66 results about "Geographic space" patented technology

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The invention relates to the technical field of urban geography, in particular to a crowd movement flow-based multi-scale community structure importance identification method, which comprises the following steps of: constructing a crowd movement space-time network according to a preset geographic space unit and a time interval on the basis of crowd movement starting place and destination data; identifying a dynamic community structure based on a crowd mobile space-time network; performing multi-scale community structure object role and importance identification; and performing pattern classification by using the time sequence importance of the multi-scale community structure object. According to the method, the problems of inaccurate dynamic community structure recognition and lack of multi-scale object contribution decoupling mechanisms are solved.

The invention provides a beach big data geographic space intelligent application service method based on a large model Agent, and relates to the technical field of geographic space data artificial intelligence processing and analysis. According to the method, a distributed platform comprising a parallel computing cluster, a spatial database and a large language model is constructed, and standardized storage and metadata management of multi-source remote sensing, vector and attribute data are supported. The system generates a spatio-temporal query instruction through natural language analysis, completes cross-source matching and spatial calculation, outputs an analysis result in a multi-modal form such as a map, a chart and a text, and realizes intelligent and interactive service of geographic data processing.

The invention provides a trade whole industry chain data intelligent matching and resource docking method, and relates to the technical field of international trade data analysis and geographic information systems.The method comprises the steps that customs declaration data, logistics tracking data, enterprise supply chain data and market public opinion data are collected in real time through a standardized interface; constructing a multi-source heterogeneous data fusion system of block chain evidence storage; dynamically modeling a whole industry chain relation network based on the knowledge graph, and quantifying the supply chain dependency, the trade flow direction and the policy influence weight; calculating an industrial chain correlation index ICRI and a resource matching priority coefficient RMPC, optimizing a logistics path in combination with a geographic space analysis algorithm, and avoiding a policy restriction area in real time; and rendering a matching result through a three-dimensional visualization platform, and iteratively updating model parameters based on user interaction feedback. According to the method, the limitation of a traditional data island is broken through, dynamic quantitative analysis and accurate resource matching are realized, and the trade data integration efficiency is remarkably improved through intelligent path planning and interactive decision support.

The invention discloses a GIS resource management method, system and device and a medium, and belongs to the technical field of resource management, and the method comprises the steps: obtaining a city planning map, constructing a city three-dimensional model, and collecting multi-source city feature information; constructing a multi-source data fusion matrix of a GIS graph and generating three layers of attribute tags; a spatial gravity model is constructed, and reachability analysis is carried out on urban resources; constructing a dynamic prediction model, and triggering a model iteration mechanism; and generating a resource health degree index based on a model iteration mechanism result, and triggering a resource allocation control signal. The method has the beneficial effects that through multi-source data fusion matrix construction, precise space-time association of geographic space, Internet of Things time sequence and visual feature data is realized, and the integrity and accuracy of data integration are improved; quantitative evaluation and prospective pre-judgment of resource supply and demand are realized based on reachability analysis of a space gravity model and a dynamic prediction model of a space-time diagram convolutional network.

The invention discloses a slope monitoring data calibration and three-dimensional point cloud matching method, belongs to the field of monitoring data calibration, and aims to improve orientation errors caused by the fact that global satellite positioning and navigation system measurement endpoint errors and equipment length problems are not fully considered in existing slope radar measurement. The method solves the problem of position deviation of a monitoring result caused by the fact that the position of a deformation area is monitored, breaks through the limitation of insufficient precision when a traditional radar image is converted into geographic space coordinates, improves the accuracy and reliability of data, remarkably improves the position precision of slope radar monitoring data, and enables the position monitoring of the deformation area to be more accurate. The high-precision requirement of slope stability monitoring is met, reliable data support is provided for fine monitoring of slope stability, potential safety hazards can be found in time, and corresponding measures can be taken.

The invention discloses a big language model dialogue recommendation method based on multi-modal geographic information fusion and context modulation, which comprises the following steps of: firstly, coding and fusing a text address, geographic coordinates and numerical attributes to obtain a geographic context matrix; then intention modeling is carried out to generate user intention representation, then context feature modulation is utilized, dynamic modulation is carried out on the user intention representation by utilizing an asymmetric fusion mechanism, a context guiding signal containing geographical constraints is generated, and finally, the context guiding signal is spliced before the user intention representation. The big language model input to the parameter freezing obtains user preference vectors and calculates recommendation scores with the candidate positions, the candidate positions are sorted according to the recommendation scores, and the candidate positions with high scores are recommended to the user. According to the method, on the premise that internal parameters of the large language model are not changed, the geospatial reasoning ability is effectively given to the large language model, and the accuracy and interpretability of dialogue recommendation are remarkably improved.

The invention relates to an ecological system health space-time driven attribution analysis method based on IGTWR-XGBoost, and the method comprises the steps: obtaining an ecological system health index of a target region, calculating the ecological system health index of the target region according to the ecological system health index, and collecting a multi-source driving factor; constructing an interactive geographic space-time weighted regression model according to the multi-source driving factor, the ecological system health index and a geographic-time weighted regression model; acquiring a multi-dimensional local regression coefficient matrix according to the interactive geographic space-time weighted regression model; correcting a multi-source driving factor according to the multi-dimensional local regression coefficient matrix to form a space-time enhanced feature matrix; and inputting the space-time enhanced feature matrix into an XGBoost model, and obtaining a prediction result of the ecological system health index and global and local contribution values of each driving factor. According to the invention, the accuracy and reliability of ecosystem health driving mechanism analysis can be significantly improved.

The invention relates to a GIS technology-based geographic information acquisition method, device and equipment and a medium, and the method comprises the steps: obtaining a satellite image, an unmanned aerial vehicle LiDAR point cloud and ground sensor data through multi-source geographic data acquisition, and generating a multi-source geographic data set and an environment parameter matrix; performing dynamic credibility evaluation on the environment parameter matrix to generate a credibility vector; carrying out self-adaptive weight distribution on the credibility vector in combination with an inherent error coefficient of the equipment to generate a dynamic weight vector; performing space-time fusion on the multi-source geographic data set and the dynamic weight vector, and generating a fused geographic information matrix through coordinate registration and weighted fusion; and finally, carrying out closed-loop distortion calibration based on the verification reference point, correcting a system error by using a credibility vector, and generating an optimized geographic information database. According to the method, measurement deviation caused by environmental factors can be effectively overcome, intelligent fusion and quality control of multi-source heterogeneous geographic data are achieved, and the application requirement of high-precision geographic space analysis is met.

The invention, which relates to the technical field of digital interaction, discloses a block-chain-based scenic spot digital creative and creative interaction method comprising the following steps: obtaining tourist block chain identity information and initializing a creative and creative interaction context; carrying out multi-point geographic space binding according to the interaction context to obtain a content triggering set; performing identity binding mapping according to the content triggering set to obtain a to-be-casted collection set; on-chain registration is carried out according to the to-be-cast collection set, and a right-confirmed collection set is obtained; performing multi-dimensional grouping and layering according to the right-confirmed collection set to obtain an associated collection relation graph; and carrying out interactive on-chain combination according to the associated collection relationship graph to obtain the composite cultural assets. According to the method, the distribution accuracy is improved, the exclusiveness and emotion recognition of the generated digital cultural and creative collections are increased, the irreplaceability and identity exclusiveness of the collections are improved, map nesting and layering of multi-dimensional grouping are realized, the structural hierarchy is increased, and the individuation degree of cultural assets of different tourists is improved.

The invention discloses a dynamic shelter restoration method and system based on continuous streetscape panoramic images, and the method comprises the steps: firstly obtaining a to-be-restored target panoramic image A and a to-be-restored reference panoramic image B, and generating an original pixel-level shelter mask; secondly, extracting matching points among the panoramic images, realizing cross-view geometric alignment of the images, and obtaining a target perspective view C and a reference perspective view D through perspective re-projection; and then inputting the target perspective view C and the reference perspective view D into a three-dimensional reconstruction framework, and performing three-dimensional point cloud reimaging under the camera pose of the target perspective view C by using a depth inspection mechanism. And finally, carrying out image restoration on a three-dimensional point cloud re-imaging result, restoring to a panoramic coordinate system, splicing with an original panoramic image, and outputting a shielding-free panoramic image. According to the method, it is ensured that the repairing result conforms to the authenticity and geometric consistency of the geographic space, and the problems of overlapping conflicts and visual tearing during multi-view projection fusion are effectively solved.

The invention belongs to the technical field of geographic information science space optimization, and particularly discloses a low-altitude vertical take-off and landing field site selection method fusing mobile phone signaling big data dynamic maximum coverage, which comprises the steps of fusing multi-source geographic space big data, and constructing an urban low-altitude vertical take-off and landing field candidate position set; defining a multi-time-sequence take-off and landing field maximum coverage site selection model, and determining an optimization objective function, constraint conditions and a decision-making method; a genetic algorithm is used as a basic algorithm model, and three limiting conditions of a multi-time-sequence influence factor, total quantity constraint and integer programming are added to form a multi-time-sequence integer constraint spatial evolution algorithm; based on MP-ICSEA, an MP-MCLP problem is solved, an optimal solution of service facility configuration is obtained, the effectiveness of vertical take-off and landing field site selection and vertical take-off and landing aircraft redeployment along with the change of the flow of people is verified, and therefore the reasonability of vertical take-off and landing field site selection and the efficiency of vertical take-off and landing aircraft redeployment according to the time sequence are greatly improved.

The invention discloses a method for intelligently evaluating the susceptibility of a landslide disaster in combination with deep learning, and relates to the technical field of geological disaster evaluation. According to the method, data processing is more accurate, standardized processing of multi-source geographic space data is achieved through the GIS technology, coordinate deviation, resolution difference and magnitude influence are eliminated, a high-quality and high-consistency data basis is provided for model training, and interference of data noise on an evaluation result is effectively reduced; model fusion is efficient, the advantages of RF and KNN basic models (the RF is high in anti-overfitting capacity, and the KNN is sensitive to local features) are fused, the prediction result serves as the input feature of the deep learning model, cooperation of machine learning and deep learning is achieved, the utilization efficiency of the model on the landslide influence factors is remarkably improved, and the method is suitable for the landslide prediction of the landslide. The evaluation precision is improved by 15%-20% compared with that of a single model (AUC value verification shows that the AUC value of the model is larger than 0.85).

The invention discloses a geographic space analysis task-oriented GIS tool intelligent identification method and system, and belongs to the field of geographic information. The method comprises the following steps: firstly, acquiring a GIS tool, GIS tool set information and a GIS task case, and constructing a triple sample based on the GIS tool set information and the GIS task case; designing a task alignment and hierarchical perception loss function aiming at tool characteristics for fine tuning of the vector model to obtain a GIS tool vector model; after a tool vector library, a tool set vector library and a task case vector library are constructed, an RAG workflow is designed so as to realize GIS tool intelligent identification oriented to a geographic space analysis task. According to the method, the intelligent recognition accuracy of the GIS tool facing the geographic space analysis task is improved, and the method has the capability of intelligently recognizing a large number of tools, so that various GIS tasks can be processed, and meanwhile, the consumption of computing resources and large model Token in the implementation process and the application stage of the method is reduced.

The application provides an environmental pollution risk assessment method, system, computing device and storage medium. The method comprises the following steps: obtaining geographical space data and environmental pollution data of a target area; determining a first pollution risk distribution map and a pollutant diffusion path by using a target pollutant diffusion model according to the environmental pollution data; generating a second pollution risk distribution map by using a pre-trained graph convolutional neural network in combination with the geographical space data and the pollutant diffusion path; and performing fusion analysis according to the first pollution risk distribution map and the second pollution risk distribution map to obtain an environmental pollution risk assessment result of the target area. The technical solution provided by the application improves the accuracy of the environmental pollution risk assessment result.

The invention is suitable for the technical field of geographic element data analysis, and provides a geographic spatiotemporal weighted vector autoregression-based multi-element collaborative change modeling method, which comprises the following steps of: constructing a GTWR-VAR model, and combining a multivariable feedback mechanism of vector autoregression with locality of geographic spatiotemporal weighted regression to obtain a GTWR-VAR model; the method comprises the following steps of: quantifying spatial-temporal heterogeneity of mutual influence among geographic elements, introducing a Bayesian optimization mechanism, adaptively determining an optimal lag order, a spatial bandwidth and a time bandwidth by taking verification set mean square error minimization as a target, and finally adopting Monte Carlo block permutation test to determine the optimal lag order, the spatial bandwidth and the time bandwidth on the premise of retaining inherent time autocorrelation of data. And evaluating and eliminating non-significant items in the local coefficient to ensure the statistical effectiveness of the result. According to the method, the continuous change rule of the influence mechanism between the geographic elements in time and space is disclosed, and a more accurate and reliable basis is provided for regional response research under fine management, prediction and climate change.

The invention discloses a local resident doctor-seeing accessibility calculation method considering doctor-seeing mobility. And determining that the geographic space elements for calculating the reachability of the medical resources are three-level hospital positions, community positions and a travel mode for connecting the two positions. The number of medical staff of the third-level hospitals is obtained, and the cross-city doctor seeing population proportion of the third-level hospitals is obtained; obtaining the number of resident population of each community; five common travel modes including public transportation, private cars, taxis, bicycles and walking are mixed into a multi-mode transportation mode. Determining the number of medical staff of actual service local residents according to the cross-city doctor seeing population proportion of each third-level hospital; and according to the population sharing rates of the five trip modes under different distances, determining the number of trips of each community under different trip modes. And calculating and considering the medical-seeing mobility and the medical-seeing accessibility of the multi-mode traffic mode by using the corrected third-level hospital supply capability, the number of people who travel in different travel modes in the community and different distance attenuation functions of the travel modes. The method can scientifically and accurately determine the reachability of the local residents to obtain the medical resources, and provides a basis for the distribution and adjustment of the medical resources in the later period.

The invention relates to the technical field of geographic space information, and discloses a rural cadastre dynamic updating and property management method and system based on remote sensing and a GIS (Geographic Information System). A multi-source data acquisition module, a data arrangement module, a dynamic updating module, a property management module, a data analysis module, a three-dimensional visualization module, a quality inspection scale module, a public participation module, a dispute mediation module, a resource sharing module and an operation and maintenance security module are deployed, and initialization configuration and linkage testing of each module are completed. The multi-source data acquisition module is used for integrating multi-source data, the dynamic updating module is used for detecting land type changes through a CNN model, the data analysis module is used for quantifying conflict risks and cultivated land / development trends, and all the modules cooperatively support application, so that the system has the advantages of real-time monitoring, intelligent early warning, accurate metering and dynamic optimization. The problems that in traditional rural cadastral management, land utilization change perception is lagged, ownership disputes are passively handled, cultivated land protection quantification is absent, and development intensity regulation and control are not accurate are solved.

The application provides a target positioning method based on panoramic segmentation, and belongs to the field of target positioning. Firstly, an image acquisition sensor is used to acquire a real scene image of a target area, and an initial pose of the sensor is acquired; a rendering image is generated according to the initial pose of the sensor and a three-dimensional geographical space model which has been established; then, panoramic segmentation is performed on the real scene image and the rendering image, corner point detection and matching are performed on the segmentation results, and matched point pairs of the two images are obtained; the real pose of the sensor is calculated by using the matched point pairs and the initial pose of the sensor; finally, the real pose of the sensor is used to realize target positioning in a real scene. Compared with the matching method in the prior art which uses a deep learning method, the application does not need a large number of samples, uses a corner point detection method to determine the matched point pairs of the two images, improves the work efficiency, improves the matching precision between the heterogeneous images, and uses the real pose of the sensor to realize target positioning, thereby improving the target positioning precision.

The invention discloses a karst surface collapse identification method and system based on GIS and machine learning, and relates to the technical field of geological disaster monitoring. The method comprises the specific steps that a karst surface collapse geographic space database is constructed; collapse point locations and non-collapse point locations are selected from the karst surface collapse geographic space database to form an initial sample data set, n influence factors are screened, and a sample set is generated through a geographic detector; constructing a karst surface collapse susceptibility evaluation model based on a random forest algorithm through the initial sample data set and the sample set; dividing the karst surface collapse susceptible place into a plurality of grid units, and predicting the collapse probability of each grid by using the karst surface collapse susceptibility evaluation model; and importing the probability value of collapse prediction into a GIS platform, and assigning values to grid points according to spatial positions to obtain a karst surface collapse susceptibility index layer. According to the invention, through coupling of geographical detection and machine learning, efficient prediction of karst surface collapse is realized.

The invention relates to the technical field of cultural relic protection and public safety, in particular to a cultural relic crime risk early warning method and system fusing space measurement and fuzzy QCA. Cultural relic crime cases, geographic space and social economic data are collected and subjected to standardized preprocessing and double-channel parallel analysis; the quantitative channel analyzes a crime space overflow effect by using a space Durbin model, and generates a hot spot region and an overflow index; a fuzzy QCA model is constructed by a qualitative channel, a high-risk condition combination is identified, a comprehensive risk assessment result is generated by assessing the credibility and situation adaptability of two channel results, a fusion weight is dynamically calculated, a risk level boundary is dynamically determined according to the risk assessment result and an intelligent interval identification algorithm, and optimization and adjustment are performed in combination with a historical early warning effect. According to the method, accurate historical relic crime risk early warning levels and early warning suggestions are output, advantages of space measurement and fuzzy QCA complement each other, the reliability of an early warning result is improved, and powerful support is provided for historical relic protection and public safety.

The embodiment of the invention provides a satellite false fire point data elimination method, device and equipment and a storage medium, and the method comprises the steps: collecting a satellite fire point product and auxiliary geographic space data, determining a fire point label map, obtaining original wave band data, determining an original observation time sequence tensor according to the original wave band data, and obtaining an observation time sequence tensor according to the original observation time sequence tensor; building a deep learning model based on the LSTM network and the supplementary structure, dividing the fire point label map and the original observation time sequence tensor into a training set and a test set by using a preset date strategy, training and testing the deep learning model to obtain a prediction model, and inputting spatio-temporal information of each target fire point in the target satellite fire point product into the prediction model to obtain a prediction result. According to the method, the category of the target fire point is determined, and the data corresponding to the target fire point of which the category is the preset false fire point category is eliminated, so that accurate identification and automatic elimination of the false fire point caused by the non-combustion heat source are realized, and the false alarm rate of the satellite fire point product is remarkably reduced.

The invention relates to the technical field of remote sensing vision, and particularly discloses a remote sensing multi-modal reasoning method and system based on a geospatial thinking chain, and the method comprises the steps: constructing a geospatial thinking chain data set which comprises structured reasoning data organized according to a planning-positioning-synthesizing three-section cognitive architecture, the association module is used for establishing verifiable association between visual evidence in a remote sensing image and a text conclusion; constructing a remote sensing visual language basic model, wherein the model comprises a visual encoder, a language decoder and a visual projection layer connecting the visual encoder and the language decoder; according to the method, remote sensing analysis is modeled into a verifiable multi-step reasoning process, so that the model provides a verifiable analysis track while outputting a final answer, the problem that output cannot be verified due to traditional end-to-end training is solved, the performance of a complex analysis task is remarkably improved, and meanwhile, the accuracy of the result is improved. And the conversion from opaque perception to structured and verifiable reasoning is realized.

This invention relates to the field of agricultural information technology and discloses a method for constructing an innovative platform for cultural tourism to support agricultural industrialization based on historical resources. The method includes the following steps: S1: Multi-dimensional deconstruction and annotation of historical resources: Historical resources are collected, and spatiotemporal positioning, thematic hierarchical classification, and semantic attribute annotation are performed to construct a knowledge graph containing multi-dimensional associations of "historical events - geographic space - agricultural production - folk culture." The spatiotemporal positioning is accurate to the year, month, latitude, longitude, or administrative code. Through multi-dimensional deconstruction (spatiotemporal positioning, thematic classification, and semantic annotation) and knowledge graph construction, the originally isolated historical materials are transformed into machine-understandable and reasonable digital assets, supporting cross-temporal and cross-category related queries (such as "what crops were planted in a certain place under what background in a certain year"), facilitating in-depth exploration of potential points of integration between cultural tourism and agriculture.

The invention provides a zoonosis risk prediction method coupling climatic factor time sequence characteristics and land utilization space landscape characteristics under the background of climate and land utilization change, and relates to the technical field of zoonosis space epidemiology. According to the method, land utilization types such as buildings, roads, greenbelts and water bodies are identified through high-resolution remote sensing, landscape features are constructed, weather time sequences such as air temperature, rainfall and humidity are obtained by combining a geographic space big data cloud computing platform, and climate features are extracted by adopting a long-short-term memory network; and meanwhile, spatial population distribution characteristics are formed by utilizing the population data set. And based on the multi-source spatial-temporal characteristics, constructing a multilayer perceptron to simulate and predict the propagation rate. A true propagation rate is calculated based on the case matrix and the susceptible-infected-restorer epidemiological model. And calculating a loss function of the predicted propagation rate and the real propagation rate to optimize deep learning network parameters, and realizing risk dynamic assessment of the coupling space-time propagation mechanism.

The application discloses a kind of dynamic shelter repair method and system based on continuous street view panoramic image, the method is first obtained target panoramic image A and reference panoramic image B to be repaired, generate original pixel-level shelter mask.Secondly, the matching points between panoramic images are extracted, the geometric alignment of image across view angle is realized, and the target perspective C and reference perspective D are obtained by perspective re-projection.Then, the target perspective C and reference perspective D are input into the three-dimensional reconstruction framework, and the three-dimensional point cloud is re-imaged using the depth checking mechanism under the camera pose of the target perspective C.Finally, the image restoration result of three-dimensional point cloud re-imaging is restored to the panoramic coordinate system, and spliced with the original panoramic image, and the de-shelter panoramic image is output.The application ensures that the repair result conforms to the reality and geometric consistency of geographic space, effectively solves the overlapping conflict and visual tearing problem when multi-view projection fusion.

The invention discloses a residential quarter livability evaluation method considering different crowd preferences, and particularly relates to the technical field of human settlement environment evaluation, which comprises the steps of obtaining vector and attribute data of residential quarters in a city, constructing an evaluation basic data set, and constructing an evaluation system containing five-dimensional and three-level indexes such as living safety. And performing quantitative statistics and geographic space indexes, grouping questionnaires according to ages to obtain index weights, and performing weighted summation to obtain a residential quarter livability evaluation result considering crowd preferences. The method focuses on smaller spatial scale to carry out evaluation, gives consideration to objective environmental factors and subjective preferences of different crowds, adopts an adaptive quantification mode for different types of data, accurately measures the influence of various geographic space factors, fully distinguishes the demand differences of different age groups, enables the evaluation result to better fit an actual living scene, and improves the evaluation efficiency. The accuracy and rationality of human settlement environment evaluation are improved, a reliable basis is provided for urban planning and human settlement environment improvement, and ordered promotion of differentiated community construction is supported.

The application discloses a social interaction method and system based on GIS space-time base and digital twin simulation, relates to the field of digital twin and GIS fusion, and comprises the following steps: collecting multi-source heterogeneous data of a target geographic area through sparse sensing nodes, and encoding the data into implicit space-time tokens by using a Bit space-time variational autoencoder; inputting the tokens into a generative world model based on a diffusion transformer as a unique condition, and reconstructing a four-dimensional space-time field; converting the four-dimensional space-time field into a three-dimensional Gaussian sputtering representation, and rendering the representation into a two-dimensional view in real time by using a progressive resolution strategy; updating Gaussian sputtering parameters asynchronously through a time series prediction network in response to newly-arrived sparse sensing data; constructing a social interaction layer on the rendered view, and supporting multi-user distributed access; and predicting a future video frame sequence by using a random mask strategy in response to an interaction instruction. The application realizes the construction of a full-scale, high-fidelity and interactive digital twin world from a macro geographic space to a micro device state.