935 results about "Geo spatial" patented technology

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention relates to the technical field of flood early warning, and discloses an urban flood disaster early warning method and system based on artificial intelligence, and the method comprises the steps: collecting five types of information, i.e., meteorological perception, hydrological monitoring, geographic space, urban operation and social perception in real time, and obtaining multi-source data with precise space-time coordinates; through preprocessing, gridding space-time alignment and key feature screening, rainfall accumulation and confluence evolution related features are extracted; constructing a physically constrained space-time fusion deep learning model, and outputting a future ponding depth prediction result in combination with a multi-head attention mechanism; environmental changes such as urban terrains and drainage facilities are adapted through incremental updating and transfer learning; and fusing the ponding depth, the influence range and the regional vulnerability characteristics to generate multi-level early warning, and synchronously outputting a spatial distribution map, a time evolution trend and affected object evaluation information. According to the invention, urban flood control and disaster reduction decision making and public accurate risk avoiding can be effectively supported.

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

The invention is applicable to the technical field of remote sensing, and provides a remote sensing multi-modal reasoning method based on a hybrid expert mechanism, which comprises the following steps of: constructing a hybrid expert enhanced geographic space perception visual language basic model; constructing a remote sensing data set for model training, which comprises two stages: in the stage I, forming a basic data set by aggregating existing remote sensing data sets across tasks, and in the stage II, improving the diversity of the data set by adopting a data enhancement technology; a double-stage training strategy is adopted, pre-training initialization and sparse architecture optimization are combined, and meanwhile, a total loss function containing autoregression loss and auxiliary loss is designed to train the model. According to the method, task requirements are adapted through a mixed expert mechanism, multi-modal features are fused, a sequence long-range dependency relationship is captured, performance is optimized, a diversified remote sensing data set is constructed, and a model is promoted to distinguish the global situation and details. According to the method, the performance of the remote sensing perception task is improved, and the powerful performance of the reasoning task is maintained.

The invention discloses a personnel trajectory low-delay accurate positioning method based on safety production monitoring, and relates to the field of industrial safety monitoring, and the method comprises the steps: obtaining signal data based on UWB anchor points, obtaining distance data through a laser range finder, and obtaining image data through a thermal imaging camera; establishing a three-dimensional geographic coordinate system based on the geographic space of the production workshop; extracting scene features of a production workshop, and constructing a virtual workshop; through timestamp alignment, consistent signal data, consistent distance data and consistent image data are obtained; calculating multi-dimensional positioning data through confidence evaluation; calculating scene feature weights; the multi-dimensional positioning data and the scene feature weight are imported into a virtual workshop, the virtual workshop performs multi-channel splicing on the multi-dimensional positioning data and the scene feature weight, and a positioning model is constructed; and obtaining positioning data. The method has the advantages that through multi-source heterogeneous data fusion and scene feature constraint, the positioning robustness in a complex industrial environment is improved.

The invention discloses a garden landscape design method based on digital twinning, and belongs to the technical field of landscape garden design. According to the method, a target site environment data set is collected, and a site thermodynamic distribution cloud picture is generated through a geographic space grid processing unit; extracting a site space pattern feature matrix on the basis; constructing a landscape effect distribution model, decomposing the feature matrix into topographic relief, seasonal phase color and illumination reflection parameters, driving the model to generate a three-dimensional scene rendering sequence, embedding the model into a three-dimensional digital twin scene of the garden site, and associating the parameters with physical attributes of the twin scene in real time; dynamic optimization of the landscape scheme is executed, an element replacement instruction is generated, and the spatial topological relation network is updated; and outputting a site design scheme map, adjusting facility layout coordinates, and generating a final scheme data packet in combination with the path connectivity, so that the precision and adaptability of garden landscape design can be improved.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention relates to the technical field of data processing, and discloses a data processing method and application based on multi-source data fusion. The method comprises the following steps: acquiring multi-source time sequence data (including sensor, geographic space, user behavior data and the like) and space vector base map data of a target area; de-noising and normalizing the multi-source time series data, extracting an associated feature map through a heterogeneous model, generating a regional data object set based on base map adaptive segmentation, and extracting multi-dimensional features; optimizing a data object set through spatial topology verification, feature matching screening and weighted fusion; and generating statistics, space-time coupling analysis and a dynamic prediction result by utilizing optimized data, or generating a visual map supporting interaction by mapping a base map. The method improves the multi-source data fusion precision and spatial analysis capability, is suitable for multi-field cross-modal decision support, and can be used for scenes of smart cities, environment monitoring and the like.

The invention belongs to the technical field of photovoltaic power prediction, and particularly relates to a distributed photovoltaic power prediction method, which comprises the following steps of: acquiring multi-source information of a plurality of photovoltaic power stations; constructing a gridding geographic space model based on the collected geographic position information; mapping the geographic position information of each photovoltaic power station to a corresponding space grid unit; for each space grid unit, fusing multi-source information of all photovoltaic stations in the grid, and constructing grid-level multi-source features including meteorological features, power features and geographic position features; and the grid-level multi-source features are inputted into a space-time Transform model which is already trained, and the space-time Transform model outputs a photovoltaic power prediction value. According to the method, the photovoltaic power prediction value of the target at the future moment is obtained through joint modeling of the time sequence dependence and the spatial relationship, so that the prediction precision and stability of the future power change trend of the distributed photovoltaic power station are improved.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

The invention discloses a natural language question and answer method, system and device, a medium and a product, and relates to the technical field of semantic recognition. The method comprises the steps that a user question is recognized based on a named entity recognition model, and a key entity is determined; the key entity comprises a place name, a distance and an orientation in the question; performing text classification on the user question based on a user intention recognition model, and determining the intention of the user; determining a structured query statement based on a preset language model according to the key entity and the intention; obtaining multi-dimensional geographic space data in the Internet map; converting the multi-dimensional geographic space data into a triple, and constructing a structured knowledge graph; and according to the structured query statement and the structured knowledge graph, determining an answer to the user question. According to the method, the intention of the question of the user is accurately and intelligently understood, and the accurate answer is returned.

Building on U.S. Provisional Patent Application No. 63 / 693,803, paragraphs for 2D-to-3D conversion and [0023]-[0024] for secure content verification, the Matrix 3D AI Polygon Mesh Hash Key System revolutionizes transforming 2D content into secure, interactive 3D AR / VR assets. Integrating AI and LiDAR, it enables real-time 3D mesh generation with precise geospatial anchoring. Users can reshape 3D content instantly via natural language commands, enhancing interactivity. Quantum-resistant encryption, using AES-256 and CRYSTALS-Kyber, ensures robust asset security. The system excels in gaming, surveillance, content verification, military operations, space exploration, deepfake detection, and pharmaceutical research, offering unmatched precision and scalability. Its multi-stage rendering pipeline, powered by distributed AI-driven bots, supports efficient real-time 3D mesh generation, achieving 95% accuracy and 1 cm resolution. This AR / VR technology advancement transforms industries with scalable, secure solutions for interactive 3D content creation and management, setting a new standard for precision and versatility.

The invention discloses a methane point source emission remote sensing recognition and plume extraction method and a methane point source emission remote sensing recognition and plume extraction system, belongs to the technical field of methane point source recognition, and solves the problems of incomplete methane plume extraction and lack of morphological characteristics of an extraction result in a traditional signal filtering method by introducing threshold segmentation, spatial continuity and geographic space constraints. And the method has good applicability to the multi-point dissipation condition in the after-mine activity process of a coal mine area.

The invention discloses a karst development area geological disaster identification method and system based on artificial intelligence, relates to the field of geological disaster early warning and risk assessment, and solves the problems that it is difficult to integrate geology and related data, it is difficult to construct a model to predict disaster categories based on historical disaster cases and real-time monitoring data, and the risk assessment efficiency is improved. The technical problem of lack of association of a prediction result with a geographic space to generate a risk distribution map and dynamically update a high-risk area is solved; comprising the following steps: introducing multi-source heterogeneous data, and combining historical geological disaster cases; preprocessing the data; extracting underground water level change rate, soil humidity and vibration frequency characteristics; combining a plurality of features into a feature matrix, and projecting the feature matrix to a principal component space; after training the model by using a random forest algorithm, optimizing the constructed model by combining cross validation with a Bayesian algorithm; the model prediction result is converted into a risk level value through a mapping rule; and generating a risk level distribution map based on the GIS data.

The invention relates to the technical field of environmental shadow analysis, and discloses an environmental shadow analysis method for photovoltaic power station design, which comprises the following steps: collecting and preprocessing geographic space data of a photovoltaic power station; calculating a solar altitude angle and an azimuth angle based on the pre-processed parameters, and generating a three-dimensional solar direction vector; analyzing the geometric surface of the three-dimensional obstacle model, screening a visible surface patch according to a sun direction vector, and sampling effective projection points; dynamically updating the sun direction vector in a specified time period, and calculating shadow coordinates of projection points on the ground or an inclined plane in each time period; and generating shadow contours of all time periods and combining the shadow contours into a complete influence area. According to the invention, through the dynamic sun trajectory modeling and complex obstacle surface analysis technology and in combination with the multi-modal projection correction and Boolean operation merging strategy, high-precision analysis of shadow spatial-temporal distribution in a complex environment, multi-installation scene self-adaption and dynamic shielding influence quantitative evaluation are realized.

The present disclosure relates a technique for determining gas emission parameters over a geospatial area. The system includes a non-transitory computer-readable medium for storing the spectral signals obtained from overhead sensors and one or more trained deep-learning classification models. The system further includes one or more processors configured to determine one or more gas emission parameters over the geospatial area based on the one or more spectral signals using the one or more trained deep-learning classification models. Each of the one or more trained deep-learning classification models is generated by generating training data based on training samples representative of spectral signals from the one or more geospatial areas at two or more different time-periods, forming a set of training data batches, and training a deep-learning classification model based on the set of training data batches by applying an iterative optimization procedure to adjust hyperparameters of the deep learning classification model.

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 spatial data analysis, and particularly provides a commercial vitality space-time prediction and dynamic evaluation method based on multi-modal feature fusion, which comprises the following steps: collecting geographic space, text semantics, real-time behavior and economic attribute multi-modal heterogeneous data, and cleaning and desensitizing to obtain a structured data set; features are extracted from the single-modal data, and commercial vitality core feature vectors are obtained through cross-modal attention fusion in combination with an emotion-vitality association model; inputting the result into a space-time sequence prediction model, and outputting a commercial activity prediction result in a future preset time period; combining real-time updated data to construct a quantization-quality two-dimensional evaluation system, and performing real-time calculation by using a stream processing framework to obtain a dynamic evaluation result; and displaying and outputting a structured evaluation report through a visual panel. According to the method, timely and reliable decision support is provided for scenes such as business district operation optimization, business investment decision and regional economy monitoring, and the precision and real-time requirements of business vitality analysis are comprehensively met.

The invention relates to the technical field of environmental assessment, and provides a plateau region soil erosion attribution method, system, medium and equipment, and the method comprises the steps: calculating the soil erosion rate of each calculation unit in a research region through a soil erosion model based on multi-source geographic space data; the soil erosion rate is a product of a rainfall erosivity factor, a soil erodibility factor, a slope length gradient factor, a vegetation coverage management factor and a water and soil conservation measure factor; a rainfall erosivity factor, a soil erodibility factor, a slope length and gradient factor, a vegetation coverage management factor and / or a water and soil conservation measure factor are used as key driving factors, and the control degree of the key driving factors on the soil erosion rate change is calculated by adopting a logarithmic mean Dewei index method; wherein the control degree sets a weighting coefficient for each calculation unit in the calculation process, and the weighting coefficient is constructed based on the difference between each calculation unit and the preset reference region. And the independent contribution of each factor to the erosion change can be accurately separated.

The invention relates to the technical field of meteorological data processing, in particular to a training data acquisition method and system for a power transmission line fault prediction model. The method comprises the following steps: constructing a multi-source heterogeneous original data pool containing severe convection meteorological data, power transmission line operation and fault data and geographic space data; performing time-space reference unified processing and grid division on the data; mapping the line paragraph to a grid unit, identifying a severe convection event and dividing an influence area through a density clustering algorithm based on a multi-parameter collaborative threshold value, calculating a grid exposure degree, and combining a strength grade weight and a terrain correction weight to calculate a space correlation strength; setting a differentiation time association window to calculate a time factor according to the severe convection dominant type, calculating association credibility based on the space association strength and the time factor, and screening high-credibility association results; and performing attribution labeling on the fault information to obtain a training data set. The problem of low training data quality is solved.

The invention provides a power grid mountain fire prediction method based on causal driving and a space-time diagram convolutional network, and belongs to the technical field of mountain fire prediction. A dynamic feature encoder and a static feature encoder are designed to extract high-dimensional spatial-temporal features of meteorological time sequence information, geographic space environment and power transmission line distribution multi-source heterogeneous data, a causal discovery algorithm is adopted to construct a dynamically evolved causal graph topology, a real causal driven relationship between variables is identified, a causal intensity matrix is decoupled into positive and negative adjacent matrixes, and the dynamic evolved multi-source heterogeneous data is obtained. And designing a causal constrained graph convolution module to aggregate and propagate high-order information, and finally realizing accurate prediction of the power grid forest fire. According to the method, the PCMCI causal discovery algorithm is introduced, so that the real causal driven relationship among multivariate time sequence factors is effectively identified; a causal GCN layer in the CSTGCN model extracts spatial dependence features by using an adjacent matrix constrained by a causal structure, the CSTGCN model embeds causal structure information into a spatio-temporal feature learning framework, and higher prediction precision and generalization performance are achieved.

The invention discloses an intelligent alarm positioning method and system based on an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle monitoring and geographic space information processing, and the method comprises the steps: obtaining a video stream in real time based on the unmanned aerial vehicle, and recognizing a risk point location in a video image; a three-dimensional space positioning model is constructed based on telemetry data and lens angle parameters acquired by the unmanned aerial vehicle in real time. And performing three-dimensional coordinate dynamic solution on the risk point location based on a three-dimensional space positioning model to obtain a world coordinate of the risk point location. And determining and outputting an alarm position of the risk point location based on the world coordinates. An automatic detection and artificial interaction dual-channel mechanism is adopted, an alarm target is accurately identified and positioned in an unmanned aerial vehicle real-time video, a corresponding relation between video pixels and geographic coordinates is established through real-time registration of an unmanned aerial vehicle image and the digital earth, a space coordinate conversion error caused by view angle difference is corrected by using a depth map, and an alarm target is accurately identified and positioned. Accurate conversion from video pixel coordinates to world coordinates is realized, and high-precision positioning support is provided for remote monitoring of the unmanned aerial vehicle.

The invention discloses a complex system data visualization modeling method based on GIS and topological graph fusion, and relates to the technical field of data visualization modeling, and the method comprises data fusion modeling, space-time anomaly reasoning, state propagation modeling and multi-dimensional visualization enhancement. The method comprises the following steps: performing unified access, cleaning and standardization processing on operation data, topological relation data, geographic space data, function configuration data and environment data of a complex system, and constructing a unified data model fusing geographic attributes and topological attributes; realizing abnormal root cause reasoning and spatial constraint propagation modeling of a system state by using a convolutional time sequence modeling method combined with a graph neural network; a dynamic evolution rule of a node state is described through a time gating mechanism, and interactive display is realized; according to the method, global visualization of the operation state of a complex system, dynamic backtracking of an abnormal propagation path and hierarchical analysis of key nodes can be realized, and the intelligent monitoring, situation awareness and decision support capabilities of the system are improved.

The invention belongs to the technical field of geospatial information systems, and particularly relates to a component-level catenary three-dimensional model lightweight three-dimensional tile conversion method, which comprises the following steps: S1, forming a component-level fine model data set with semantic annotations; s2, constructing a detail level division rule; s3, reconstructing a lightweight linear component model; s4, constructing a comprehensive evaluation function Q, deleting the body-shaped components of which the Q values are smaller than a preset threshold value, and retaining the body-shaped components of which the Q values are greater than or equal to the preset threshold value; s5, obtaining optimized body-shaped component model data; and S6, according to the detail level division rule in the S2, assembling and recombining the lightweight linear component model in the S3 and the optimized body-shaped component model data in the S5, generating a hierarchical component structure, converting each component into a three-dimensional instance model, and outputting a three-dimensional tile data structure. And efficient organization and visualization of the contact network BIM model in the GIS environment are realized.

The invention relates to the field of smart city management, and discloses a multi-source data fusion and treatment method for a CIM platform, and the method comprises the steps: sorting the current situation of city spatial data, and carrying out the unified access and classification of the data; carrying out unified conversion on the data, and carrying out merging and layer sequence arrangement; carrying out unified space coding on the geographic space object, and constructing a coding rule; the granularity is divided into construction granularity and expression granularity, data are fused through spatial association, and unified expression of multi-scale data is achieved; extracting spatial semantic information, establishing a mapping relation between a unified address and a CIM model, and realizing semantic expression of a place name and the address; constructing a pyramid model, and realizing fusion of the building information model and the CIM; and performing graph falling processing on the government affair data to form a thematic data base plate associated with the CIM model. According to the method, efficient access, unified processing and deep fusion of the multi-source data are realized, and the integration and availability of the data in the CIM platform are improved.

The invention relates to the technical field of machine learning, and particularly discloses a spatial local XGBoost machine learning model based on multi-scale geographic weighting, and the model comprises a feature decoupling module which is used for constructing a two-channel input structure of geographic features and non-geographic features, and achieves the feature decoupling and fusion through a multi-scale spatial weight matrix; the bandwidth allocation module dynamically determines the bandwidth scale in the XGBoost tree splitting process, and establishes a dynamic weight bound by tree levels; the constraint gain module generates split points with scale marks; a contribution decoupling module extracts split features and associates geographic and non-geographic features to realize salinization prediction and contribution index extraction; and the verification optimization module optimizes model parameters through a multi-scale thermodynamic diagram and spatial autocorrelation analysis. The model can capture geospatial effects of different scales, improves the spatial local prediction precision, and is applied to soil salinization analysis scenes with spatial heterogeneity.

The invention discloses a business district drawing method and device based on geographic space aggregation, and the method comprises the steps: carrying out the spatial density clustering of all network points in a to-be-drawn space, and obtaining a plurality of network point clusters; correspondingly generating a plurality of convex hulls based on the plurality of dot clusters; and performing boundary optimization on the convex hull according to the collected river network data, and determining the optimized convex hull as the business circle in the space to be drawn. According to the method, the problems of fuzzy boundary, data lag, poor strategy adaptability and the like in the traditional technology can be effectively solved, accurate definition of the contour of the business district and standardized output of grade division are realized, and then the efficiency of drawing the business district is improved.

The invention discloses a privacy protection track generation method based on track feature distribution modeling. A credible track is generated by extracting similarity of track positions in semantics, geography and directions. The method comprises the following steps: firstly, collecting original trajectory data, preprocessing, and extracting trajectory data in a specified longitude and latitude range as an original trajectory data set; then, geospatial discretization processing is carried out on the original trajectory data in a gridding mode, and the original trajectory is converted into a grid trajectory; and then, extracting features of the grid trajectory, constructing a global movement model, generating a synthesized grid trajectory based on the features, and converting the synthesized grid trajectory into a position trajectory to obtain a final synthesized trajectory. And finally, the final synthetic trajectory is used for replacing the original trajectory for publishing, so that privacy protection of the original trajectory is realized.

The invention discloses a BIM / CIM-based engineering design whole-process auxiliary decision-making method, relates to the technical field of engineering design, and discloses a BIM / CIM-based engineering design whole-process auxiliary decision-making method, which comprises the following steps: responding to a project red line range, obtaining geographic space data, and carrying out preprocessing, model reconstruction and lightweight processing on the geographic space data to obtain a project red line range; generating a macroscopic current situation scene; obtaining at least two candidate schemes, and performing spatial registration and fusion on the BIM models corresponding to the candidate schemes and the macroscopic current situation scene to generate a corresponding fusion decision scene; based on the fusion decision-making scene, carrying out collaborative decision-making on the candidate schemes, and determining a bid-winning scheme; and packaging the BIM model corresponding to the bid winning scheme and associated data, and transmitting the packaged BIM model and associated data to a downstream construction management system through an interface. The scheme collaborative decision-making efficiency is effectively improved, the information fault problem in the design and construction stages is solved, and the whole-process decision-making closed loop of the engineering project is achieved.

The invention discloses an unmanned aerial vehicle dynamic projection map construction method and system based on spatio-temporal data fusion, and belongs to the technical field of unmanned aerial vehicle video monitoring and geographic scene fusion, and the method comprises the steps: obtaining multi-modal flight data in real time based on an unmanned aerial vehicle, and the multi-modal flight data comprise video image data and spatio-temporal reference data; and performing data preprocessing on the multi-modal flight data to obtain route data. And constructing an unmanned aerial vehicle dynamic projection model, and performing space-time synchronous projection rendering on the route data to obtain an unmanned aerial vehicle dynamic projection map. And constructing a dynamic calibration model, and correcting the dynamic projection map of the unmanned aerial vehicle. According to the method, an integrated dynamic geographic information sensing system is constructed through deep cooperation of a spatio-temporal data fusion framework and an intelligent calculation engine, accurate mapping of the geographic space driven by spatio-temporal reference fusion is realized, millimeter-level space registration capability is constructed through multi-modal data intelligent solution and terrain adaptive rendering, and the real-time dynamic geographic information sensing system is constructed. And a technical breakthrough is formed in the dimensions of accurate positioning, data fusion, real-time response and the like.