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62 results about "Geographical distance" patented technology
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Geographical distance is the distance measured along the surface of the earth. The formulae in this article calculate distances between points which are defined by geographical coordinates in terms of latitude and longitude. This distance is an element in solving the second (inverse) geodetic problem.
Traffic accident prediction method fusing multi-source features and adaptive structure
The invention provides a traffic accident prediction method fusing multi-source features and a self-adaptive structure, and the method comprises the steps: extracting spatial features such as a geographic position, traffic flow and interest point distribution, combining the time features such as traffic flow change trend, periodicity and anomaly detection, and the external features such as weather and signal lamp density, and carrying out the prediction of a traffic accident. Node multi-dimensional feature representation is comprehensively constructed, a static adjacency matrix and a dynamic adjacency matrix are respectively constructed, geographic distance and node feature similarity information are fused, a self-adaptive adjacency matrix is generated by utilizing learnable parameters, and road network structure changes are dynamically described. Finally, traffic accidents are modeled and predicted based on a graph convolutional neural network, and accurate identification and early warning of accident risks in a complex traffic environment are realized. According to the method, the modeling capability of the prediction model for nonlinear and strong space-time correlation characteristics of traffic data is effectively improved, the accuracy and robustness of traffic accident prediction are remarkably improved, and the method has wide engineering application prospects and popularization value.
Owner:SHANGHAI UNIV
Short-term wind speed prediction method for multiple offshore wind power plants
ActiveCN121602355AGeneration forecast in ac networkData processing applicationsGeographical distanceAtmospheric sciences
The invention discloses a short-term wind speed prediction method for multiple offshore wind power plants, relates to the technical field of power system intellectualization, and constructs a dynamic graph structure fusing the correlation between geographic distance and wind speed according to the correlation between the geographic position of a target area and the wind speed, namely a multi-wind-plant connected graph, and represents the spatial topological relation of a wind power plant group. A wind power plant group is mapped into a node network by constructing a dynamic graph structure fusing geographic distance and wind speed correlation, spatial dependence intensity between nodes is quantized by using a weighted adjacent matrix, spectral domain convolution operation is carried out by a graph convolution network based on a normalized Laplacian matrix, and the spatial dependence intensity between nodes is quantized by using a normalized Laplacian matrix. Efficient neighborhood feature aggregation is achieved through Chebyshev polynomial approximation, complex spatial association caused by geographic position difference and meteorological condition interaction can be accurately captured, the defect of non-Euclidean spatial relationship modeling in a traditional method is overcome, and the representation capacity of the spatial dependency relationship in the multi-wind-power-plant environment is remarkably improved.
Owner:自然资源部天津海洋中心(自然资源部天津海洋预报台)
Multi-source environment monitoring data fusion analysis and abnormity early warning method based on deep learning
PendingCN121093276AGeneral water supply conservationBiological modelsCosine similarityFeature vector
The invention discloses a multi-source environment monitoring data fusion analysis and abnormity early warning method based on deep learning. According to the method, the spatial attention weight is calculated by fusing the feature similarity and the geographic distance, and the modeling precision of the complex spatial dependency relationship between the environmental monitoring nodes is remarkably improved. Specifically, the model firstly divides a monitoring area into fine grid units, each unit serves as an independent node to participate in calculation, the direction consistency of node feature vectors is quantified through cosine similarity, and meanwhile, a geographic distance attenuation factor is introduced to dynamically adjust weight distribution. The double consideration enables nodes with similar environmental characteristics in adjacent areas to obtain higher attention, and effectively avoids the problem of spatial correlation misjudgment easily occurring in a complex terrain in a traditional fixed adjacent matrix. When local pollution diffusion occurs in a monitoring area, the model can quickly position a propagation path of a core pollution source and surrounding affected nodes, and the space correlation identification efficiency is improved.
Owner:ZHEJIANG SHUREN UNIV
Next Interest Point Recommendation Method Based on Spatiotemporal Power Law Attention
ActiveCN115935065BDigital data information retrievalBiological modelsAccess frequencyGeographical distance
Owner:BEIJING INST OF TECH
Energy storage residual capacity distribution method suitable for multi-scene application
ActiveCN120414617ABatteries circuit arrangementsAc network load balancingCluster algorithmDynamical optimization
The invention discloses an energy storage residual capacity distribution method suitable for multi-scene application, and relates to the technical field of multi-scene optimization control, and the method comprises the steps: generating a dynamic scene cluster through an improved clustering algorithm based on the multi-dimensional features of scene demands, and forming a pre-matching relation library; constructing a space-time joint cost model according to the pre-matching relation library, comprehensively considering dynamic constraints of power deviation, capacity difference and geographic distance, and generating an initial allocation scheme by adopting a hybrid optimization algorithm; an elastic redistribution mechanism is triggered by monitoring the health degree, the residual capacity change rate and the communication state of the energy storage unit in real time; when the energy storage capacity is close to the critical value, the distribution proportion is dynamically reduced according to the priority; when sudden power fluctuation is detected, adjacent energy storage cooperative compensation is started; predicting a capacity change trend by adopting a reinforcement learning model, adjusting an allocation strategy in advance, and outputting a dynamic optimization scheme and an adjustment log; and performing multi-dimensional verification on the dynamic optimization scheme.
Owner:SUZHOU XINYELIAN INTELLIGENT TECHNOLOGY CO LTD
Wind power prediction method based on multi-objective optimization
ActiveCN121886374AGeneration forecast in ac networkSingle network parallel feeding arrangementsData acquisitionGeographical distance
The invention belongs to the technical field of artificial intelligence, and particularly relates to a wind power prediction method based on multi-objective optimization, and the method comprises the steps: wind power data collection and training data set construction, adaptive sliding window and dynamic fluctuation decomposition of wind power time series data, construction of a short-term power prediction model, and short-term power prediction. According to the method, the historical window length and the decomposition scale can be autonomously adjusted according to the inherent fluctuation characteristics of the data, and multi-scale accurate characterization of the non-stationary power sequence is realized; according to the method, a dynamic space-time diagram fusing geographic distance and instantaneous power correlation is constructed, and a diagram attention network combined with trend similarity gating is designed, so that dynamic refined modeling of a space incidence relation is realized; according to fluctuation intensity self-adaptive loss function dynamic balance point prediction precision and interval prediction reliability, synchronously outputting deterministic and probabilistic prediction results; the rated power limit and the ramp rate constraint are embedded into the model in a soft mode, and it is ensured that the prediction result conforms to the actual operation rule of the wind turbine generator.
Owner:CHANGCHUN INST OF TECH
GIS buffer generation method and system based on fuzzy semantics and geographic constraints
ActiveCN120726256ABiological modelsNatural language data processingGeographical distanceSpace mapping
The invention provides a GIS buffer generation method and system based on fuzzy semantics and geographic constraints, and belongs to the technical field of geographic information systems.The method comprises the steps that fuzzy descriptors, membership values and geographic entity types are input into a pre-trained language-space mapping model, and basic distance parameters are obtained; querying a spatial database based on the geographic entity keywords, obtaining geometric features associated with the geographic entities, and calculating feature lengths corresponding to the geometric features; correcting the basic distance parameter based on the intention direction, the membership value, the geographic entity type and the feature length expressed by the fuzzy description word to obtain a geographic distance threshold; generating an initial two-dimensional buffer polygon based on the geographic distance threshold and the spatial geometric features of the geographic entity; and querying a digital elevation model to obtain topographic data based on the initial two-dimensional buffer region polygon, and generating a three-dimensional buffer region curved surface through a gradient sensitive interpolation algorithm. According to the invention, the accuracy of generating the GIS buffer area is improved.
Owner:MUCHENG SURVEYING & MAPPING (BEIJING) CO LTD
Ground surface deformation space-time prediction method combining InSAR and graph neural network
The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and graph neural network combined earth surface deformation space-time prediction method, which is suitable for space-time prediction of earth surface deformation. Based on DS-InSAR, obtaining radar sight line-to-time sequence earth surface deformation of the high-coherence measuring points; calculating the geographic distance between the measuring points and the mutual information between the deformation sequences corresponding to the points; the method comprises the following steps: judging connectivity between high-coherence measuring points according to a geographic distance between the measuring points and mutual information to generate an adjacent matrix, and organizing an original InSAR deformation monitoring result into graph structure data; and inputting the graph structure data into the LSTM-GCN model to predict and obtain the surface deformation of all the high-coherence measuring points in the whole area. The method is high in prediction precision and wide in application range, and can be effectively applied to the fields of space-time prediction of deformation of earth surfaces and buildings (structures) caused by mine closure, underground resource development, natural disasters and the like.
Owner:CHINA UNIV OF MINING & TECH
Distributed photovoltaic ultra-short-term solar irradiance prediction method based on multi-scale spatial-temporal feature fusion
The invention discloses a distributed photovoltaic ultra-short-term solar irradiance prediction method based on multi-scale spatial-temporal feature fusion, and the method comprises the steps: constructing a multi-source spatial adjacency matrix through fusing a geographic distance adjacency matrix, a historical correlation matrix and a dynamic adjacency matrix generated by a graph self-attention network; therefore, the inter-site spatial dependency relationship is dynamically captured. Meanwhile, multi-scale time features are extracted in combination with a convolutional long-short-term memory network, dynamic weighted integration of spatial-temporal features is finally realized through a gating fusion unit, and high-precision prediction of future ultra-short-term irradiance is completed. A verification result on a certain eight distributed photovoltaic station data set shows that the model is remarkably superior to a traditional neural network model under different weather conditions, and particularly, the model shows higher adaptability and prediction precision in sudden change weather and an irradiance peak value interval; and reliable technical support can be provided for distributed photovoltaic power station power prediction and power grid dispatching.
Owner:ZHEJIANG UNIV +1
Coastal wind turbine group generation power multi-scale space-time prediction method
The invention discloses a coastal wind turbine group generation power multi-scale space-time prediction method, which mainly comprises the following steps: carrying out space interpolation and error correction on a target wind turbine position by adopting a numerical weather forecast statistical downscaling technology, generating a high-resolution wind speed prediction sequence covering a short term and a long term, aligning and splicing the predicted wind speed, the field actually-measured wind speed, the environment and the unit operation variables into node dynamic input characteristics; the method comprises the following steps: encoding longitude and latitude and time sequence monitoring data of N fans of a coastal fan group into graph nodes, determining an edge weight according to geographic distance and wake flow coupling, and forming a fan graph network containing static and dynamic characteristics; and inputting the static and dynamic feature sequences into a graph neural network comprising a space attention layer, a time recursion layer and a physical constraint regular term, completing model training, and outputting the generated power of each fan in a plurality of time steps in the future and the total power predicted value of the fan group. The method can provide powerful support for wind power plant operation scheduling, power grid-connected management and new energy consumption.
Owner:UNIV OF CHINESE ACAD OF SCI
Calculation method for simulating urban road network traffic efficiency through spatial partition
The invention relates to the technical field of data processing, in particular to a calculation method for simulating urban road network traffic efficiency by spatial partitions, which comprises the following steps: acquiring population raster data and road network data; converting the population raster data into a two-dimensional matrix and standardizing the two-dimensional matrix; spatial clustering is carried out by adopting a comprehensive distance index fusing geographic distance and population difference, a partition set is generated, a shortest path between point pairs is searched in a road network, the ratio of the length of a spatial linear path to the length of the shortest path is calculated as a length efficiency index, and a comprehensive traffic efficiency index is output. According to the method, population distribution characteristics are fused through a space partitioning technology, diversified travel scenes are covered by Monte Carlo simulation, and the accuracy of urban road network traffic efficiency evaluation is remarkably improved by combining a path length and shape two-dimensional evaluation mechanism. The comprehensive traffic efficiency index directly represents the overall efficiency level of the road network, and a quantitative basis is provided for road network structure optimization.
Owner:LANZHOU JIAOTONG UNIV
Road surface disease intelligent acquisition and classification identification method based on mobile terminal
ActiveCN120495284AImage analysisParticular environment based servicesFeature vectorGeographical distance
The invention discloses a pavement disease intelligent acquisition and classification identification method based on a mobile terminal, and particularly relates to the technical field of pavement disease intelligent identification, synchronous acquisition of continuous image frames and track data is realized through combined driving of camera equipment and a positioning module, and the pavement disease intelligent acquisition and classification identification method based on the mobile terminal is realized by combining a sliding time window and a minimum time difference principle. A dynamic pairing relation between image frames and track points is constructed, a timestamp dislocation alignment anomaly coefficient is introduced, a geographic mapping error accumulation coefficient is constructed, the two types of coefficients are fused to construct a disease space-time foldover risk assessment model, a disease space-time foldover risk assessment index is output, and space redundancy risk measurement of disease identification is realized. Intelligent marking of foldover risk points is dynamically completed, repeated disease points are accurately recognized through geographic distance judgment and image feature vector similarity calculation on the basis of a redundant point fusion mechanism of space consistency and image feature similarity, and track positions of the repeated disease points are dynamically updated through a weighted fusion strategy.
Owner:BEIJING MUNICIPAL BRIDGE MAINTENANCE MANAGEMENT +1
Product full life cycle dynamic supply chain optimization method and management system based on CBAM
The invention relates to the technical field of supply chain management, in particular to a CBAM-based product full-life-cycle dynamic supply chain optimization method and management system, and the method comprises the steps: obtaining materials and numbers required by an outlet, building the association between raw materials and transportation links, collecting production carbon release details, and screening compliance paths to form an operation list. And judging the position overlap ratio to generate a resource path atlas, and outputting a supply chain scheduling scheme according to time consumption and span sorting. The method comprises the following steps: quantifying a geographic distance and a transportation mode in a raw material path, establishing mapping between transportation and a carbon source, identifying a carbon release path by combining an equipment operation section sequence, rejecting non-compliant paths according to target market limitation, associating a screening result with a carrying node space position, and constructing a connection combination according to a position overlap ratio. The path is optimized through time consumption and span sorting, and collaborative optimization of path screening and job scheduling is achieved.
Owner:SHANGHAI CUSTOMS MECHANICAL & ELECTRICAL PROD TESTING TECH CENT
Credit evaluation model construction method and credit evaluation method
The invention provides a credit evaluation model construction method and a credit evaluation method, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring financial index data, geographical location information and time period data of each merchant; calculating the geographic distance between the merchants based on the geographic position information, and determining the period similarity in combination with the fluctuation correlation of the financial indexes in the same time period; the geographic distance and the period similarity are fused to generate comprehensive association strength, and the closer the geographic distance is, the higher the period similarity is and the higher the association strength is; constructing a spatio-temporal data graph according to the comprehensive association strength, and taking the merchant as a node and the association strength as an edge weight; and establishing a dynamic credit evaluation model based on the spatio-temporal data graph. According to the scheme, dynamic influences of regional economic differences and seasonal fluctuations on credit risks are captured in real time, so that the real-time performance and accuracy of credit assessment are remarkably improved, and the problem that a static assessment strategy and a dynamic environment are disjointed in the prior art is effectively solved.
Owner:INDUSTRIAL AND COMMERCIAL BANK OF CHINA
Precipitation prediction map convolution method based on graph reconstruction and spatial self-attention
PendingCN120911250AWeather condition predictionBiological modelsEarly warning systemCorrelation coefficient
The invention relates to a rainfall prediction map convolution method based on graph reconstruction and spatial self-attention, and belongs to the technical field of crossing of artificial intelligence and urban meteorological early warning technologies. The method and the device are used for solving the problems of excessive smoothness and insufficient spatial dependency capture caused by dependence on a geographic adjacency relation in precipitation prediction of a traditional graph convolutional network. A sparse graph structure reflecting real meteorological association is constructed by calculating a Pearson's correlation coefficient of Beidou multi-base-station meteorological time series data, and a traditional geographic adjacency matrix is replaced; designing a dynamic space self-attention mechanism, fusing the static association graph and a dynamic attention weight, and realizing adaptive aggregation of long-range dependence between nodes; and in combination with multilevel feature propagation of graph convolution and an attention matrix, the modeling capability of a heterogeneous meteorological spatial mode is improved. According to the method, the accuracy of short temporary rainfall prediction is remarkably improved, the method is particularly suitable for association mining of base stations which are far in geographic distance but similar in meteorological feature, and a high-robustness spatial feature extraction scheme is provided for a meteorological early warning system.
Owner:BEIJING INST OF TECH
Gps-based visual positioning method, system, computer and storage medium
The application provides a GPS-based visual positioning method, system, computer and storage medium, which comprises the following steps: acquiring the GPS coordinates of a query image, and screening reference images with close geographical positions from a database based on the GPS coordinates; screening reference images with a geographical distance less than or equal to a preset radius as a candidate set; determining several similar images based on k-nearest neighbor search; clustering according to the co-visibility relationship of each similar image to generate a plurality of locations corresponding to the candidate image, each location containing commonly observed 3D points; performing 2D-3D matching on each location, and estimating and outputting a six-degree-of-freedom camera pose through a perspective n-point algorithm and a random sample consensus algorithm to realize visual positioning. The GPS coordinates are used to dynamically screen reference images, the search time is reduced, the quick response requirement of real-time scene application is met, and the cross-environment high robustness is guaranteed.
Owner:JIANGXI QIUSHI INST OF ADVANCED STUDIES
A Hydrological Trend Prediction Method Based on Big Data Analysis
This invention proposes a hydrological trend prediction method based on big data analysis. It constructs an initial graph structure with monitoring stations as nodes and geographical distance and water system connectivity as edge weights. A message passing mechanism is used to iteratively aggregate node features, capturing the spatial interaction of hydrological variables between stations. Based on rainfall changes, the water system connectivity parameters and edge weights are dynamically adjusted to generate an adaptive graph structure. Combining graph neural networks, dynamic Bayesian networks, and spatiotemporal co-Kriging interpolation algorithms, hourly-level hydrological dynamic transmission features are extracted to form a spatiotemporally coupled prediction model. A prediction deviation threshold triggers model parameter optimization and message passing mechanism adjustments, achieving an adaptive prediction process of "dynamic modeling - feature fusion - closed-loop optimization." This method can accurately characterize the spatiotemporal correlation of hydrological elements, improve the analytical accuracy of complex hydrological processes, and enhance adaptability to sudden hydrological events and changes in the watershed environment.
Owner:广东省水文局江门水文分局
Urban population OD flow prediction method and related equipment
The invention relates to the technical field of crowd flow prediction, discloses a crowd OD flow prediction method and related equipment, and effectively overcomes the defects in fine-grained city internal OD flow prediction in the prior art by constructing an urban crowd flow prediction model fusing multi-dimensional key factors. Spatial correlation among geographic units is captured by means of a spatial adjacency encoder, regional function complementarity and geographic distance characteristics are mined through a land utilization complementation encoder, traffic reachability information is integrated by means of a traffic connection encoder, and a characteristic pattern cooperatively output by a multi-dimensional encoder provides comprehensive support for prediction. According to the design, the comprehensive influence of spatial positions, land utilization and traffic conditions on urban travel is fully considered, the defects that a traditional model ignores spatial dependence and an existing machine learning and deep learning model is insufficient in multi-factor integration are overcome, and the prediction accuracy and the fitting degree are remarkably improved.
Owner:SHAANXI NORMAL UNIV
Distributed charging pile group dynamic load balancing and power optimization allocation system and method
The present application relates to electric vehicle charging scheduling technical field, especially to distributed charging pile group dynamic load balancing and power optimization allocation system and method, including: cloud coordinator obtains the space correlation data between piles containing geographical distance and traffic similarity and fuses with historical and weather data, inputs into the spatiotemporal graph neural network prediction model, outputs probability distribution prediction value and prediction confidence interval and issues, edge control node constructs multi-objective optimization model based on prediction uncertainty, generates dynamic allocation table and backup scheme under capacity constraint to minimize charging time and maximize renewable energy consumption as the target, issues allocation table to execute power allocation and obtains actual power feedback, when the actual power exceeds the confidence interval range, switch backup scheme to preferentially reduce flexibility load. The present application effectively improves the deficiency that single-point prediction lacks defense, realizes reliable global dynamic load balancing under the premise of guaranteeing the safe operation of power grid transformer.
Owner:STATE GRID HENAN ELECTRIC POWER COMPANY ANYANG POWER SUPPLY +2
Global data-free area visibility forecasting method considering geographical distance
The invention discloses a global no-data area visibility forecasting method considering geographical distance, and relates to the technical field of visibility forecasting, and the global no-data area visibility forecasting method considering geographical distance mainly comprises the steps: carrying out the preprocessing of meteorological grid data and visibility monitoring data, and obtaining a forecasting data set; the constructed artificial intelligence model comprises an encoder and a decoder; and training and evaluating the artificial intelligence model by using the forecast data set to obtain a basic artificial intelligence model, and predicting the visibility of the global data-free area to obtain a prediction result. By implementing the global data-free area visibility forecasting method considering the geographic distance, the seasonal forecasting accuracy of the visibility of the global station-free area can be improved.
Owner:LANZHOU UNIV
Short-term power load prediction method and system based on improved DSSFA-SAC-ConvLSTM
The invention discloses a short-term power load prediction method and system based on improved DSSFA-SAC-ConvLSTM, and the method comprises the steps: firstly collecting and preprocessing regional historical power load data, then employing DSSFA to analyze and extract features, constructing a feature matrix, generating a distance adjacency matrix and a dynamic adjacency matrix based on a geographic distance and DSSFA features, fusing the distance adjacency matrix and the dynamic adjacency matrix into an autocorrelation matrix, and carrying out the prediction of a short-term power load based on the autocorrelation matrix. Then splicing the feature matrix and the autocorrelation matrix, inputting the spliced feature matrix and the autocorrelation matrix into a ConvLSTM model for training, adjusting a learning rate by adopting Warmup and improved OneCycleLR, and finally mapping an output feature into a power load prediction value; according to the method, key features are extracted through DSSFA, the data dimension is effectively reduced, important information is reserved, a dynamic adjacency matrix is generated in combination with a geographic distance and a self-attention mechanism, spatial information is fully considered, the accuracy of multi-region prediction is improved, the ConvLSTM learning rate is optimized by adopting a Warmup and an improved OneCycleLR strategy, the model training effect is enhanced, and the prediction efficiency is improved. And the accuracy of power load prediction is further improved, so that the power load prediction method has more excellent performance in a complex scene.
Owner:ECONOMIC & TECH RES INST OF HUBEI ELECTRIC POWER COMPANY SGCC
Next interesting point of interest recommendation system based on spatiotemporal power law attention
ActiveCN115952355BDigital data information retrievalBiological modelsGeographical distanceArtificial intelligence
The application discloses a next interest point recommendation system based on space-time power law attention, and relates to the field of recommendation system. t The application comprises a short-term preference module: a recurrent neural network is used to capture the short-term preference h t of a user check-in sequence; a long-term preference module: a time interval power law distribution, a geographical distance power law distribution and space-time power law attention are calculated; the short-term preference h t is combined with the long-term preference and user representation p u to predict a next interest point through a neural network and make a recommendation. The system uses the power law attenuation properties of the time interval and geographical distance between each check-in to model the long-term preference of a user through space-time power law attention, and considers the space-time relationship between non-continuous check-ins in modeling, thereby improving the effect of next interest point recommendation.
Owner:BEIJING INST OF ELECTRONICS SYST ENG +1
Training apparatus, training method, data matching apparatus, data matching method, and storage medium
PCT designated stageWO2026048594A1Machine learningNeural learning methodsData matchingPositive sample
A training apparatus performs: acquiring an anchor sample, a positive sample, and a plurality of negative samples, extracting first hard-negative samples from the plurality of the first negative samples, selecting, as a selected first negative sample, one of the first hard-negative samples based on a geographical distance between the place represented by the anchor sample and the place represented by each of the first hard-negative samples; and updating a first feature extracting model and a second feature extracting model based on the feature value of the anchor sample, a feature value of the first positive sample, and a feature value of the selected first negative sample. The feature value of the data of the first type and the feature value of the data of the second type is used to determine whether the data of the first type matches the data of the second type.
Owner:NEC CORP
Image stitching method and device based on linear array camera, equipment and storage medium
ActiveCN116362965BImage analysisInternal combustion piston enginesPattern recognitionComputer graphics (images)
This invention discloses an image stitching method, apparatus, device, and storage medium based on a line scan camera. The method includes: obtaining the starting and ending geographic coordinates corresponding to an initial image sequence, and determining the geographic distance corresponding to the initial image sequence; stitching together first-type images in the initial image sequence to obtain a second-type image corresponding to the initial image sequence; obtaining the image length of the second-type image, and adjusting the second-type image according to the geographic distance and the image length to obtain a target image corresponding to the initial image sequence. This invention determines the geographic distance corresponding to the initial image sequence based on the starting and ending geographic coordinates in the initial image sequence, and adjusts the second-type image obtained by stitching together each first-type image in the initial image sequence according to the geographic distance, so that images of the same resolution represent the same length information, thereby enabling the stitching of images of the same resolution.
Owner:成都圭目机器人有限公司
A wind power prediction method based on multi-objective optimization
ActiveCN121886374BGeneration forecast in ac networkSingle network parallel feeding arrangementsData setAlgorithm
The application belongs to the technical field of artificial intelligence, and particularly relates to a wind power prediction method based on multi-objective optimization, which comprises wind power data acquisition and training data set construction, adaptive sliding window and dynamic fluctuation decomposition of wind power time series data, short-term power prediction model construction and short-term power prediction. The method can autonomously adjust the historical window length and decomposition scale according to the inherent fluctuation characteristics of the data, realize the multi-scale accurate representation of the non-stationary power sequence, construct a dynamic space-time graph by fusing the geographical distance and the correlation of instantaneous power, design a graph attention network combined with the trend similarity gate, realize the dynamic fine modeling of the spatial correlation, dynamically balance the prediction accuracy and interval prediction reliability through the fluctuation intensity adaptive loss function, synchronously output the deterministic and probabilistic prediction results, and embed the rated power limit and the climbing rate constraint in the model in a soft manner, so that the prediction results conform to the actual operation law of the wind turbine.
Owner:CHANGCHUN INST OF TECH
A graph convolutional neural network water level prediction method based on geographical optimal similarity
This invention discloses a graph convolutional neural network (GCN) method for water level prediction based on geographic optimal similarity. The method includes: acquiring hydrological monitoring data from multiple stations and related explanatory variables, and preprocessing the data; calculating the geographic optimal similarity (GOS) between stations based on the historical statistical characteristics of the explanatory variables of each station, and constructing a dynamic adjacency matrix reflecting nonlinear hydrological associations; using this adjacency matrix as the graph structure of a graph convolutional neural network (GCN) to construct a GOS-GCN model for water level prediction; training and testing the GOS-GCN model using real hydrological data, and evaluating the prediction results. This invention effectively solves the limitations of relying solely on geographic distance for mapping, and significantly improves the prediction accuracy and robustness of the model under complex spatiotemporal conditions.
Owner:JIANGXI ACAD OF ECO-ENVIRONMENTAL SCI & PLANNING +1
Air quality prediction method based on deep learning model of latent source contribution analysis
This invention discloses an air quality prediction method based on a deep learning model using potential source contribution analysis. Applied to the field of air quality prediction technology, it addresses the problem that existing technologies only consider the geographical distance of the research object, lacking a deeper exploration of its spatial diffusion impact. This invention utilizes potential source contribution analysis based on airflow trajectories to construct a spatial impact map to represent the spatial correlation between cities. Then, by using a graph convolution algorithm, it extracts effective spatial features from the spatial impact map in conjunction with the pollution status of each city. Furthermore, it employs a long short-term memory model to learn the temporal characteristics of past air pollutant concentration data, generating prediction results. First, the results of potential source contribution analysis are applied to a graph convolutional network, and then combined with the long short-term memory model to predict urban air pollution data. The method of this invention can accurately predict urban air pollutants on both spatiotemporal scales.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
A short-term wind speed prediction method for offshore multi-wind farm
ActiveCN121602355BGeneration forecast in ac networkData processing applicationsElectric power systemPredictive methods
The application discloses a kind of short-term wind speed prediction methods for offshore multi-wind farm, it is related to electric power system intelligentization technical field, according to the geographical location of target area and wind speed correlation, construct the dynamic graph structure of fusion geographical distance and wind speed correlation, i.e. The application maps the wind farm group into a node network by constructing the dynamic graph structure of fusion geographical distance and wind speed correlation, quantifies the spatial dependence intensity between nodes using a weighted adjacency matrix, performs spectral domain convolution operation based on a normalized Laplacian matrix using a graph convolution network, and realizes efficient neighborhood feature aggregation through Chebyshev polynomial approximation. This can accurately capture complex spatial correlations caused by geographical location differences and meteorological condition interactions, overcome the shortcomings of traditional methods in modeling non-Euclidean spatial relationships, and significantly improve the representation ability of spatial dependence relationships in a multi-wind farm environment.
Owner:自然资源部天津海洋中心(自然资源部天津海洋预报台)
85th vehicle speed survey method based on deep learning
PendingCN120451736AImage analysisCharacter and pattern recognitionPattern recognitionUncrewed vehicle
The invention relates to a 85th vehicle speed survey method based on deep learning, and belongs to the field of vehicle tracking. The method comprises the following steps: realizing multi-target vehicle detection by using a deep learning algorithm, identifying and classifying vehicles, and continuously updating the position and state information of the vehicles; mapping a camera picture to geographic coordinates, calculating coordinates of four angular points in a shooting range according to parameters of the unmanned aerial vehicle, and updating a corresponding relation between the coordinates; determining the position of the vehicle in a camera coordinate system through a deep learning model, and calculating the geographic coordinates of the vehicle through pixel coordinate normalization; calculating the geographical distance of driving in one second by utilizing a vehicle tracking result, connecting speed values at all moments to obtain a speed-time curve, calculating a distance-time curve by combining a road model created by track information, and calculating the 85th vehicle speed; the current position of the unmanned aerial vehicle can be corrected by using continuous images of continuous frames. Accurate and reliable data support is provided for applications such as vehicle tracking.
Owner:FUZHOU UNIV +1
A method and system for spatiotemporal prediction of charging station load based on multi-source matrix fusion
PendingCN122088760AImprove adaptabilityHigh precisionForecastingBiological modelsAdaptive learningLoad forecasting
This invention discloses a method and system for spatiotemporal prediction of charging station load based on multi-source matrix fusion. The method includes: collecting historical load data and geographical coordinates of charging stations, performing standardization and sample partitioning; constructing in parallel a prior adjacency matrix based on geographical distance, a temporal similarity adjacency matrix based on dynamic time warping distance, and a dynamic adaptive adjacency matrix based on learnable node embedding; adaptively weighting and fusing the three matrices through trainable scalar parameters to generate a fused adjacency matrix; and constructing and utilizing a spatiotemporal graph convolutional network for load prediction based on the fused adjacency matrix as the spatial relationship basis. This invention comprehensively improves the accuracy and reliability of spatiotemporal prediction of charging station group load by fusing complementary spatial relationships from multiple sources, enhancing the robustness of temporal similarity measurement through dynamic time warping, and combining data-driven adaptive learning.
Owner:INFORMATION & COMM CO OF STATE GRID SHAANXI ELECTRIC POWER CO LTD