68 results about "Geoweb" patented technology

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 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.

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.

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.

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.

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.

The invention relates to the technical field of instant distribution, and discloses a distribution addressing auxiliary method and system based on spatial modeling and real-time excitation. The method comprises the following steps: on the basis of a three-dimensional geographic grid generated by taking an addressing auxiliary anchor point as a center, converting an unstructured description related to addressing into structured data containing an azimuth vector and a grid coordinate; value quantification is carried out on the structured data according to multiple dimensions including a time dimension, a reputation dimension and a problem dimension, and a value information score is obtained; calculating a real-time dynamic reward given to the rider based on the value information score in combination with the addressing difficulty of the distribution position; and triggering and pushing the structured data through the geofences arranged around the addressing auxiliary anchor points, and performing dynamic iterative optimization according to the collected feedback data of the rider. According to the invention, through the cooperative effect of spatial modeling, multi-dimensional value quantification, real-time excitation and closed-loop optimization, the instant distribution service efficiency can be improved.

The application discloses the technical field of information collection and discloses an open-source information collection and integration system and method based on geographic grid coding, comprising: obtaining a geographic area to be divided and information to be determined; after the geographic area to be divided is divided according to a geographic spatial grid hierarchy system, geographic grid coding is performed on each grid unit; after all information elements in the grid are stored based on the geographic grid coding, entity relationship extraction is performed to generate a triple knowledge graph; after the geographic grid whose triple knowledge graph is generated is subjected to multi-source heterogeneous data cleaning and disambiguation and event time mapping; based on the geographic grid after the event time mapping is completed, the information to be determined is subjected to association relationship determination and credibility determination. The application converts any geographic area into a calculable grid set by using global split grid coding, crawls multi-source and multi-modal open-source information in the area, and realizes rapid convergence, cross-verification and credible presentation of information.

The invention discloses an earthquake fortification management and disaster analysis system based on unmanned aerial vehicle brain-like real-time calculation, which relates to the field of disaster analysis and comprises an image set acquisition unit used for acquiring a time sequence image set, a feature map extraction unit used for outputting corresponding N convolution feature maps, a group classification unit used for acquiring feature groups and a feature map fusion unit. The fusion feature map obtaining unit is used for obtaining a fusion feature map, the feature arrangement unit is used for generating a feature geographic distribution map, and the disaster detection unit is used for obtaining a disaster feature geographic distribution map; wherein the disaster feature geographic distribution map represents multi-view fusion disaster features of all geographic grids in the time window. The evolution graph construction unit is used for constructing a disaster feature space-time evolution graph; the disaster feature geographic distribution map finally formed by the method can accurately reflect the spatial distribution and evolution process of the damage state of the disaster area, and provides structured disaster data for earthquake fortification management and disaster analysis.

The invention discloses a driver and passenger matching guiding method and system based on dynamic supply and demand prediction, and the method comprises the steps: enabling a thermal service to initiate a request for obtaining predicted order issuing data to an algorithm service based on the predicted supply and demand data after receiving a predicted supply and demand data request, carrying out real-time statistics on current idle driver data in each geographic grid according to the driver position data and the driver departure data; the algorithm service receives a request for predicting order-issuing data, calls a prediction model to obtain the predicted order-issuing data of each geographic grid, and feeds back the predicted order-issuing data to the thermal service; and according to the current idle driver data and the predicted order issuing data, obtaining predicted supply-demand ratio data in each geographic grid, according to the predicted supply-demand ratio data in each geographic grid and the supply-demand ratio reference data, matching the heating power gear data, and feeding back the heating power gear data to the client. The traditional historical heating power is upgraded to a dynamic probability field, and through minute-level order density prediction, the driver order receiving prediction accuracy is greatly improved.

The invention discloses a data storage method and system for online car-hailing, and the method comprises the steps: receiving the heartbeat data of a driver through a distributed message queue, and enabling the heartbeat data of the driver to at least comprise latitude and longitude coordinates, business state data and metadata; consuming longitude and latitude coordinates through a grid index builder, and obtaining a corresponding geographic grid according to the longitude and latitude coordinates; according to the geographic grid, the grid index builder sends an index updating request to the grid index service cluster, and the grid index service cluster updates driver list data; the service state data and the metadata are consumed through a state processor, and the service state data and the metadata are integrated and then stored in a multi-level cache storage cluster; the matching engine obtains updated driver list data through the grid index service cluster; and according to the updated driver list data, the matching engine sends batch requests to the multi-level cache storage cluster to obtain total data of the target driver. According to the invention, the matching delay can be obviously reduced and is more stable.

This invention provides a data spatial query method, apparatus, electronic device, and computer-readable storage medium. The data spatial query method provided by this invention includes: acquiring target business data; constructing a spatial field based on the latitude and longitude data of the target business data; importing the spatial field into a preset index; acquiring the latitude and longitude data of two diagonal vertices within a visualized map area; determining a data query range based on the latitude and longitude data of the two diagonal vertices; acquiring a geographic grid based on the data query range and the shrinkage ratio of the visualized map; and aggregating and statistically analyzing the target business data within the geographic grid area based on the spatial field to obtain query results. The data spatial query method provided by this invention can aggregate points on a large number of maps and respond promptly to the aggregation.

The invention provides a community intelligent planning governance service method, which comprises the following steps of: acquiring spatial data and business data, and acquiring user demand data; performing spatio-temporal indexing on the spatial data and the business data according to the geographic grid of the community; preprocessing the obtained user demand data, and carrying out semantic association on the user demand data, the spatial data and the business data; performing intention recognition on the obtained user demand data, and judging whether the user intention is clear or not; when the user intention obtained through intention recognition is clear, dialogue response content is generated according to the user demand data; the dialogue response content is supplemented and recorded in a multi-round dialogue memory and context association mode; and obtaining dialogue response content as service content and outputting the service content. According to the method, key information and intention in user requirements can be accurately identified, so that response content better meeting the actual requirements of the user is generated, and the response accuracy of the system and the user satisfaction degree are improved.

The invention relates to the technical field of online car hailing, and discloses a transport capacity management method and device and computer equipment, and the method comprises the steps: a server side receives an original position point reported by a user side in a preset range, updates the original transport capacity distribution information of a geographic grid corresponding to the preset range in a server map according to the original position point, and stores the original transport capacity distribution information; generating a corresponding transport capacity scheduling strategy; and the user side sends an original position point of the user side to the server side, receives a push data packet returned by the server side based on the original position point, analyzes the scheduling suggestion information and the transport capacity feedback information in the push data packet, executes an updating operation of a user side map according to the transport capacity feedback information, and executes a reminding operation of the scheduling suggestion information at the same time. Through interactive cooperation of the server side and the user side, dynamic updating and accurate scheduling of transport capacity distribution are realized, and the utilization efficiency of transport capacity resources is effectively improved. According to the invention, the problem of unbalanced supply and demand of transport capacity caused by asymmetry of driver and passenger information in the prior art is solved.

The application provides a distributed quotation data collaborative pushing method and system. Wherein, through collecting transmission path environment fluctuation data, a dynamic correlation model with network congestion trend is established to generate environment network correlation parameters; real-time quotation data is cut in dual dimensions of geographic grid boundary and commodity classification level, dynamic binding of data blocks and storage nodes is realized through a scalable hash ring; based on environment parameter energy integration, geographic grid coverage density and commodity level update rate, a dynamic routing strategy is generated, transmission path priority is calculated in combination with geographic expansion direction and data volume growth trend; a path state evolution model is constructed to evaluate node failure probability and spectrum energy attenuation, when the path correction factor exceeds the threshold, a multi-channel pushing mechanism is triggered. The technical scheme provided by the application enables the distributed quotation data pushing to have network state adaptive multi-path collaborative capability.

The application 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: collecting 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 characteristics and GIS information of the energy supply side; constructing a multi-energy flow topology constraint matrix based on the energy characteristics, performing fine-grained geographical grid division based on the GIS information of the energy supply side, and determining grid-level energy dynamic parameters; combining the multi-energy flow topology constraint matrix to perform dynamic trend adjustment on the grid-level energy dynamic parameters, and determining a grid-level energy dynamic prediction atlas; finding a best trade-off solution set through a population aggregation and dispersion search algorithm; combining a safe operation boundary to perform constraint solving on the best trade-off solution set, and determining an optimal solution of energy dispatching. The application improves the accuracy and foresight of energy management, accurately traces loss links, and drives the system to continuously evolve in the direction of low carbon and high efficiency.

This invention discloses a method, system, and device for presenting tree information and educational content, applied to a WeChat mini-program. The method includes: setting a root node for each tree species containing immutable basic taxonomic features, and assigning dynamic sub-nodes defined by three dimensions: region, phenological period, and growth environment; collecting user-submitted native tree observation data, extracting tree morphological feature vectors, LBS geographic grid data, user-supplemented fields, and collection identifiers; when user observation data in the same scenario reaches a preset quantity and similarity threshold, updating the average feature value and deviation range to the feature library of the corresponding dynamic sub-node; obtaining user LBS location and seasonal context data, calling the dynamic sub-nodes to obtain scenario-based feature deviation thresholds and dynamically adjusting matching weights, completing secondary precise matching, and outputting search results; and dynamically generating digital twin archives containing phenological change timelines and ideological and political growth values ​​based on empirically verified user observation data belonging to trees.