255 results about "Urban building" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention belongs to the technical field of knowledge maps and artificial intelligence, and discloses a building safety risk identification method of a big language model assisted knowledge map. According to the technical scheme, the overall process comprises the steps of data collection and preprocessing, construction of urban building structured table data, construction of an urban building safety knowledge graph, integration of the structured table data and knowledge graph data, model fine adjustment, model training, model performance evaluation and model recognition effect verification. According to the technical scheme, the large language model with the high semantic modeling capacity and the knowledge graph with the high graph structure expression capacity are integrated, the related knowledge of urban building safety is automatically extracted, constructed and integrated, high-risk events such as fire disasters and structural hidden dangers are recognized, and the informatization level and the intelligent level of urban building safety management are improved.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention belongs to the technical field of earthquake disaster scene construction, and discloses an earthquake disaster scene construction method based on big data, and the method comprises the following specific steps: 1, multi-source heterogeneous data collection and preprocessing ground structure data collection: employing a multi-platform remote sensing technology to obtain urban building group space distribution characteristics; a three-dimensional coupling model of an underground pipe network and a ground building is constructed by integrating multi-source data, so that the defect of isolated analysis of a ground system and an underground system in a traditional method is overcome, and synchronous simulation of a dynamic association process of pipe network fracture and building damage is realized; the additional influence of pipe network failure on the building foundation stability and the linkage effect of soil erosion caused by leakage fluid are accurately analyzed, meanwhile, a time-space association rule of pipe network fracture events and multiple types of secondary disasters such as building fire disasters and fire fighting failure is established, and the building group function paralysis risk and the disaster diffusion range under different damage degrees are dynamically predicted; and the disaster evolution path is displayed in real time through a visual platform.

The invention relates to the technical field of unmanned aerial vehicle application and urban airspace management, in particular to an intelligent site selection method and system for unmanned aerial vehicle take-off and landing points in an urban building group, and the intelligent site selection system for the unmanned aerial vehicle take-off and landing points in the urban building group specifically comprises the following steps: S1, carrying out the multi-source heterogeneous data fusion and high-precision three-dimensional environment modeling; s2, fluid dynamics simulation and airflow field pre-calculation are calculated; s3, real-time meteorological monitoring and dynamic risk correction; s4, establishing a quantitative safety evaluation model and multi-objective optimization site selection; and S5, visual decision support and feedback learning. According to the scheme, by deeply fusing multi-source heterogeneous geographic information data and constructing a high-precision three-dimensional digital twinborn city environment, accurate simulation and risk assessment of an airflow field around a building group are realized, geometric, material and semantic information is automatically extracted by using oblique photography, laser point cloud and a building information model, and the construction efficiency is improved. A unified three-dimensional grid with physical attributes is formed, and a reliable basis is provided for subsequent fluid calculation.

The invention relates to the technical field of building risk monitoring, in particular to an urban building safety evaluation method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a synthetic aperture radar image, an optical remote sensing image, a hyperspectral image, topographic data and building contour data of a to-be-evaluated region; based on the optical remote sensing image and the building contour data, identifying spatial form change information of each target building, and generating change parameters used for describing structure adjustment features; generating an interferogram based on the synthetic aperture radar image, extracting a corresponding deformation monitoring point, and generating a settlement parameter reflecting the displacement change of the building; based on the hyperspectral image and the topographic data, identifying spatial relationship information of the target building and the surrounding environment risk body, and generating external influence parameters; and based on the change parameter, the settlement parameter, the external influence parameter and the preset evaluation model, generating a safety evaluation result of each target building, thereby improving the efficiency of urban building safety evaluation.

The invention provides an urban building group carbon emission real-time monitoring and visualization system and method, and the system comprises a carbon emission data collection module which integrates multi-source data and carries out the denoising through a multi-mode perception sensor; the carbon emission calculation module is used for constructing a dynamic factor library, determining a fixed weight and accurately calculating carbon emission; the data transmission and storage module adopts a distributed account book and fog computing to ensure that the data is safe and efficient; and the carbon emission analysis module analyzes and predicts the carbon emission data by using a deep reinforcement learning algorithm based on transfer learning. The visual display module is used for forming a visual platform based on a digital twinning technology; and the early warning and decision support module is used for setting a dynamic carbon emission threshold value based on risk assessment and providing personalized emission reduction suggestions and decision support. According to the method, the accuracy of carbon emission calculation can be improved, multi-dimensional visual display is provided, and a user can conveniently analyze data and make decisions from different angles.

The invention relates to the technical field of urban earthquake disaster simulation, and provides a visualization system and method for urban building group earthquake disaster simulation, and the system comprises an offline data preprocessing and block organization module and a block dynamic data management and scheduling module. The GPU acceleration rendering module is used for mapping a global floor index to a vertex to endow the vertex with a unique identifier, and finally submitting the combined model to a GPU, and cooperating with a displacement texture and a user-defined shader to realize real-time rendering of a building form; the GPU parallel displacement picture moving module is deeply coupled to the GPU rendering pipeline and is used for resolving the vertex displacement and the color value in real time and injecting the vertex displacement and the color value into the rendering pipeline; and the efficient interaction module dynamically updates label contents and coordinates to realize three-dimensional interaction. According to the method, massive time sequence displacement data are coded into textures, and parallel calculation is performed by using the GPU vertex shader, so that real-time and smooth animation simulation of earthquake responses of all buildings is realized, and the performance bottleneck of a traditional CPU calculation mode is solved.

The invention belongs to the technical field of smart cities, and discloses a point cloud-based urban building roof wireframe reconstruction method, which comprises the following steps of: fusing a fast point feature histogram and a multi-scale roof geometric descriptor, generating robust point-by-point features, screening candidate angular point clusters in combination with a classification head, and adaptively segmenting the candidate point clusters based on density parameters by utilizing DBSCAN (Density-Based Spatial Clustering of Applications with Noise), so as to reconstruct a point cloud-based urban building roof wireframe. Initial inflection points are extracted through unsupervised clustering, noise is effectively suppressed, and irregular distribution is adapted; then multi-scale geometric information of an initial inflection point neighborhood is aggregated through an inflection point correction network, offset is learned to correct position deviation, and inflection point positioning precision is improved; and finally, the edge classification network automatically deduces the topological connection of the roof wireframe based on the geometrical relationship and feature relevance of prediction inflection points, so that error accumulation caused by dependence on manual rules in a traditional method is avoided, the generalization ability of a complex roof structure is enhanced, the correction network learns offset through a multi-scale context, and the inflection point positioning precision is remarkably improved.

The invention discloses a building three-dimensional reconstruction method and system for distributed photovoltaic potential assessment, and a medium, and the method comprises the steps: carrying out the data fusion according to the OpenStreetMap block vector data and three-dimensional building model data of a region, obtaining a three-dimensional model of a single building, carrying out the merging and classification algorithm of multi-layer patches, and obtaining a three-dimensional model of a single building; automatically identifying a surface patch to which the building roof belongs, and dividing the building into a roof layer and a main body layer; a digital elevation model of a roof layer is generated, and roof structure reconstruction is carried out according to different roof types; and carrying out adaptive elevation stretching based on the OSM vector data contour to obtain a main body layer reconstruction result, and finally outputting reconstruction results of all buildings in the area. Large-scale urban building rapid modeling based on the method is achieved, and details of related roof structures are effectively reserved.

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.

The invention relates to the technical field of image data processing, in particular to an urban building three-dimensional reconstruction method and device, and the method comprises the steps: obtaining a multi-view satellite orthoimage from the same scene, satellite-borne laser height measurement data of a target region, a digital terrain model and two-dimensional building vector data; performing accurate geometric registration on the multi-view satellite orthoimage, and executing dense matching to generate an initial parallax point cloud; and combining azimuth angle information of the multi-view orthoimage to construct a disparity map with accurate plane positioning. And calculating an elevation value above the earth surface by using satellite-borne laser altimetry data and a digital terrain model. And jointly estimating a parallax elevation proportionality coefficient by using the elevation value and the parallax value so as to convert the parallax map into a height map. And generating three-dimensional building vector data of the target area in combination with the two-dimensional building vector data. Therefore, the problems that cost based on surveying and mapping level remote sensing data is high, data collection is limited, and the requirement for global city three-dimensional reconstruction cannot be met in the prior art are solved.

The invention relates to the technical field of task scheduling, in particular to an intelligent multi-unmanned aerial vehicle cooperative task execution scheduling system and method. The method comprises the following steps: periodically scanning a space in front of a low-altitude flight path of an urban building dense area through a visual SLAM system, and processing visual information in real time by using an airborne edge calculation unit to obtain near-earth space point cloud data of a continuous time sequence; projecting obstacle height information in the near-earth space point cloud data to predefined flight grid units to obtain grid obstacle height data of each grid unit at different time points; and comparing the variation values of the grid obstacle height data in the same grid unit in adjacent time periods, and calculating the obstacle height change frequency in unit time. According to the method, the dynamic change characteristics of the obstacles in the environment can be quantitatively analyzed, the high-risk area is identified, and the challenge brought to multi-unmanned aerial vehicle cooperative task execution scheduling by the complex environment of the urban building dense area is overcome.

The invention provides an urban special-shaped building monomer reconstruction method based on inclination model data, and the method comprises the steps: carrying out the segmentation and recognition of a monomer building based on deep learning, European clustering and a graph cutting method, fitting a feature plane of the monomer building based on a point cloud plane fitting algorithm, recognizing a complex special-shaped building according to the normal vector gradient distribution of the feature plane, and carrying out the reconstruction of the complex special-shaped building. According to the method, the three-dimensional Mesh model of the special-shaped building is simplified, textures are filled and the like, reconstruction of a complex special-shaped building model is achieved, and the problems that an existing mainstream urban building monomer reconstruction algorithm is only suitable for common regular buildings, and automatic monomer segmentation and three-dimensional structured reconstruction of the special-shaped building cannot be achieved are solved. And the automation rate and the efficiency of reconstruction of the special-shaped building in the urban scene are obviously improved.

The invention relates to the technical field of construction site monitoring, in particular to a construction site monitoring system and equipment based on a remote sensing image technology, and the system comprises a remote sensing image collection module, an unmanned aerial vehicle platform, a low-orbit satellite platform and a ground mobile platform which all acquire remote sensing images of a construction site through sensors; the multi-modal fusion module is used for performing data fusion processing according to the remote sensing image to obtain multi-modal data; when the system is used, the unmanned aerial vehicle platform, the low-orbit satellite platform and the ground mobile platform are used for collecting remote sensing image data, various targets on a construction site are conveniently and accurately recognized, misrecognition is reduced, and the system is beneficial for images under complex environments such as different illumination and weather conditions and images under the complex background of urban buildings. The construction site target is identified and positioned, and in a data-deficient and new construction site environment, the self-supervised learning and transfer learning technology can effectively improve the learning ability of the model, reduce the dependence on annotated data, and quickly adapt to new task requirements.

The invention discloses an anomaly measurement system for an urban building facade, which comprises an image information acquisition module, a three-dimensional model generation module, an anomaly identification processing module, a comprehensive verification analysis module and an identification information output module, relates to the technical field of facade detection, and solves the problem that only for identification detection of a single anomaly type, the anomaly detection efficiency is high. According to the method, through standardization of an acquisition path and special scene processing, image information integrity is ensured, acquisition efficiency is improved, unmanned aerial vehicle GPS coordinates and total station control point coordinates are fused, a coordinate system is unified through seven-parameter conversion, absolute deviation of a model is reduced, and the accuracy of the system is improved. According to different characteristics of cracks, hollowing and peeling, double recognition logic of appearance characteristics and three-dimensional geometric characteristics is designed, the misjudgment rate is reduced, multiple types of anomalies are covered, a comprehensive verification module is introduced, and the deviation of an anomaly recognition result is reduced through 30% sample repeated measurement and a manual recheck mechanism.

The invention relates to the technical field of building disease detection, in particular to an urban building disease detection method and device, electronic equipment and a storage medium. Multi-modal image data formed by original visible light and thermal infrared image data is obtained, and an original thermal infrared image is subjected to geometric correction; calculating a mapping relation with an original visible light image so as to complete pixel-level registration, obtaining target multi-modal image data, inputting the target multi-modal image data into a hierarchical deep learning recognition model, recognizing building disease information, then performing three-dimensional space mapping, generating a building three-dimensional mesh model containing disease three-dimensional space setting coordinates, and finally performing three-dimensional mesh modeling. And then calculating a relationship between a model surface grid vertex and a disease point cloud density, generating a disease distribution thermodynamic diagram, analyzing disease aggregation characteristics in multiple dimensions according to the thermodynamic diagram, and quantitatively analyzing spatial correlation between the disease and a building construction node in combination with building component information. According to the invention, the urban building disease detection efficiency and precision are improved.

The present invention discloses a polarimetric SAR change detection method, device, equipment and medium, which relate to the field of radar image processing technology. The method acquires a time-series PolSAR image, uses JBLD divergence to calculate the similarity measure of the multi-phase covariance matrix; calculates a time-series edge intensity map based on the similarity measure; uses the time-series edge intensity map to initialize the cluster center and introduces a dynamic edge constraint mechanism to suppress superpixels from crossing the image edge during the iteration process, and outputs the superpixel segmentation result; constructs an image topology representation that integrates time-series feature similarity, spatial adjacency and cross-phase cross-feature similarity; constructs an energy function containing node cost and edge cost, solves the energy minimization problem through quadratic pseudo-Boolean optimization, and obtains a change detection map. The present invention can avoid errors caused by regional discontinuity and boundary fuzziness in superpixel segmentation, and exhibits strong robustness in both natural objects and complex urban building scenes.

The invention discloses an eVTOL vertical takeoff and landing field aircraft landing scheduling method, and relates to the technical field of aviation scheduling, and the method comprises the following steps: constructing a three-dimensional aerodynamic interference prediction mechanism fusing aircraft rotor wing downwash airflow and urban building reflection airflow, generating a local aerodynamic coupling strength distribution diagram based on real-time meteorological parameters and takeoff and landing sortie arrangement, and carrying out the three-dimensional aerodynamic interference prediction mechanism; the method is used for identifying high-interference areas. According to the invention, by constructing a three-dimensional aerodynamic interference prediction mechanism and evaluating the path sensitivity, advanced identification and risk path marking of a high-interference area are realized; landing time sequence disturbance and flight buffer control are combined, and the multi-machine rotor airflow overlapping probability is reduced. And adaptive flight control and rhythm regulation and control are carried out based on the disturbance level index, so that the flight safety, scheduling collaboration and airspace utilization efficiency of the eVTOL in the complex urban airspace are effectively improved, and multi-aircraft high-density safe collaborative landing is realized.

The invention relates to the technical field of image data processing, in particular to an urban building image processing method and system based on unmanned aerial vehicle inspection, and the method comprises the steps: collecting a gray image of the surface of a to-be-detected building through an unmanned aerial vehicle, and obtaining a to-be-corrected image; and mapping the to-be-corrected image to the same plane as a pre-stored to-be-measured building surface front view image by using homography transformation, and in the mapping process, solving a homography matrix by using a least square method of an adaptive weighted error term so as to complete image mapping based on the homography matrix and obtain a corrected image. According to the method, the accuracy of defect area identification can be improved, and when the homography matrix mapping the to-be-corrected image to the same plane as the front view image is solved through the least square method of the adaptive weighted error term, the influence of the defect area can be reduced, so that the accurate correction of the image can be realized based on the homography matrix with relatively high accuracy.

The invention relates to the technical field of data processing, and discloses a numerical simulation method for a synergistic effect of an urban heat island and high-temperature heat waves. The method comprises the following steps: collecting urban building form data to construct a data set, and combining remote sensing data to carry out local climate zoning; extracting a high-temperature heat wave process and performing grading by utilizing machine learning; analyzing the urban heat island intensity time sequence change based on the remote sensing data; constructing an urban canopy mode coupling hydrology and an artificial heat source; and inputting different levels of heat wave data to simulate a synergistic effect. The technical problem that the urban heat island effect and high-temperature heat wave synergistic effect mechanism cannot be accurately simulated in the prior art is solved, and the numerical simulation method for quantitatively evaluating the urban heat island intensity amplification coefficient under the condition of different grades of high-temperature heat waves is established.

The invention belongs to the technical field of three-dimensional modeling, and discloses a city building three-dimensional model refined construction method based on oblique photography. Comprising the steps of collecting inclined image data of a to-be-modeled urban area and performing data cleaning to obtain a modeling image data set; carrying out projection consistency analysis, identifying a boundary dislocation region and removing the boundary dislocation region to obtain a dislocation removal data set; shielding area identification is carried out, shielding compensation is carried out based on an identification result, and an enhanced three-dimensional point data set is output; executing detail reservation to obtain a triangular facet data set; normal optimization is carried out, plane adjustment is carried out on the triangular surface data set based on an optimization result, and a three-dimensional grid data set is obtained; constructing an initial three-dimensional model, and performing map mapping on the initial three-dimensional model to obtain a detail map set; constructing a fine building three-dimensional model in combination with the detail map set and the initial three-dimensional model; and the boundary definition, the structural integrity and the texture fineness of the urban building three-dimensional model are improved.

The invention provides an urban building three-dimensional fine model extraction method based on tomographic SAR point cloud regularization. The method comprises the following steps: separating building point cloud and ground point cloud from tomographic SAR point cloud; segmenting the building point cloud to obtain a facade point cloud and a roof point cloud; in the presence of the roof point cloud, reconstructing a roof contour by using the roof point cloud; under the condition that the roof point cloud is missing, the building thickness is estimated by using a ground blank area; and reconstructing a building three-dimensional model by using the roof contour reconstructed under the existence or absence of the facade footprint and the roof point cloud. According to the method, the facade is gradually segmented from thick to thin along the wall surface direction to reconstruct the fine facade footprint, and the representative line segments of the roof contour are selected and connected by combining the facade direction, so that the regular roof contour can be simply and conveniently extracted; and in the absence of the roof point cloud, estimating the thickness of the building roof by using the shielded area of the ground, and reconstructing a three-dimensional model of the building.

The invention discloses a collaborative improvement method for a horizontal wind speed algorithm in an urban block network model, and the method comprises the steps: enabling a high-resolution CFD wind field simulation to serve as a quasi-truth value, carrying out the comparison verification of wind speeds at different positions of the urban block network model and a quasi-truth value wind field, introducing local roughness and displacement height parameters which are combined with building features, and carrying out the calculation of a horizontal wind speed algorithm. The system evaluates the influence of the combination of the local roughness and the displacement height under different scales and the local friction speed inverted by the CFD on the wind speed simulation, selects the optimal parameter combination to replace the regional average roughness and the displacement height parameter according to the evaluation result, and optimizes the wind speed simulation capability of the urban block network model under the urban non-uniform underlying surface condition. The method has the advantages that the characteristic that the height of the underlying surface of the center of the city is not uniform can be more accurately represented by combining the local friction resistance speed, the local roughness and the displacement height parameters which are inversed by CFD simulation, so that the dragging effect of the city building is accurately simulated, and the horizontal wind speed closer to the reality can be obtained.

The invention belongs to the technical field of building three-dimensional visualization, and particularly relates to an urban building three-dimensional visual lightweight system based on data fusion, which comprises a real image acquisition module, a bridging module and a building object updating module. According to the scheme, through point cloud data comparison and vector analysis, elimination of disappearance building point clouds and addition of newly-added buildings, it is ensured that a non-change part model is unchanged, meanwhile, the large-scale point cloud processing calculation amount is reduced, octree block optimization and nearest neighbor search optimization are adopted, the point cloud matching calculation amount is further reduced, and a verification and model correction mechanism is utilized, so that the accuracy of point cloud matching is improved. Compared with a traditional static city model, the method is more suitable for dynamic scenes such as city planning and disaster monitoring; the height is reduced layer by layer from the view angle of the unmanned aerial vehicle, a middle view angle virtual image is rendered through an initial rough model, the feature difference with the ground view angle is gradually reduced, and the cross-view angle three-dimensional reconstruction quality is improved.

The invention discloses a resource evaluation method and system based on multi-landform feature correction, which remarkably improves the accuracy of wind speed and solar radiation data by combining physical constraint correction and machine learning methods, and particularly improves the accuracy of the wind speed and solar radiation data under the conditions of complex terrains and multiple earth surface types. The problem of systematic errors existing in ERA5 reanalysis data in the prior art is solved. Specifically, through elimination of invalid values of radiation data at night and correction of wind speed data based on surface roughness, deviation caused by factors such as terrain shielding, vegetation canopy reflectivity and urban building group wind resistance effect is effectively eliminated. The ERA5 data is trained and optimized by using a machine learning model such as a random forest, so that the wind speed and radiation data at different geographic positions, seasons and time periods can be more accurately corrected, and the corrected data is more in line with an actual observation result.

The invention relates to the technical field of photovoltaic distribution installation, in particular to a photovoltaic distribution decision-making method for a building group, which comprises the following steps: acquiring an original live-action image of a target area, and processing the original live-action image of the target area to obtain a target live-action image; obtaining meteorological characteristic data of the target area, and determining photovoltaic output data of the target area based on the target real scene image and the meteorological characteristic data of the target area to form a multi-scene power generation prediction data set; acquiring operation data of a power distribution system of the target building group, and establishing a comprehensive target function taking the total cost as an optimization target based on the operation data and the multi-scene power generation prediction data set; based on the constraint of the constraint condition set, solving the comprehensive target function, and obtaining and outputting a photovoltaic distribution target solution in the target area; according to the invention, the accuracy, safety and comprehensive economic benefits of distributed photovoltaic development of urban building groups are systematically improved.

The invention discloses an urban agglomeration anti-seismic damage prediction method and system based on an unmanned aerial vehicle technology and a deep learning model. The method comprises the following steps: constructing a typical building multi-angle aerial image database and a building earthquake damage database under different load coupling effects; training an urban building group identification model based on the aerial photo database to identify the type, geometry and texture information of the building; constructing and training horizontal and vertical seismic oscillation amplification coefficient prediction models for correcting seismic oscillation intensities under different topographic conditions; a typical building earthquake damage prediction model is constructed and trained, and the building damage grade is predicted in combination with the corrected earthquake vibration intensity; and finally, realizing three-dimensional visualization of the damage condition of the urban building group through a damage grade imaging module. According to the method, the problems of inaccurate urban building group modeling information, large earthquake damage analysis workload and low precision in the prior art are solved, and a scientific basis is provided for construction and evaluation of tough cities.

The invention provides a building group structure type identification method and device based on a multi-modal large model, and relates to the technical field of multi-modal large models. The method comprises the following steps: extracting observable building features through a multi-modal large model; establishing a graph enhanced multi-modal large model to perform matching and reasoning on the features and the knowledge graph; an expert auditing mechanism is introduced to correct an identification result, the knowledge graph is dynamically updated, and the graph enhanced multi-mode large model is finely adjusted; and establishing an urban building group rapid identification system, and establishing a rapid identification model based on deep learning and an interpretable identification system based on a graph enhanced multi-modal large model by using a knowledge graph. According to the method, the explainable reasoning capability of deep learning rapid classification and map enhancement is fused, and a structure identification closed-loop system with self-learning and self-updating capabilities is constructed. The method is suitable for scenes such as urban building census, anti-seismic evaluation and BIM auxiliary modeling, and has high accuracy, strong generalization and sustainable evolution capability.

The invention discloses a long-period regional scale building water retaining measure distribution method under typhoon flood, and the method comprises the steps: constructing a typhoon rainfall model of a research region, and obtaining a ten thousand-grade scale typhoon rainfall event library; constructing a training data set required by the typhoon rainfall-waterlogging maximum inundation proxy model; constructing a typhoon rainfall-waterlogging maximum inundation proxy model fusing the convolutional neural network and a gated circulation unit; determining urban building water retaining measure types and cost-benefit parameters; extracting all events of which the recurrence periods are smaller than a decision threshold; establishing an optimization model of a regional scale measure planning decision problem, and performing multi-objective optimization of minimum loss and minimum cost; and solving the optimization model of the regional scale measure planning decision problem to obtain a combined optimization solution of regional disaster reduction measures, and making a disaster reduction plan according to actual conditions. According to the method, cost-benefit optimization of different disaster reduction measure combinations under the regional scale is realized, and a scientific basis and decision support are provided for pre-disaster waterlogging prevention measure configuration.