the smoke diffusion dynamic process.

[0061] Furthermore, the parameters include component thermal parameters, geometric topological relationships and environmental boundary conditions. Component thermal parameters include material density, constant pressure specific heat capacity, benchmark thermal conductivity and temperature coefficient. Geometric topological relationships include the three-dimensional coordinates, dimensional parameters and spatial connection methods of building components. Dimensional parameters include length, width and height. Spatial connection methods include welding, bolt connection and nesting relationship between components. Environmental boundary conditions include environmental parameters and the terrain slope and road capacity around the building in GIS geographic spatial data.

[0062] Specifically, the coupled calculation module is responsible for converting the fused data output by the data fusion module into quantitative simulation results of the fire process. By constructing a multi-physics field coupled fire dynamics model, it depicts the dynamic interaction between the temperature field, structural mechanics field, and smoke diffusion field, providing a scientific physical basis for the intelligent decision-making module. Generally speaking, the coupled calculation module includes a model construction unit, a thermal coupling calculation unit, a smoke diffusion calculation unit, and a simulation result integration unit. Each unit is connected in sequence according to the data flow, gradually realizing the complete process from data input to simulation result output.

[0063] Specifically, the model construction unit receives the fused data output by the data fusion module and extracts key parameters required for constructing the fire dynamics model. These parameters include component thermal parameters, geometric topological relationships, and environmental boundary conditions. Optionally, component thermal parameters are primarily derived from material properties within the BIM building detail data, including material density, specific heat capacity at constant pressure, baseline thermal conductivity, and temperature coefficient, reflecting the rate of change of thermal conductivity with temperature. Geometric topological relationships are based on the 3D coordinates and dimensional parameters of the BIM, including the 3D coordinates of building components and their spatial connections, such as welded joints between beams and columns and the nested relationships between pipes and walls. Environmental boundary conditions integrate the terrain slope surrounding the building from GIS geospatial data, road capacity, and environmental parameters collected by the multi-dimensional sensing module. By extracting and integrating these parameters, the model construction unit constructs a fire dynamics model based on a multi-physics coupling algorithm, integrating the interaction of temperature, structural mechanics, and smoke diffusion fields, providing a foundational framework for subsequent simulation calculations.

[0064] As a possible implementation method, the thermal coupling calculation unit is used to receive the fire dynamics model and related parameters output by the model building unit, and realize the coupled calculation of the temperature field and the structural mechanics field by solving the heat conduction equation, the structural dynamics equation, and the temperature-dependent thermal conductivity formula. Among them, the heat conduction equation is used to describe the heat transfer process in the building components, and the expression is: ,in, is the material density, is the specific heat capacity at constant pressure, is the component temperature, For time, is the thermal conductivity, is the gradient operator, is the fire heat release rate, which is determined by the fire source characteristic parameters in the fused data.

[0065] The structural dynamics equation is used to describe the relationship between the force and deformation of building structures under high temperature. The expression is: ,in, is the stress tensor, where Corresponding to x, y, and z directions respectively; is the divergence of the stress tensor, is the gravitational acceleration component, is the component acceleration component.

[0066] The temperature-dependent thermal conductivity formula is used to correct the thermal conductivity of materials at different temperatures. The expression is: ,in, is the thermal conductivity at the base temperature, is the temperature coefficient.

[0067] By coupling and solving the above three equations, the thermal coupling calculation unit outputs the dynamic data of temperature field, i.e., the temperature distribution at different times and positions, and the dynamic data of structural deformation, i.e., the changes of beam deflection and column lateral displacement over time.

[0068] Specifically, the smoke diffusion calculation unit is used to receive the temperature field dynamic data output by the thermal coupling calculation unit and environmental parameters such as wind speed and humidity collected by the multi-dimensional sensor module. It simulates the diffusion process of smoke inside and around the building based on the fractional-order diffusion equation. The fractional-order diffusion equation takes into account the non-classical effects of complex building structures such as stairwells and duct shafts on smoke diffusion. The fractional-order diffusion equation is: ,in, is the flue gas concentration, is the time fractional order, which is dimensionless and ranges from 0.5 to 1.5, reflecting the memory effect of smoke diffusion. is the spatial fractional order, which is dimensionless and ranges from 1 to 2, reflecting the non-local diffusion characteristics. is the diffusion coefficient, which is determined by the temperature gradient in the temperature field dynamic data and the wind speed in the environmental parameters; is a spatial fractional operator, is the smoke source term, which is determined by the fire source location and combustion rate. By solving the above equation, the smoke diffusion calculation unit outputs the smoke diffusion dynamic data, that is, the spatial distribution of smoke concentration at different times.

[0069] Generally, the simulation result integration unit is used to receive the temperature field dynamic data, structural deformation dynamic data, and smoke diffusion dynamic data output by the thermal coupling calculation unit, and the smoke diffusion dynamic data output by the smoke diffusion calculation unit. It then integrates these three types of data in a time series (e.g., one time step per second) and spatial coordinates (e.g., GIS-based geodetic coordinates). For example, the temperature value, structural displacement, and smoke concentration value at a certain location at t = 60 seconds are matched to form comprehensive state data for that time-space node. Through this integration, the simulation result integration unit outputs physical simulation results that include the temperature field, structural deformation, and smoke diffusion dynamic processes, providing a quantitative basis for risk assessment and strategy generation in the intelligent decision-making module.

[0070] In some embodiments, the simulation result integration unit will also perform secondary association between the integrated physical simulation results and BIM building detail data and GIS geographic spatial data, such as mapping the temperature field dynamic data to the specific components of the BIM model and superimposing the smoke diffusion dynamic data to the road network of the GIS base map, so as to enhance the spatial intuitiveness of the simulation results.

[0071] Intelligent decision-making module: used to generate evacuation routes and emergency strategies based on physical simulation results through dynamic causal reasoning and multi-objective optimization algorithms;

[0072] Furthermore, the intelligent decision-making module includes a dynamic reasoning unit, a path optimization unit and a strategy generation unit. The dynamic reasoning unit is used to construct a state node set based on the physical simulation results using a dynamic Bayesian network, update the posterior probability of each node in the state node set through a particle filtering algorithm, and use the dynamic Bayesian network to establish causal dependencies between nodes based on a dynamic causal reasoning mechanism, and output a fire risk assessment result. The path optimization unit is used to construct a path optimization objective function based on a multi-objective optimization algorithm according to the fire risk assessment results, and output an evacuation path. The strategy generation unit is used to generate an emergency strategy based on the fire risk assessment results and the evacuation path, combined with the distribution of building fire protection facilities and surrounding rescue resource information.

[0073] Furthermore, the state node set includes fire source intensity, temperature field, structural deformation, smoke concentration and evacuation difficulty, and the emergency strategy includes equipment control instructions and traffic scheduling plans.

[0074] Specifically, the intelligent decision-making module is responsible for converting the physical simulation results output by the coupled computing module into executable evacuation routes and emergency response strategies. It uses dynamic causal reasoning to explore the evolution of fire risk and combines it with multi-objective optimization algorithms to achieve a balance between safety and efficiency, providing intelligent support for building fire safety emergency response. Generally speaking, the intelligent decision-making module includes a dynamic reasoning unit, a path optimization unit, and a strategy generation unit. These units work together to form a complete decision-making chain from risk assessment to strategy generation.

[0075] Specifically, the dynamic reasoning unit receives the physical simulation results output by the coupled calculation module. These results include dynamic data on temperature fields, structural deformations, and smoke diffusion. Alternatively, the dynamic reasoning unit uses a dynamic Bayesian network to construct a state node set, which includes fire source intensity, temperature fields, structural deformations, smoke concentrations, and evacuation difficulty. Fire source intensity reflects the combustion power of a fire, temperature fields reflect the thermal state of each building area, structural deformations characterize the degree of damage to building components, smoke concentrations reflect the diffusion range of toxic gases, and evacuation difficulty comprehensively measures the ease or difficulty of evacuation.

[0076] In one possible implementation, the dynamic reasoning unit updates the posterior probability of each node in the state node set through a particle filtering algorithm, that is, it continuously revises the judgment of the true value of each state node based on the physical simulation results. At the same time, the dynamic reasoning unit uses a dynamic Bayesian network to establish causal dependencies between nodes based on a dynamic causal reasoning mechanism. For example, an increase in the intensity of a fire source may lead to an increase in the temperature field, which in turn may aggravate structural deformation and the spread of smoke concentration. The changes in structural deformation and smoke concentration jointly affect the difficulty of evacuation. Through the above reasoning process, the dynamic reasoning unit outputs a fire risk assessment result, which quantifies the fire risk level in different areas and at different times.

[0077] In some embodiments, the path optimization unit is used to receive the fire risk assessment results output by the dynamic reasoning unit and construct a path optimization objective function based on a multi-objective optimization algorithm to generate an optimal evacuation path. The objective function expression of the multi-objective optimization algorithm is: ,in, is the comprehensive target value; is the length of the evacuation path; For evacuation time; is the cumulative heat exposure experienced by personnel during the evacuation process, where Position on the path At the moment temperature; is the density of people on the path; is the weight coefficient, and , respectively reflecting the importance of path length, heat exposure risk, and congestion level in decision-making. By solving this objective function, the path optimization unit outputs an evacuation route that balances safety and efficiency. For example, it avoids high-temperature areas and passages with severe structural deformation, and prioritizes spacious routes with low smoke concentrations.

[0078] Specifically, the strategy generation unit receives the fire risk assessment results from the dynamic reasoning unit and the evacuation routes from the route optimization unit. It then generates an emergency strategy based on the distribution of building firefighting facilities in the BIM building detail data and surrounding rescue resource information in the GIS geospatial data. Typically, building firefighting facilities include the location and operating status of sprinkler systems, fire shutters, and smoke exhaust fans. Surrounding rescue resource information includes the location of fire stations, the number of fire trucks, and road accessibility.

[0079] As one possible implementation, the emergency strategy generated by the strategy generation unit includes equipment control instructions and traffic dispatch plans. In some embodiments, equipment control instructions are formulated based on the fire risk assessment results, such as triggering sprinkler system activation and fire shutter closing in high-risk areas. Traffic dispatch plans plan the optimal routes for fire trucks based on evacuation routes and the distribution of rescue resources, and coordinate surrounding traffic signals to ensure unobstructed rescue channels. Through this process, the strategy generation unit achieves the transformation from risk assessment to specific actions, ensuring the timeliness and effectiveness of emergency response.

[0080] In some embodiments, when constructing a state node set, the dynamic reasoning unit will use the location of fire-fighting facilities in the BIM building detail data and the road network in the GIS geographic spatial data as node attributes. For example, the location of the sprinkler system is associated with the temperature field node. Through dynamic causal reasoning, the inhibitory effect of fire-fighting facilities on the evolution of the temperature field is judged, so that the fire risk assessment results are more in line with the actual scenario.

[0081] Generally speaking, when constructing the objective function, the path optimization unit will introduce the evacuation channel width parameter in the BIM model and use it as a constraint to ensure that the generated evacuation path meets the personnel passage needs. At the same time, it will refer to the distribution of outdoor evacuation sites in the GIS geospatial data to ensure that the end point of the evacuation path is accurately connected with the safe area.

[0082] Interactive control module: used for real-time transmission and visual interaction of various data, and to link fire-fighting equipment and traffic systems to perform hierarchical control according to emergency strategies.

[0083] Furthermore, the interactive control module includes a transmission interaction unit and a linkage control unit. The transmission interaction unit is used to transmit various data in real time through the transmission equipment, and use the interactive equipment to perform visual interactive operations. The transmission equipment includes Ethernet switches, 5G industrial gateways and optical fiber transmission equipment. The interactive equipment includes three-dimensional visualization large screens, AR interactive glasses and mobile smart terminals. The linkage control unit is used to link fire-fighting equipment and traffic systems according to emergency strategies to perform hierarchical control. The hierarchical control includes: first-level response, second-level response and third-level response. The first-level response is when the temperature field dynamic data in the physical simulation results shows that the temperature exceeds the preset threshold, the warning information is triggered and pushed to the interactive device. The second-level response is when the smoke diffusion dynamic data shows that the smoke concentration exceeds the preset threshold, the sprinkler system is automatically triggered to start, the fire shutter is closed and the smoke exhaust fan is operated. The third-level response is when the structural deformation dynamic data shows that the probability of structural yield exceeds the preset threshold, the fire rescue command system is linked and the traffic dispatch plan is sent to the traffic control platform to adjust the timing of traffic lights around the fire point to open an emergency channel.

[0084] Specifically, the interactive control module fulfills the dual functions of data transmission and control execution. By transmitting various data in real time, enabling visual interaction, and linking related systems based on the emergency strategies output by the intelligent decision-making module, it ensures a closed-loop operation of fire emergency management. Generally, the interactive control module consists of a transmission and interaction unit and a linkage control unit. These two units work together to respectively handle data transmission and interaction and specific control execution, forming a bridge for interaction between the platform and the outside world.

[0085] Specifically, the transmission interaction unit is used to receive data output by the multi-dimensional sensing module, data fusion module, coupled calculation module, and intelligent decision-making module. This data includes raw sensor data, fused data, physical simulation results, fire risk assessment results, evacuation routes, and emergency strategies. Alternatively, the transmission interaction unit transmits this data in real time via transmission equipment, including Ethernet switches, 5G industrial gateways, and fiber optic transmission equipment. The Ethernet switches are used for data exchange between modules within the platform, while the 5G industrial gateways are responsible for transmitting data to remote monitoring centers and mobile terminals. The fiber optic transmission equipment ensures the high-speed and stable transmission of large-scale physical simulation results.

[0086] In one possible implementation, the transmission interaction unit uses interactive devices for visual interaction, including a 3D visualization screen, AR interactive glasses, and mobile smart terminals. The 3D visualization screen is typically deployed in the fire command center to comprehensively display the building panorama, the dynamic distribution of temperature fields and smoke diffusion, and evacuation route planning results, overlaid with the BIM model and GIS base map. AR interactive glasses are used by on-site rescue personnel to overlay physical simulation results with the actual scene through augmented reality technology, such as marking high-temperature areas and structural danger points in the field of view. Mobile smart terminals push warning information and evacuation instructions to management personnel and evacuees. Through the above-mentioned visual interaction, various types of data are presented in an intuitive and easy-to-understand format, improving decision-making efficiency and execution effectiveness.

[0087] In some embodiments, the transmission interaction unit utilizes data fragmentation and prioritization during data transmission. This allows for fragmented transmission of large amounts of data, such as physical simulation results, while prioritizing critical data, such as fire risk assessment results, to ensure the real-time delivery of important information. Furthermore, the transmission interaction unit supports bidirectional data exchange, receiving data output by the front-end module and collecting operational instructions and on-site information from interactive devices, creating a closed data loop.

[0088] Specifically, the linkage control unit is used to receive the emergency strategy output by the intelligent decision-making module, and to link the fire-fighting equipment and the traffic system to perform hierarchical control according to the strategy. Generally, fire-fighting equipment includes sprinkler systems, fireproof roller shutters, smoke exhaust fans and fire pumps, and the traffic system includes traffic lights, road monitoring and vehicle dispatch platforms. As a possible implementation method, hierarchical control includes first-level response, second-level response and third-level response. The first-level response is when the dynamic data of the temperature field in the physical simulation results output by the coupling calculation module shows that the temperature exceeds the preset threshold, the linkage control unit triggers the early warning information to be pushed to the interactive device, such as sending a high temperature warning to the mobile smart terminal of the property management personnel, and highlighting the temperature abnormality area on the three-dimensional visualization large screen.

[0089] The second-level response is when the dynamic data of smoke diffusion in the physical simulation results show that the smoke concentration exceeds the preset threshold. The linkage control unit automatically triggers equipment control instructions, including starting the sprinkler system, closing the fire curtain, and running the smoke exhaust fan. The equipment control instructions are transmitted to the control module of the fire-fighting equipment via the industrial bus, realizing remote control of the equipment to suppress the spread of fire and smoke diffusion. The third-level response is when the dynamic data of structural deformation in the physical simulation results show that the probability of structural yield exceeds the preset threshold. On the one hand, the linkage control unit links with the fire rescue command system to push detailed fire information and the distribution of dangerous areas in the building structure. On the other hand, it sends a traffic dispatch plan to the traffic control platform. By adjusting the timing of traffic lights around the fire point to open emergency channels, it ensures the rapid arrival of fire trucks and the safe evacuation of personnel.

[0090] In some embodiments, when executing hierarchical control, the linkage control unit will combine the location of fire-fighting equipment in the BIM building detail data and the road network topology in the GIS geographic spatial data to accurately locate the equipment and traffic nodes that need to be controlled. For example, it can determine the fire shutter number within a certain fire compartment based on the BIM model, and determine the location of traffic lights along the emergency passage based on GIS data, to ensure the accurate execution of control instructions.

[0091] Generally speaking, the interactive control module will also provide feedback monitoring on the execution status of various control instructions, collect the operating data of fire-fighting equipment and the status information of the traffic system through the multi-dimensional sensing module, and transmit it back to the platform in real time through the transmission interaction unit, so that managers can understand the control effect and make necessary adjustments to improve the reliability of emergency control.

[0092] Through the multi-dimensional sensing module, BIM building detail data and GIS geographic spatial data are accessed, and multi-dimensional data of the building is collected in real time to provide comprehensive original information for subsequent processing. The data fusion module uses the spatiotemporal attention mechanism to align the above data in time and space and fuse the features. After standardization, the fused data is obtained to improve the misalignment and weak feature correlation of multi-source data in the spatiotemporal dimensions. The coupling calculation module uses the multi-physics field coupling algorithm to construct a fire dynamics model based on the fused data, which includes the interaction of temperature field, structural mechanics field and smoke diffusion field. It dynamically simulates the evolution process of temperature field, structural deformation and smoke diffusion to achieve an accurate portrayal of the physical process of fire development. The intelligent decision-making module uses dynamic causal reasoning and multi-objective optimization algorithms to generate evacuation routes and emergency strategies that adapt to the dynamic changes of the fire based on the physical simulation results, ensuring the real-time and scientific nature of the decision. The interactive control module transmits various data in real time and interacts visually, linking fire-fighting equipment and traffic systems to perform hierarchical control according to emergency strategies, forming a complete closed loop from data collection, fusion, simulation to decision-making and control, and realizing real-time perception, precise simulation and intelligent response to the dynamic evolution laws of temperature fields, structural deformation and smoke diffusion during the development of fire, thus solving the problem that traditional urban building fire safety intelligent management platforms are difficult to adapt to the dynamic evolution laws of temperature fields, structural deformation and smoke diffusion during the development of fire.

[0093] Please see the attached Figure 7 The urban building fire safety intelligent management method based on the multi-source integration of BIM and GIS is applied to the above-mentioned urban building fire safety intelligent management platform based on the multi-source integration of BIM and GIS, and includes the following steps:

[0094] Multi-dimensional sensing: access BIM building detail data and GIS geospatial data, and collect multi-dimensional data of buildings in real time;

[0095] Data fusion: Use the spatiotemporal attention mechanism to perform spatiotemporal alignment and feature fusion on BIM building detail data, GIS geospatial data, and multi-dimensional data, and perform standardization to obtain fused data;

[0096] Coupling calculation: Using a multi-physics field coupling algorithm, a fire dynamics model that includes the interaction of temperature field, structural mechanics field, and smoke diffusion field is constructed based on the fusion data. The dynamic process of temperature field, structural deformation, and smoke diffusion is simulated to obtain physical simulation results.

[0097] Intelligent decision-making: Generate evacuation routes and emergency strategies based on physical simulation results through dynamic causal reasoning and multi-objective optimization algorithms;

[0098] Interactive control: Real-time transmission and visual interaction of various data, and linkage of fire-fighting equipment and traffic systems to implement hierarchical control according to emergency strategies.

[0099] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The intelligent management platform for urban building fire safety based on the multi-source integration of BIM and GIS is characterized by: include: Multi-dimensional sensing module: used to access BIM building detail data and GIS geospatial data, and collect multi-dimensional data of the building in real time; Data fusion module: It is used to perform spatiotemporal alignment and feature fusion of BIM building detail data, GIS geospatial data and multi-dimensional data using the spatiotemporal attention mechanism, and perform standardization processing to obtain fused data; Coupling calculation module: Used to use the multi-physics field coupling algorithm to construct a fire dynamics model that includes the interaction of temperature field, structural mechanics field and smoke diffusion field based on the fusion data, and simulate the dynamic process of temperature field, structural deformation and smoke diffusion to obtain physical simulation results; Intelligent decision-making module: used to generate evacuation routes and emergency strategies based on physical simulation results through dynamic causal reasoning and multi-objective optimization algorithms; Interactive control module: used for real-time transmission and visual interaction of various data, and to link fire-fighting equipment and traffic systems to perform hierarchical control according to emergency strategies.

2. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 1 is characterized by: The multi-dimensional sensing module includes a sensor unit, a data access unit and an edge preprocessing unit. The sensor unit is used to collect multi-dimensional data of the building in real time through sensing equipment. The sensing equipment includes a fiber Bragg grating temperature sensor, a distributed acoustic sensor, a temperature and humidity sensor, a smoke concentration sensor and a pressure sensor. The multi-dimensional data includes building structure status, environmental parameters and fire equipment operation data. The data access unit is used to access BIM building detail data using an IFC standard parsing tool and access GIS geospatial data using a geospatial data conversion interface. The edge preprocessing unit is used to filter abnormal data in the multi-dimensional data, BIM building detail data and GIS geospatial data using a sliding window algorithm.

3. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 2 is characterized by: The building structure status includes structural temperature and vibration displacement, the environmental parameters include wind speed, humidity and smoke concentration, the fire equipment operation data includes sprinkler system pressure and fire door switch status, the BIM building detail data includes the geometric dimensions, material properties and fire protection facility locations of building components, and the GIS geographic spatial data includes the terrain around the building, road network and fire protection resource distribution.

4. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 1 is characterized by: The data fusion module includes a data preprocessing unit, a spatiotemporal alignment unit and a feature fusion unit. The data preprocessing unit is used to use the Z-score standardization formula to perform format conversion and normalization processing on BIM building detail data, GIS geographic spatial data and multi-dimensional data to obtain standardized data. The spatiotemporal alignment unit is used to use the dynamic time warping algorithm to perform time alignment based on the standardized data, and use the improved iterative nearest point algorithm to perform coordinate alignment to obtain spatiotemporal aligned data. The feature fusion unit is used to use the spatiotemporal attention mechanism to calculate the time attention weight and the space attention weight based on the spatiotemporal aligned data, perform feature reorganization on the spatiotemporal aligned data through weight distribution, generate a fused feature vector, and output the fused data.

5. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 1 is characterized by: The coupling calculation module includes a model construction unit, a thermal coupling calculation unit, a smoke diffusion calculation unit and a simulation result integration unit. The model construction unit is used to extract the parameters of the building based on the fusion data, and construct a fire dynamics model that includes the interaction of the temperature field, structural mechanics field and smoke diffusion field based on the multi-physics field coupling algorithm. The thermal coupling calculation unit is used to solve the dynamic relationship between the temperature field and the structural deformation based on the fire dynamics model, the heat conduction equation, the structural dynamics equation and the temperature-dependent thermal conductivity formula, and output the temperature field dynamic data and the structural deformation dynamic data. The smoke diffusion calculation unit is used to simulate the smoke diffusion process based on the fractional-order diffusion equation according to the temperature field dynamic data and environmental parameters, and output the smoke diffusion dynamic data. The simulation result integration unit is used to associate and integrate the temperature field dynamic data, the structural deformation dynamic data and the smoke diffusion dynamic data with the spatial coordinates in time series, and output the physical simulation results including the temperature field, structural deformation and smoke diffusion dynamic process.

6. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 5 is characterized by: The parameters include component thermal parameters, geometric topological relationships and environmental boundary conditions. The component thermal parameters include material density, constant pressure specific heat capacity, reference thermal conductivity and temperature coefficient. The geometric topological relationships include the three-dimensional coordinates, dimensional parameters and spatial connection methods of building components. The dimensional parameters include length, width and height. The spatial connection methods include welding, bolt connection and nesting relationships between components. The environmental boundary conditions include environmental parameters and the terrain slope and road capacity around the building in GIS geographic spatial data.

7. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 1 is characterized by: The intelligent decision-making module includes a dynamic reasoning unit, a path optimization unit and a strategy generation unit. The dynamic reasoning unit is used to construct a state node set using a dynamic Bayesian network based on the physical simulation results, update the posterior probability of each node in the state node set through a particle filtering algorithm, and use the dynamic Bayesian network to establish causal dependencies between nodes based on a dynamic causal reasoning mechanism, and output a fire risk assessment result. The path optimization unit is used to construct a path optimization objective function based on a multi-objective optimization algorithm according to the fire risk assessment results, and output an evacuation path. The strategy generation unit is used to generate an emergency strategy based on the fire risk assessment results and the evacuation path, combined with the distribution of building fire protection facilities and surrounding rescue resource information.

8. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 7 is characterized by: The state node set includes fire source intensity, temperature field, structural deformation, smoke concentration and evacuation difficulty, and the emergency strategy includes equipment control instructions and traffic scheduling plan.

9. The urban building fire safety intelligent management platform based on BIM and GIS multi-source integration according to claim 1 is characterized by: The interactive control module includes a transmission interaction unit and a linkage control unit. The transmission interaction unit is used to transmit various data in real time through transmission equipment and perform visual interaction operations using the interactive equipment. The transmission equipment includes an Ethernet switch, a 5G industrial gateway and a fiber optic transmission equipment. The interactive equipment includes a three-dimensional visualization large screen, AR interactive glasses and a mobile smart terminal. The linkage control unit is used to link fire-fighting equipment and traffic systems to perform hierarchical control according to the emergency strategy. The hierarchical control includes: first-level response, second-level response and third-level response. The first-level response is to trigger an early warning message to be pushed to the interactive device when the temperature field dynamic data in the physical simulation results shows that the temperature exceeds a preset threshold. The second-level response is to automatically trigger the device control instructions to start the sprinkler system, close the fireproof roller shutter and operate the smoke exhaust fan when the smoke diffusion dynamic data shows that the smoke concentration exceeds a preset threshold. The third-level response is to link the fire rescue command system and send a traffic scheduling plan to the traffic control platform when the structural deformation dynamic data shows that the structural yield probability exceeds a preset threshold, adjusting the timing of traffic lights around the fire point to open an emergency channel.

10. An intelligent management method for urban building fire safety based on the multi-source integration of BIM and GIS is characterized by: The urban building fire safety intelligent management platform based on BIM and GIS multi-source fusion as described in any one of claims 1 to 9 comprises the following steps: Multi-dimensional sensing: access BIM building detail data and GIS geospatial data, and collect multi-dimensional data of buildings in real time; Data fusion: Use the spatiotemporal attention mechanism to perform spatiotemporal alignment and feature fusion on BIM building detail data, GIS geospatial data, and multi-dimensional data, and perform standardization to obtain fused data; Coupling calculation: Using the multi-physics coupling algorithm, a fire dynamics model that includes the interaction of temperature field, structural mechanics field, and smoke diffusion field is constructed based on the fusion data. The dynamic process of temperature field, structural deformation, and smoke diffusion is simulated to obtain physical simulation results. Intelligent decision-making: Generate evacuation routes and emergency strategies based on physical simulation results through dynamic causal reasoning and multi-objective optimization algorithms; Interactive control: Real-time transmission and visual interaction of various data, and linkage of fire-fighting equipment and traffic systems to implement hierarchical control according to emergency strategies.

Images