1204 results about "Geo spatial" patented technology

The invention provides a gas leakage time-space correlation early warning method and system based on multi-modal data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting real-time concentration data and a historical leakage accident database through an NB-IoT combustible gas sensor network disposed at a key node of a gas pipe network; according to the real-time concentration data and a historical leakage accident database, a UTM projection coordinate system is adopted to carry out registration on geographic space data, a three-dimensional space mapping model of a pipe network topological structure is established, timestamp alignment is carried out on all dynamic monitoring data streams through an NTP clock synchronization protocol, and a time-space correlation standardized data set is formed. According to the invention, full-chain intelligent management of the gas leakage risk from sensing, prediction to disposal is realized.

The invention discloses a city expansion area transformer substation layout method and system based on an artificial intelligence algorithm. The method comprises the following steps: predicting future electrical load demand data of an area by mining an association rule between geographic space data and historical load data of the city expansion area; analyzing the prediction data by using a large language model, partitioning the city expansion area according to an analysis result, obtaining each sub-area and load difference data thereof, combining the data with historical power distribution network grid structure data of the city expansion area, and processing the data through the large language model to obtain an initial layout scheme of the transformer substation; by simulating the power supply capacity of each sub-region at different load growth rates, the initial layout scheme of the transformer substation is optimized, an optimization scheme is obtained and executed, and the optimal layout of the transformer substation is obtained by comprehensively applying artificial intelligence algorithms such as a large language model and a machine learning algorithm dynamic planning algorithm. Accurate prediction of the electrical load demand of the urban expansion area and effective optimization of the transformer substation layout scheme are realized.

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 a multi-source sea area data intelligent supervision method, system and device and a storage medium, and the method comprises the steps: obtaining sea area geographic space data and historical sea area supervision data of a target sea area, and carrying out the monitoring construction, and obtaining an island monitoring grid; acquiring multi-source monitoring data of the target sea area according to the island monitoring grid, and performing grid data identification to obtain a global pattern spot dynamic data set; extracting a suspected specific target area coordinate of the global pattern spot dynamic data set and calculating a coordinate specific target confidence coefficient; and carrying out range comparison on the suspected specific target area coordinates, and carrying out behavior evaluation in combination with the coordinate specific target confidence to obtain a sea area specific target early warning report.

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

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 Otwei interactive maps, and discloses a geological data interaction method and system based on an Otwei interactive map. The method comprises the following steps: carrying out feature recognition and structural analysis on original geological data to obtain a standardized data packet, and establishing a geographic space reference conversion index table; performing feature extraction and classification on geological elements in the standardized data packet to obtain structured geological data; importing the structured geological data into an Ovoucher interactive map, and carrying out local registration through a mesh generation technology to obtain a visual geological element map layer; geological element drawing and attribute input are carried out, and an edited geological data set is obtained; and carrying out structure recombination and reverse coordinate conversion to obtain a standard format data file adaptive to the target geological information system. According to the method, accurate conversion of multi-source geological data between different coordinate systems and measuring scales is realized, and the problem of spatial dislocation during integration of different-source geological data in a traditional method is effectively solved.

The invention discloses a building carbon emission analysis method based on an STIRPAT algorithm and a building information model technology, and the method comprises the steps: collecting the whole life cycle information of a building through the BIM technology in a data collection and integration stage, covering the data of all links of design, construction and operation, widely collecting the external environment data of a project location, such as social economy, energy technology, etc. Multi-source data such as geographic space and weather are introduced and fused, and real-time monitoring is performed by means of an intelligent sensing technology. Secondly, an extended STIRPAT model is constructed, core independent variables such as population, economy and technology and control variables such as building scale are comprehensively considered, and the influence of factors is accurately quantified through dynamic parameter adjustment and scene simulation prediction; according to the method, carbon emission attributes are given to components and equipment based on a BIM model, full-life-cycle carbon emission is simulated and calculated, integrated optimization is carried out with solar photovoltaic, intelligent control, waste management and other technologies, finally, results are comprehensively analyzed, carbon emission distribution and trend are displayed by utilizing a BIM visualization function, and a building low-carbon decision is provided.

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention discloses a 3DTiles data storage method and a map data query method.The 3DTiles data storage method comprises the following steps that (1) an LOD hierarchy pyramid type hierarchy table is established, and an LOD range index is created; 2) generating a three-dimensional object into an agent point, and segmenting a tile according to a KD-tree structure to construct a three-dimensional KD-tree index tree; and 3) storing the data of the KD-tree leaf node into a GridFS of MongoDB multi-mode storage, mapping a file path into a unique hash value, writing the unique hash value into a corresponding node according to a KD-tree node storage structure, and creating a geographic spatial index. The method creatively solves the problems that file system management is difficult due to the fact that the number of files is large, a hierarchical structure based on a file directory is difficult to support efficient spatial range retrieval, and the spatial query response time is reduced to the millisecond level in combination with a three-dimensional KD-tree index and a MongoDB geographic spatial index.

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 discloses an intelligent nuclear radiation dose real-time monitoring system based on deep learning, and the system comprises the steps: S1, a multi-mode sensing module which is used for outputting radiation intensity data with timestamps, environment image data and geographic space position information; s2, a preprocessing module used for outputting a structured feature sequence; s3, a fusion modeling module which is used for generating a unified fusion feature vector; s4, an end-edge reasoning module which is used for processing the fusion feature vector in stages and generating a nuclear radiation dose prediction result; s5, the task scheduling module is used for dynamically determining whether the reasoning task is executed on the end side or the edge node; s6, an anomaly detection module which is used for determining whether there is an abnormal change of the nuclear radiation dose; and S7, the system control module is used for coordinating the modules. The method has the advantages of being high in environmental adaptability, fast in reasoning response and high in anomaly recognition precision.

The invention provides a three-dimensional modeling and mapping method, system and device based on a fixed-wing unmanned aerial vehicle, and relates to the technical field of geographic mapping. According to the method, through synchronous data acquisition of an inclined camera, a laser scanner and a position and attitude measurement system, and in combination with navigation trajectory calculation, point cloud coordinate transformation, image attitude calculation and multi-source data fusion processing, space collaboration of image data and point cloud data is realized under a unified coordinate system. And performing aerial triangulation based on the fused data, further carrying out spatial modeling and texture mapping, and effectively improving geometric consistency and modeling continuity among multi-source data. According to the method, ground control points do not need to be arranged, high-precision and high-consistency geographic space information expression can be achieved, and real scene modeling and multi-type surveying and mapping data result generation in a large-scale mapping scene are supported.

The invention discloses an unmanned aerial vehicle airport site selection method suitable for large-scale electric power line inspection, and the method comprises the steps: S1, carrying out the primary selection of a geographic space, constructing a geographic constraint evaluation model, carrying out the processing of elements and historical disaster data through discrete coding, generating a safe and suitable region through the multi-factor weighted stacking space analysis, and carrying out the calculation of an evaluation result; and a K-Means clustering algorithm based on Euclidean distance is combined to screen a primary selection region. S2, risk assessment preselection: constructing an entropy weight-TOPSIS risk assessment model, determining a risk factor weight by using an entropy evaluation method, calculating a scheme closeness degree through a TOPSIS algorithm, and screening a low-risk preselection region; and S3, constructing a binary relationship between a demand center set and a pre-selected site set by combining an electric power inspection target and taking an electric power inspection task target as a demand node, measuring weighted traffic cost through Euclidean distance, designing a double-layer nested 0-1 decision variable, and realizing optimization of an airport deployment area by taking minimization of a total weighted distance as a target function.

The invention relates to the technical field of road maintenance intelligence, and discloses a road intelligent maintenance decision-making system based on multi-dimensional evaluation and deep reinforcement learning, and the system comprises a geographic space data preprocessing module, a fuzzy TOPSI S evaluation module, a context awareness DQN strategy module, a credibility driving recommendation module, and a security strategy library module. The method comprises the following steps: generating a road health degree and a clustering label through multi-source data geographical weighted preprocessing and fuzzy TOPSI S dynamic weight evaluation; a reinforcement learning reward function is configured based on label differentiation, and a strategy space is explored in combination with noise; the confidence is verified through multiple models, a historical security policy is matched, and model optimization is driven through priority sampling injection samples. According to the method, the evaluation precision is improved through entropy weight-clustering dynamic weight, and flexible decision is realized in combination with reinforcement learning; and three-level security verification and closed-loop optimization are constructed to ensure that the risk is controllable, historical experience migration and multi-source data expansion are supported, and the cross-scene adaptive capacity is enhanced.

The embodiment of the invention relates to the technical field of ground surveying and mapping data modeling, in particular to a ground surveying and mapping driven space-time evolution modeling method and system.The method comprises the steps that firstly, multi-dimensional correlation features of elevations, coverage types and deformation data in at least three continuous periods are integrated; the limitation of single time point or single parameter analysis is broken through, and the nonlinear space-time coupling rule in earth surface evolution can be accurately captured; secondly, a spatial-temporal model construction method based on dynamic parameter optimization effectively fuses geographic spatial correlation features and coverage evolution trends, so that the model has adaptive characterization capability for a complex geographic process; in addition, by establishing a closed-loop feedback mechanism of prediction results and model parameters, real-time iterative optimization of the model parameters is realized, and long-term prediction stability in different geographical scenes is remarkably improved.

The invention provides a land geographic surveying and mapping model generation method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a geographic surveying and mapping data set containing a satellite remote sensing image, surface elevation and the like in a target region, then carrying out the topographic feature interpretation, and generating a topographic semantic, surface coverage and surveying and mapping task correlation feature set; then calling a geographic space encoder to carry out multi-scale space modeling on the related feature set to obtain a geographic space representation vector set, fusing the geographic space representation vector set with a preset geographic entity topological graph dynamic feature to generate a geographic entity related feature set, and carrying out multi-scale space modeling on the geographic entity related feature set; and finally, training a geographic surveying and mapping analysis model according to the geographic entity associated feature set and the surveying and mapping task associated feature set, generating a geographic surveying and mapping optimization strategy, and feeding back the geographic surveying and mapping optimization strategy to a surveying and mapping terminal to trigger surveying and mapping parameter dynamic adjustment operation so as to realize more optimized land geographic surveying and mapping model generation.

A system for assessing the ecological sensitivity and development potential of areas within ecologically sensitive mountain towns, consisting of: a data acquisition module configured to extract various data types, including multi-source geospatial data, environmental data, built environment data, socio-economic data and community participation data, with the extracted data being validated and stored in a database; a central processing unit comprising one or more processors connected to a memory, the processor communicatively connected to the database in which the extracted data is stored, the memory storing instructions that, when executed, configure the processor to implement the following: a data input module configured to receive data corresponding to multiple indicators from different assessment dimensions, including ecology and environment, built environment, economy, heritage and culture, mobility, location, community participation, visual aspects and safety from hazards; an indicator evaluation module configured to evaluate each indicator according to predefined sensitivity categories and assign corresponding sensitivity index values in the range of 0.1 to 1; a dimension weighting module configured to apply dimension-specific weights to calculate weighted scores for each indicator; a development potential calculation module that aggregates the weighted assessments to determine an overall sensitivity assessment and assigns this overall sensitivity assessment to a development potential rating scale; and a visualization module configured to generate visual representations and reports of the assessment results; and an output module having a user interface operatively connected to the visualization module of the central processing unit, the output module configuring the user interface to display the visualized results and the final index value obtained from the central processing unit.

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.

The invention relates to the technical field of urban planning, in particular to an industrial space suitability optimization method and system, and the method comprises the steps: collecting geographic information data, industrial data, industrial service related data, enterprise information and infrastructure data as a multi-source data set, associating the multi-source data set with geographic space coordinates, and inputting the geographic space coordinates into a database; the adjusted weight coefficient is applied to the LSTM-ARIMA hybrid model, and updated future industry prediction data is obtained; generating optimized industrial land layout scheme data based on a preset parametric modeling rule; determining planning scale data selected by a user, and obtaining a unified database associated with cross-level data; and comparing the land utilization change condition and the enterprise activity state data with data in the unified database, and when it is detected that data updating lag exists, updating the latest data after verification and prediction filling to the unified database. According to the invention, the adaptability and the intelligent level of urban industrial space planning 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 hydraulic ring surveying and mapping, in particular to a hydraulic ring surveying and mapping data acquisition method and system based on geographic space information, and the method comprises the following steps: S1, data acquisition and preliminary data set generation: generating an initial geographic space data set; s2, space-time alignment and coupling feature extraction: constructing a preliminary geographic space model; s3, dynamic verification and cross verification: generating a dynamic verification parameter set; s4, three-dimensional geologic modeling and parameter fusion: generating a comprehensive three-dimensional geologic model; s5, environment interference correction and data optimization: analyzing the offset of an environment interference factor to sensor data through an adaptive learning algorithm, and outputting corrected hydraulic ring surveying and mapping data; and S6, safety threshold comparison and early warning map generation: generating a visual early warning map. According to the method, deep support is provided for underground structure analysis, and the comprehensive capability of hydraulic ring surveying and mapping is improved.

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 embodiment of the invention provides a map plotting method and device based on a large language model, equipment and a medium, and relates to the technical field of map plotting based on the large language model, and the method comprises the steps: obtaining raster image data and vector road data, so as to build a geographic space reference coordinate system; collecting a positioning information text corpus based on natural language description; building a spatial semantic processing model based on a self-attention mechanism according to the geographic space reference coordinate system and the positioning information text corpus; receiving a mixed instruction input by a user, and converting the mixed instruction into a structured instruction tree; generating a coordinate set according to the structured instruction tree, and generating a coordinate set scheme after verifying the coordinate set; plotting the coordinate set scheme into an interactive map based on a spatial semantic processing model; a user is supported to input a mixed data instruction, complex semantics in a natural language can be analyzed, map drawing is automatically carried out, and map drawing efficiency is improved.

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.

The invention discloses an action scheme computable modeling method and system, particularly relates to the field of simulated confrontation, is used for solving the problem of multi-platform collaborative path planning in a dynamic environment, takes a multi-constraint hierarchical hypergraph as a core, carries out fusion abstraction on space, time and resources, and compresses a search domain through convex hull relaxation expansion and gradient sampling so as to realize multi-platform collaborative path planning. Meanwhile, a stable factor is dynamically adjusted by means of resource phase mapping and quantum taboo migration, and it is ensured that a global optimal solution is rapidly approached; fine time sequence correction is further achieved through symplectic time window rearrangement and conjugate gradient slippage, the survival potential and coverage confidence coefficient of the path are verified through adversarial prediction simulation, and the reliability and safety of the scheme under the disturbance of a frequently-changing three-dimensional geographic space, a dynamic forbidden area and moving obstacles are enhanced; therefore, strict time sequence and resource requirements of the multi-platform collaborative task are effectively met, and advanced computational modeling support is provided for the multi-platform collaborative task.