85 results about "Geoweb" patented technology

The invention discloses a flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting the original data of a flood detention area site, and forming a monitoring array; a monitoring array is divided into a plurality of monitoring units according to geographic grids, a rainfall permeation model is established according to rainfall data and soil humidity data, a surface runoff field is constructed in combination with terrain elevation data, and the confluence direction of the runoff field is calibrated through water level data; based on a rainfall penetration model and a surface runoff field, generating a flood detention area inundation evolution sequence, segmenting into an inundation grade map according to a time step length, planning a flood detention area emergency evacuation path, matching with a road network in a geographic information system, generating an optimal evacuation path, and issuing graded early warning information to an early warning area. And sending a withdrawal instruction to residents in the flood detention area through the emergency broadcasting system. According to the invention, by establishing a partition early warning mechanism and a differential early warning strategy, accurate delivery and efficient transmission of early warning information are realized.

The invention discloses a high-precision three-dimensional point cloud visualization method, electronic equipment and a storage medium, and belongs to the technical field of geographic space information systems.The high-precision three-dimensional point cloud visualization method comprises the steps that S1, a point cloud file is input, and meta-information of the point cloud file is obtained; s2, performing scalable data geographic gridding processing on the point cloud file meta-information to obtain a basic grid object; s3, adopting a data stratified sampling method to obtain a multi-level grid object; s4, constructing a multi-level index file based on the multi-level grid, and outputting the multi-level index file; s5, performing high-availability, high-concurrency and compression processing on the output multi-level index file; and S6, performing real-time hierarchical scheduling rendering on the point cloud based on the processed multi-level index file. A point cloud is quickly split into small blocks by using a gridding technology, then parallel processing is performed, a hierarchical rendering level is generated by performing sub-sampling on a whole data set from bottom to top by using a plurality of sampling strategies, and meanwhile, a quick layering strategy is introduced, so that the throughput is remarkably improved.

The invention provides an operation situation analysis method and device, equipment and a storage medium, and relates to the technical field of data processing, in particular to the technical field of intelligent transportation, automatic driving, large models and data analysis. The specific implementation scheme is as follows: according to a plurality of geographic grid units formed by dividing a target operation area, carrying out gridding aggregation on operation data in a space-time knowledge graph to obtain aggregation operation indexes of the plurality of geographic grid units; generating an operation thermodynamic diagram representing demand distribution according to the current order data of the plurality of geographic grid units; determining the scheduling efficiency of the plurality of geographic grid units according to the aggregation operation index; and for a target grid unit in the operation thermodynamic diagram, predicting an expected operation index of the target grid unit at a future target moment according to the current operation index, the historical operation index and the scheduling efficiency of the target grid unit. According to the scheme provided by the embodiment of the invention, the real-time diagnosis and future prediction of the operation situation are realized by aggregating, presenting and analyzing the data in the space-time knowledge graph.

The invention relates to the technical field of knowledge maps, in particular to an intelligent networked automobile data processing system based on data elements and knowledge maps, and the system comprises a data element definition module which analyzes original intelligent networked automobile data elements, distinguishes the original intelligent networked automobile data elements into a vehicle static parameter set and a vehicle dynamic parameter set, and forms a structured map blueprint. According to the method, element analysis is carried out on the intelligent network connection automobile data, and the intelligent network connection automobile data is divided into the static parameter set and the dynamic parameter set, so that the data has hierarchical structural characteristics, and the controllability and definition of subsequent data processing are improved. And in combination with differentiated structure construction of the static storage layer and the dynamic storage layer, a hierarchical data configuration model with clear logic is formed, and the data scheduling efficiency and the system load stability are improved. A geographic grid coding mode is introduced, space division is carried out on entity data with position attributes, spatial dimension information is made to have high-density indexability, and positioning and regionalization operation can be achieved easily.

The invention provides a distributed quotation data collaborative pushing method and system. The method comprises the following steps: establishing a dynamic association model with a network congestion trend by collecting transmission path environment fluctuation data, and generating environment network association parameters; real-time quotation data is cut by adopting double dimensions of a geographic grid boundary and a commodity classification hierarchy, and dynamic binding of data blocks and storage nodes is realized through a scalable hash ring; generating a dynamic routing strategy based on the environmental parameter energy integral, the geographic grid coverage density and the commodity level update rate, and calculating a transmission path priority by combining a geographic extension direction and a data volume growth trend; and constructing a path state evolution model, evaluating a node failure probability and spectrum energy attenuation, and when a path correction factor exceeds a threshold value, triggering a multi-channel push mechanism. According to the technical scheme provided by the invention, distributed quotation data pushing has a network state self-adaptive multi-path cooperation capability.

The invention relates to an electric vehicle track synthesis method based on differential privacy, and the method is characterized in that the method comprises the steps: obtaining a real electric vehicle track data set of an urban geographic space, carrying out the meshing of the urban geographic space according to regions, and obtaining a divided geographic grid; according to a geographic grid and a road network of an urban geographic space, track movement mode extraction is carried out on a real electric vehicle track to obtain a geographic grid transfer matrix and a region transfer matrix which meet differential privacy constraints; according to the charging pile set in the urban geographic space, modeling is carried out on the consumption of the vehicle driving electric quantity, and an electric quantity state updating rule and a charging guiding strategy of the vehicle are obtained; based on Markov sampling, combined with the region transfer matrix, the grid cell transfer matrix, the electric quantity state updating rule and the charging guide strategy, a synthetic track is generated, the legality of the synthetic track is verified, and an electric vehicle synthetic track data set is obtained; according to the method, the privacy leakage risk is effectively avoided, electric vehicle trajectory data with high behavior fidelity can be generated, and the method is suitable for various privacy protection data analysis and intelligent travel decision-making scenes and has good engineering practicability and popularization prospects.

The invention discloses a building identification method, device and system, and a storage medium. The building identification method comprises the following steps: S1, obtaining geographic space information of buildings in a plurality of data sources; s2, coding the building according to the geographic space information to obtain a building grid coding set table; wherein the building grid code set table comprises a building ID of each data source and a grid code corresponding to the building ID; s3, according to the building grid coding set table, obtaining a spatial relation data table oriented to a geographic grid; wherein the spatial relation data table comprises all data source building ID sets and the number of corresponding overlapped grids; and S4, according to the spatial relationship data table oriented to the geographic grid, calculating the coincidence condition and degree of the corresponding buildings in the plurality of data sources so as to realize building matching. By adopting the technical scheme of the invention, the contradiction between the accuracy of code identification and the high efficiency of code matching is balanced to the greatest extent.

The invention discloses a path planning method based on a city engine and an unreal engine. The method comprises the following steps: completing basic data preparation, rule definition, model generation and visual presentation and analysis, and establishing a city model; secondly, constructing a vehicle motion model in an unreal engine, and simulating weather; the three-dimensional static model and a dynamic model in an unreal engine are fused to generate an urban environment; carrying out geographical gridding on the urban area; introducing a trafficability map and physical restrictions of the vehicle, and setting the trafficability map of the vehicle; an improved A * algorithm based on geographic gridding is introduced, path planning is conducted, the improved A * algorithm is used, and path optimization is achieved through a heuristic function and a penalty function; and outputting the generated path plan.

This invention discloses a method for earthquake landslide hazard zoning based on subtractive clustering ANFIS, belonging to the field of earthquake landslide hazard zoning technology. The method includes the following steps: collecting earthquake landslide monitoring data including seismic data, topographic data, meteorological and hydrological data, and image data; extracting features from the image data to obtain feature-extracted data; based on the earthquake landslide monitoring data, using a subtractive clustering algorithm to determine the initial cluster centers of the earthquake landslide area, thereby obtaining initial clustering information for the earthquake landslide; and combining the earthquake landslide hazard levels and their monitoring data within each geographic grid unit to draw an earthquake landslide hazard zoning map. The data acquisition technology, subtractive clustering algorithm application technology, ANFIS model construction technology, and data calibration technology in this method are closely integrated with modern information technology. The hazard calibration coefficient is used to calibrate the hazard index, improving the accuracy of earthquake landslide hazard assessment.

The invention provides a take-out order scheduling method and system, a storage medium and a program product, and relates to the field of personnel or project management, and the method comprises the steps: generating a path network according to the coordinates of a pickup point and a delivery point of a new order; determining a group of geographic grid indexes covering the path network based on a preset grid cell size to obtain a new order path index set; calculating an intersection index set of the new order path index set and the future path index total set of the candidate rider; and when the number of the unique identifiers of the geographic grid indexes in the intersection index set exceeds a preset path matching threshold value, adding the new order into the order sequence of the target candidate rider with the highest comprehensive score, and adjusting the planned path corresponding to the target candidate rider to complete the scheduling of the new order. By implementing the method, the problem of misjudgment of the smoothness caused by path simplification can be relieved, and the matching accuracy of the order and the rider is improved, so that the overall delivery efficiency is improved.

The invention discloses an energy supply side optimization management system and an implementation method, and relates to the technical field of energy management. The method comprises the following steps: acquiring source end data of an energy supply side, analyzing the source end data through a multi-level protocol adaptation engine and a dynamic rule matching engine, and extracting various energy features and energy supply side GIS (Geographic Information System) information; constructing a multi-energy flow topology constraint matrix based on energy features, performing fine-grained geographic grid division based on energy supply side GIS information, and determining grid-level energy dynamic parameters; performing dynamic trend adjustment on the grid-level energy dynamic parameters in combination with the multi-energy-flow topology constraint matrix, and determining a grid-level energy dynamic prediction map; searching an optimal tradeoff solution set through a population aggregation and dispersion search algorithm; and carrying out constraint solution on the optimal tradeoff solution set in combination with the safe operation boundary, and determining an energy scheduling optimal solution. According to the invention, the accuracy and foresight of energy management are improved, the loss link is accurately traced, and the system is driven to continuously evolve towards the low-carbon and high-efficiency direction.

The present disclosure is related to an electronic device and a method for cluster based object sensing. A method for object sensing comprises: selecting one or more network nodes from a cluster of network nodes associated with a geographic grid; and triggering the one or more network nodes to sense one or more objects in the geographic grid.

Disclosed here are methods, systems, and apparatuses for optimizing in-building network coverage through aggregation of usage data, network data, and other performance metrics and geospatial analysis employed to map network coverage data onto a geographic grid, facilitating spatial visualization of performance. The network coverage data is mapped to the buildings data to create a spatial visualization. A model is then applied to the network coverage data and the spatial visualization to identify buildings with a network coverage score below a threshold, and an appropriate action is performed based on the identification.

To facilitate efficient entity resolution, systems and methods include a first dataset is received from a first database associated with a first entity and a second dataset is received from a second database associated with a second entity. A geo-grid is mapped to a geographical area covering the first entity data records and the second entity data records. A grid matching area in the geo-grid is generated for each first entity data record based on latitude data and longitude data of each first entity data record. Candidate matching records are determined from the second entity data records based on respective grid matching areas. Actual matching records are determined from the candidate matching records based on a threshold for a trigram similarity between each candidate matching record and the respective first entity data record. The actual matching records are associated with the respective first entity data record in the first database.

The invention relates to the technical field of computers, in particular to a site selection model construction method based on a knowledge graph, a site selection method based on a site selection model, computer equipment for implementing the methods, a computer readable storage medium and a computer program product. The site selection model construction method comprises the following steps: acquiring a data set for site selection analysis; extracting data from the data set according to the knowledge graph framework and constructing a knowledge graph; extracting knowledge graph characteristics taking the user identifier as a main key from the knowledge graph; dividing the site selection area into geographic grids, and extracting grid features; fusing the knowledge graph features and the grid features to form a fused feature set; and based on the fused feature set, a machine learning algorithm is used to train a site selection model, and the site selection model is used to predict the site selection value of each geographic grid.

This invention discloses a building identification method, apparatus, system, and storage medium, comprising: step S1, acquiring geospatial information of buildings from multiple data sources; step S2, encoding the buildings according to the geospatial information to obtain a building grid code set table; wherein, the building grid code set table includes: the building ID of each data source and its corresponding grid code; step S3, obtaining a spatial relationship data table oriented towards the geographic grid based on the building grid code set table; wherein, the spatial relationship data table includes: the set of building IDs of all data sources and the number of corresponding overlapping grids; step S4, calculating the overlap and degree of corresponding buildings in multiple data sources based on the spatial relationship data table oriented towards the geographic grid, so as to achieve building matching. The technical solution of this invention maximizes the balance between the accuracy of coding identification and the efficiency of coding matching.

This application relates to the field of data analysis technology, and in particular to a method, apparatus, computer device, storage medium, and computer program product for extracting profile lines from oblique photogrammetry data. The method includes: dividing the oblique photogrammetry data into multiple block files according to a geographic grid, with each block file containing a root node corresponding to a specific block file, and hierarchically storing node files corresponding to child nodes derived from the root node; identifying target block files and target node files that intersect with the projection line data; reading specific geometric data from the target node files, with the geometric data associated with profile line extraction; using a preset intersection algorithm to traverse the geometric data in all target node files to obtain profile points, and connecting the profile points to generate target profile lines. This method can improve the efficiency of profile line extraction in large-scale oblique photogrammetry data and reduce the computational load of profile line extraction.

The invention relates to the technical field of fire risk early warning, and particularly provides a power grid facility fire danger early warning method and device, and the method comprises the steps: obtaining fusion perception data of a geographic grid to which a to-be-analyzed power facility belongs; taking the fused sensing data as a pre-trained neural network model to obtain a fire danger early warning degree of the to-be-analyzed electric power facility output by the pre-trained neural network model; performing fire danger early warning on the to-be-analyzed electric power facility based on the fire danger early warning degree of the to-be-analyzed electric power facility; wherein the fusion sensing data comprises at least one of satellite heat source point data, meteorological element data and power grid data. According to the technical scheme provided by the invention, the space-time precision of fire danger prediction and the disaster prevention toughness of a power grid are effectively improved, and accurate technical support is provided for active defense strategies such as activation of a fire barrier of a power transmission corridor, dynamic load scheduling and the like.

The invention relates to the technical field of land value evaluation data processing, and discloses a land value evaluation and circulation transaction system based on multi-source heterogeneous data fusion, which comprises the steps of converting policy texts, environment monitoring and market transaction data into standardized influence vectors, calculating an information unbalance degree through geographic grid projection to represent data conflict intensity, and calculating a data conflict degree; according to the method, internal contradictions of multi-source data are spatially developed, conflicts masked in traditional assessment are quantified into operable risk indexes, meanwhile, dynamic confidence degree modulation is achieved in combination with transient feature analysis, and the reliability of the system is improved. Land value judgment has trend insight and risk early warning capabilities, and a high-credibility decision-making basis is provided for circulation transaction.

Embodiments of the present application provide a positioning method, device, equipment and storage medium, wherein the method can comprise: first, obtaining positioning request information of a target object, the positioning request information comprising historical positioning information of the target object, WIFI fingerprint features scanned by a device sending the positioning request information and base station fingerprint features; second, determining a target geographic grid where the target object is located according to the historical positioning information, the WIFI fingerprint features and the base station fingerprint features; and third, determining target positioning information of the target object based on fingerprint features corresponding to the target geographic grid, the fingerprint features comprising fingerprint features of a target WIFI and / or fingerprint features of a target base station that can be scanned in the target geographic grid. The embodiments are used to solve the problem of inaccurate positioning results in the related art.

The invention discloses a destination reasoning method for urban vehicle tracking. The method comprises the following steps: firstly, carrying out adaptive segmentation on an original track by adopting an improved minimum description length algorithm to obtain a globally optimal track segment; then, through grid coding and a Base2vec model, the trajectory sequence is mapped into a low-dimensional distributed vector containing a geographic topological relation, namely a trajectory embedding sequence, and the sparse problem in spatial expression is relieved; then wavelet transform is applied to the track embedding sequence, and detailed information such as rapid turning or acceleration and deceleration in target movement is captured; and finally, an LSTM network is adopted, and a self-attention mechanism is integrated in the LSTM, so that the model can dynamically weight the output of different time steps according to the current trajectory segment, the network adaptively pays attention to key points in the trajectory, and the modeling capability for long-term dependence is enhanced. And finally, classifying the output of the LSTM network through a SoftMax classifier, converting a classification result into a specific geographic grid coordinate, and obtaining a predicted target destination position.

The invention discloses a built environment crowd perception space mapping and quantitative evaluation method oriented to an urban green land scene. The method comprises the following steps: acquiring and processing multi-source heterogeneous data; carrying out recreation perception semantic mining and quantification on the social media comment text, and constructing perception scores for different ecosystem culture service types; mapping the perception score space to geographic grid units based on semantic association, and calculating an accumulated perception index of each unit by adopting a differential weight distribution algorithm according to spatial scale characteristics; extracting built environment characteristics of each grid unit; and training a machine learning model based on the accumulated perception indexes and the built environment features, establishing a mapping relation between the accumulated perception indexes and the built environment features, and performing perception index deduction on the blank area by using the model to generate global CES perception distribution. According to the method, fine spatial mapping of coordinate-free texts is realized, a perception prediction model based on environmental characteristics is established, and data-driven decision support is provided for fine design and management of a green land.

This invention discloses an automated fusion method and system for suspended sediment concentration from multi-source ocean color satellites, belonging to the field of marine remote sensing and geographic information processing technology. The method includes: S1, automatically identifying and reading the original satellite data file; S2, processing the quality labeling data to generate a comprehensive quality mask in one go, and marking invalid values ​​for the concentration data; S3, resampling all valid pixels onto a predefined regular geographic grid, calculating the cumulative concentration and pixel count of each grid cell to obtain the grid mean; S4, stacking multiple grid data from the same region and time window along the time axis, and performing spatiotemporal fusion using the median robust averaging method or the minimum value synthesis method; S5, automatically generating a standard raster file and a thematic map containing geographic features and embellishment information based on the fused data. This invention solves the problems of fragmented processing, low efficiency, and insufficient accuracy in existing technologies.

The invention discloses a satellite positioning-based shipborne temperature and salinity dragging observation method and system, and the method comprises the steps: obtaining a real-time position signal and the temperature and salinity data of observation equipment, and carrying out the fusion and initial time sequence alignment processing, and obtaining an initial position estimation sequence; comparing the initial position estimation sequence with a preset ship trajectory, and if a comparison result shows that the deviation exceeds a first preset threshold value, performing recursive updating on the real-time position signal to obtain a filtered position trajectory; based on the filtered position track, acquiring environment interference characteristics at the current moment, and compensating the real-time position signal to obtain a compensated accurate position coordinate; if the matching degree of the accurate position coordinate and the position record of the historical observation data is lower than a second preset threshold value, performing iterative correction on the accurate position coordinate to obtain final position mapping; and carrying out correlation matching on the temperature and salinity data and the corresponding geographic grids based on the final position mapping, and generating an observation data set with a position label.

The invention provides a dynamic operation and maintenance resource scheduling method and device based on reinforcement learning and electronic equipment, relates to the technical field of artificial intelligence, and aims to determine the space-time preference of a to-be-processed work order based on the geographic grid information of an operation and maintenance area, so that the comprehensive adaptation condition of the work order in the dimensions of geography, skills, battery reserve and the like can be clarified. And furthermore, work order demands are quantified based on work order modal factors, a final target geographic grid and corresponding operation and maintenance resources are determined in combination with global geographic skill collaborative knowledge, and an optimal scheduling scheme considering short-term efficiency and long-term balance can be obtained through overall planning of response efficiency, skill matching degree, resource load balance and battery reserve level. In conclusion, the method can fundamentally overcome the defects of local decision and short scheduling in the prior art, solves the problems of resource mismatching, low efficiency, high cost, uneven load and the like, and improves the stability and intelligent level of an operation and maintenance system.

The invention discloses an intelligent processing method and system for agrometeorological observation data, and relates to the technical field of agrometeorological data processing, and the method comprises the following steps: S1, receiving the agrometeorological observation data, including manual input data, Internet of Things equipment data and satellite remote sensing data; s2, performing block chain evidence storage operation on the agrometeorological observation data, and generating a non-tampering record including an operator signature, a data hash chain and a timestamp; s3, constructing a three-level cleaning mechanism to verify the agrometeorological observation data; s4, mapping the verified agrometeorological observation data to a geographic grid space, and fusing multi-source data to generate a space-time continuous data set; and S5, carrying out crop growth stage analysis, disaster influence evaluation and yield prediction based on the fused data, and outputting a result. According to the method, human tampering is completely eradicated from the source through full-link trace leaving, and meanwhile, data is scientific and reliable through data cleaning and verification.

The invention discloses a recommendation system based on unstructured local business data, and the system comprises a multi-mode semantic coding module which is used for receiving text comments, picture tags and social statements and outputting a unified semantic vector; the spatial semantic fusion mechanism is used for embedding the geographic position, the business district label and the time period into the semantic vector based on the spatial position code; the semantic graph construction and graph neural network module is used for forming a heterogeneous semantic graph by taking stores, commodities, users, comment themes, picture tags, time periods and geographic grids as nodes and establishing edges through co-occurrence and causal dependence, and outputting node-level, sub-graph-level and graph-level embedding vectors; the causal reasoning and anti-fact analysis module is used for estimating a causal effect and generating a causal weight by taking the recommended action as an intervention variable and the operating index as a result variable; and the interpretable recommendation generator is used for outputting the tetrad based on the embedded vector and the causal weight. The invention aims to improve the business intelligent analysis and recommendation capability.

This invention discloses a method, system, device, and medium for integrating multi-source heterogeneous electric carbon data, relating to the field of energy data processing technology. The specific steps are as follows: extracting key identification information and original measurement values ​​from the original messages and preprocessing them to generate semantic tags and a geographic grid index; performing carbon emission calculations and fusing the geographic grid index to construct a standardized electric carbon data frame; constructing a topological correlation map, calling a graph neural network to calculate theoretical state prediction values, generating corrected electric carbon data, generating digital fingerprints for the corrected electric carbon data and verifying the data, and generating data blocks to store in an electric carbon data lake. This invention eliminates temporal deviations in heterogeneous data by constructing semantic and spatiotemporal indexes, utilizes graph neural networks to achieve intelligent identification and automatic completion of abnormal data, and combines a blockchain notarization mechanism to ensure data immutability and full-process traceability.

Provided in the embodiments of the present disclosure are a data processing method, a data query method, a computing device, a storage medium, and a program product. A target region is divided into a geographic grid, and for at least one geographic grid cell covered by a target spatial object in a database, a grid index is queried to determine a grid cell record corresponding to the at least one geographic grid cell, wherein the grid cell record comprises a key-value pair that takes a grid cell code as a key and takes a primary key set of at least one spatial object as a value; and on the basis of a primary key of the target spatial object, the grid cell record is updated, wherein the grid index is used for determining, for an object to be queried in a query command, a target primary key set consisting of a value in a grid cell record that corresponds to at least one geographic grid cell covered by the object to be queried, and executing a query operation taking into account the target primary key set. The technical solution provided in the embodiments of the present disclosure improves the query efficiency.

The invention discloses an open source information collection and integration system and method based on geographic grid coding, and belongs to the technical field of information collection. The method comprises the following steps: obtaining a to-be-divided geographic area and to-be-judged information; the method comprises the following steps: performing geographic space grid hierarchical system division on a to-be-divided geographic region, and performing geographic grid coding on each grid unit; after all information elements in the grid are stored based on geographic grid codes, entity relation extraction is carried out to generate a triple knowledge graph; performing multi-source heterogeneous data cleaning and disambiguation and event time mapping on the geographic grid after the generation of the triple knowledge graph; and carrying out association relation judgment and credibility judgment on the to-be-judged information based on the geographic grid after the event time mapping is completed. According to the method, global subdivision grid coding is utilized, any geographic area is converted into a computable grid set, multi-source and multi-mode open source information in the area is crawled, and rapid convergence, cross verification and credible presentation of the information are achieved.