462 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 relates to the technical field of knowledge maps, in particular to a construction method of an urban building safety risk knowledge map, which comprises the following steps of: firstly, selecting structured data, semi-structured data and non-structured data for constructing a subsequent knowledge map, then constructing a mode layer from the perspective of ontology construction in a top-down manner, and constructing a model layer from the perspective of ontology construction; according to the method, knowledge extraction is completed from existing data by utilizing mode information of an ontology, then the extracted knowledge data is fused and then stored, construction of a knowledge graph is completed, the knowledge extraction accuracy and efficiency are improved by expanding the building type covered by the knowledge graph and improving the integration mode of time and space data, and the knowledge extraction efficiency is improved. And a knowledge fusion strategy is optimized, so that the problems of incomplete building type coverage, insufficient spatio-temporal data integration, low knowledge extraction efficiency and accuracy, information fusion and the like in the prior art are solved.

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 provides a city hidden unmanned aerial vehicle identification method and system. The method comprises the following steps: acquiring geometric morphology and material dielectric constant data of an urban building group, and generating a building material reflection parameter set; secondly, based on reflection parameters, millimeter wave frequency band combinations adaptive to building attenuation characteristics are screened, millimeter wave signals are directionally transmitted, reflection components of metal parts and unmanned aerial vehicle composite materials are separated, and a three-dimensional millimeter wave imaging map fused with multipath interference suppression parameters is generated; collecting a radio frequency signal, compensating path loss by using scattering phase offset, extracting a harmonic component of a propeller motor and a frequency hopping residual component of a communication module, and generating a time-space synchronous radio frequency fingerprint coding sequence; through constructing a millimeter wave and radio frequency coupling model, cross validation is carried out on a reflection component and a harmonic component, and accurate identification of the hidden unmanned aerial vehicle in the urban building group shielding area is realized. According to the invention, the urban hidden unmanned aerial vehicle identification precision is improved.

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 discloses a roof photovoltaic identification and potential evaluation method based on improved TransUNet semantic segmentation. The method comprises the following steps: obtaining a building roof satellite image data set; cleaning and classifying the images in the data set according to whether roof features exist or not, and dividing out images with roofs; according to the roof image, roof features are extracted through the constructed and trained TransUNet semantic segmentation model, and a building roof area is identified and area division is carried out; and matching the processed image with the subject name, and positioning the subject to which the building belongs. According to the method, an innovative solution of a remote sensing technology and a machine learning method is integrated, the generalization ability of roof recognition semantic segmentation can be effectively improved, the roofs are accurately recognized and divided, and the method aims at achieving rapid and accurate evaluation of large-scale urban building roofs.

The invention discloses a green building microclimate regulation emission reduction and convergence increase method based on park biodiversity, and the method comprises the steps: obtaining biodiversity data and building environment parameters, constructing a three-dimensional ecological space digital twinborn model, and carrying out the multi-physics coupling analysis; loading boundary conditions to generate a comprehensive model, then carrying out microclimate dynamic simulation, training an ecological regulation model to generate initial regulation parameters, constructing a multi-objective optimization model in combination with dynamic regulation simulation information and building design parameters to obtain optimal parameters, and finally adjusting a regulation strategy based on real-time prediction. According to the method, by integrating biodiversity and building environment data and utilizing digital twinning, multi-field coupling simulation and intelligent optimization algorithms, collaborative optimization of microclimate adjustment, carbon emission reduction and carbon sink capacity improvement is achieved, a technical scheme is provided for deep fusion of green buildings and ecological systems, and the method is suitable for popularization and application. The method is suitable for the fields of urban building energy conservation and ecological environment protection.

The invention relates to the technical field of intelligent fire early warning, in particular to an urban fire intelligent early warning system which comprises an urban structure intelligent analysis module, a fire dynamic early warning simulation module, a fire prevention strategy optimization module and an environment influence intelligent analysis module. According to the method, the urban graph model is generated by using the geographic positions, materials, fire data and building age data of the urban buildings, the relevance and characteristics between the buildings are deeply analyzed, and the fire diffusion influence is simulated through the graph convolutional network, so that the prediction precision of the fire propagation path and speed is remarkably improved; the evacuation difficulty and risk level of a building under multiple fire source points are calculated in an auxiliary manner, a basis is provided for scientific planning of evacuation routes and fireproof isolation areas, emergency response and resource allocation are optimized, meteorological, population dynamics and holiday activity data analysis are integrated, the effect of environmental factors in fire early warning is enhanced, the accuracy and timeliness of early warning are ensured, and the early warning efficiency is improved. And the overall efficiency of an urban fire prevention system is improved.

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.

A method for automatically identifying a building roof structure based on airborne point cloud relates to the field of surveying and mapping, and is characterized by comprising the following steps: laser point cloud preprocessing, building house extraction, marshalling building roof patch laser point cloud segmentation, roof point cloud fitting plane equation set, and roof structure type generation by a plurality of fitting planes. And verifying the house structure type. According to the method, the airborne radar is adopted, the high-precision three-dimensional point cloud of the urban building roof patch can be rapidly obtained in a large area, the method has the advantages of being high in precision and high in penetrability, meanwhile, the building roof patch can be segmented and a roof plane equation can be fitted by adopting an improved algorithm, and the method is high in practicability. The method is a high-precision and high-efficiency data acquisition means for quickly acquiring the urban three-dimensional model structure type.

The invention discloses an urban scale-oriented multi-source heterogeneous data fusion carbon emission spatial distribution simulation method, and solves the problems of low spatial resolution and insufficient accuracy of urban carbon emission spatial distribution simulation estimation. Comprising the steps of defining an urban carbon emission accounting range; collecting multi-source heterogeneous data; calculating the consumption of main energy types of seven industry departments under the urban scale; correcting the energy consumption of seven industry departments of the city; matching the seven industry departments with corresponding urban building types, and establishing a corresponding relationship between the energy consumption of the urban industry departments and building function application types; calculating the carbon emission; and a refined energy consumption carbon emission space distribution model is constructed, and the carbon emission space is distributed to various single buildings. According to the method, multi-source data are comprehensively utilized, high-resolution and dynamic estimation of urban scale carbon emission spatial distribution is achieved, and the limitation that an existing method is coarse in spatial resolution and difficult to quantitatively analyze carbon emission of different energy consumption departments is overcome.

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 discloses an urban building group energy flexible management digital platform and method, and the method comprises the steps: managing multi-source data through a data layer, including building environment, energy consumption, user willingness and power grid signals; the front-end module constructs a city visual model (CVM) and a building agent component based on an unreal engine, and realizes visual display and interactive operation of the energy system; the CVM covers four layers, namely a basic environment layer, a building layer, a mobile resource layer and an energy infrastructure layer; the building agent component realizes fine management of energy production, consumption, energy storage and power grid supply; the rear-end module is responsible for data processing, logical operation and algorithm execution, real-time data storage, resource scheduling and decision planning; the business function module comprises resource real-time monitoring, demand response management and resource configuration and planning deduction, and meets the requirements of short-term management and long-term planning. According to the invention, multi-scale interaction and refined operation are realized, the management granularity and the visualization effect are improved, and powerful support is provided for flexible management and low-carbon transformation of urban building groups.

The invention relates to the technical field of building risk monitoring, in particular to an InSAR building scatterer positioning method, device and equipment, a storage medium and a computer program product. According to the method, urban point cloud data is segmented to obtain an urban building monomer instance point cloud set; based on the point cloud set, adopting a surface reconstruction algorithm to construct a building grid model; based on the grid model and the SAR satellite imaging parameters, adopting an SAR simulation technology to identify a radar illuminable vector frame of the building; converting the vector frame from a geographic coordinate system into an SAR image coordinate system, performing bilinear interpolation based on the geographic coordinates of the angular points of the vector frame, and determining the geographic coordinates of the scatterer in the vector frame; and if the scatterer is located in the overlapping region of the plurality of vector frames, performing overlay identification on the pixel points in the vector frames based on the geographic coordinates, determining the unique geographic coordinates, and determining the geographic positioning of the building scatterer in combination with the deformation information of the scatterer, thereby improving the positioning precision of the building scatterer.

The invention relates to the technical field of urban building management, and discloses an urban fifth facade roof space intelligent evaluation system and method. The system comprises a main control model, an image recognition agent, an index calculation agent and an Auto-Summary summary model. The image recognition agent realizes accurate semantic segmentation of roof elements through an image encoder, a prompt encoder and a mask decoder based on an SAM deep learning model; the index calculation agent adopts an RAG technology to dynamically retrieve an index library, and drives a space operation tool to calculate compliance indexes; the Auto-Summary summary model is fused with multi-modal data, and a visual report containing a thermodynamic diagram, a three-dimensional interaction model, rectification suggestions and roof layout suggestions is generated. According to the invention, a full-automatic roof is realized, the problems of low efficiency and large error of traditional manual evaluation are solved, and the intelligence and precision of urban fifth facade management are significantly improved.

The invention relates to the technical field of new energy, in particular to an optimization method based on urban building group solar radiation energy utilization, and provides an optimization method integrating multiple factors by adopting the method provided by the invention, so that the solar energy utilization potential can be evaluated more comprehensively, and the solar energy utilization efficiency can be improved. The method comprises the following steps: firstly, comprehensively obtaining preprocessed CIM, BIM, GIS and other urban three-dimensional environment model data, accurately calculating a shielding angle and a shielding area so as to optimize an arrangement scheme of a solar panel, secondly, introducing a dynamic correction mechanism, updating a solar radiation model in real time, and combining meteorological data (such as cloud amount and atmospheric transmissivity) and seasonal green plant coverage change so as to optimize the arrangement scheme of the solar panel; an energy utilization scheme is dynamically optimized, the prediction precision and the solar energy utilization effect are remarkably improved, solar panel layout suggestions are provided by constructing an optimization objective function and combining algorithm solution, a solar panel layout scheme avoiding the caustics effect is designed by analyzing the influence of different building shapes on a caustics surface, and potential risks are reduced.

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 discloses a cloud energy storage operation mechanism based on thermal inertia of an urban building air conditioning system, which relates to the field of energy storage and comprises the following steps: (1) establishing a fixed-frequency air conditioner virtual energy storage monomer model and an aggregation model based on the thermal inertia of the urban building air conditioning system; (2) designing a cloud energy storage operation mechanism taking air conditioner aggregation virtual energy storage as a main leasing resource, and establishing a cloud energy storage operator model, a cloud energy storage user model and a transaction pricing strategy; and (3) solving the cloud energy storage operation model by taking the minimum power consumption cost of the user as a target, and distributing and evaluating the social total welfare brought by the cloud energy storage mechanism. According to the energy storage operation mechanism provided by the invention, the electricity consumption cost of the subscribing user can be effectively reduced, and the early-stage investment and the later-stage operation and maintenance cost of an operator are reduced; and under the dual-carbon background, the strategy is also beneficial to the real-time consumption of new energy on the user side.

The invention discloses an urban population statistical data spatialization method and device based on deep learning, and belongs to the field of remote sensing science and technology. The method comprises the following steps: step 1, preprocessing acquired noctilucent data, and resampling global building area footprint data and urban building height data according to the resolution of the noctilucent data; step 2, obtaining a population distribution range based on multi-scale spatial constraint based on a data preprocessing result in the step 1; 3, taking the demographic statistical data as dependent variables, and taking the urban building height data and the noctilucent target area subjected to spatial constraint as independent variables to generate a spatialization fine training sample; and step 4, constructing a deep learning model, training the deep learning model by using the spatialized fine training sample, and performing population distribution mapping based on the trained model. According to the method, the resolution of population spatialization is improved, the calculation complexity is reduced, and the intelligence and precision of population spatialization are 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 a building mixed purpose classification method and system fused with multi-modal data, and belongs to the technical field of remote sensing image data processing, and the method comprises the steps: obtaining the multi-modal data of a research region, and carrying out the preprocessing of the multi-modal data; constructing a hybrid network model based on the convolutional neural network, wherein the hybrid network model comprises a parallel branch network, a buffer area module and a predictor which are connected in sequence; and inputting the preprocessed multi-modal data into the hybrid network model, extracting a multi-scale feature map of the multi-modal data through the parallel branch network, and extracting semantic information of a main environment feature region around a building in the multi-scale feature map through the buffer region module, and outputting the probability that the building belongs to each category through the predictor. According to the invention, the method achieves the recognition of the mixed purpose of the building based on the multi-modal data, can make up for the defects existing in the classification research of urban buildings only depending on single-modal data, and improves the recognition accuracy.

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.