50 results about "Gps trajectory" patented technology

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention discloses a truck trip chain identification method based on trajectory data, which comprises the following steps: S1, acquiring GPS trajectory data of a truck, and preprocessing the GPS trajectory data, including data cleaning, noise removal and trajectory segmentation; s2, by setting a speed threshold value and a time threshold value, the stop point of the truck is recognized; s3, spatial matching is carried out on the stay point and road network data, and the accurate position of the stay point is determined; s4, segmenting the track of the truck based on the time sequence and the space distribution of the stop points to form a plurality of travel segments; s5, analyzing the driving characteristics and the stop characteristics of each travel section, and removing stop points related to non-freight; and S6, identifying key staying points related to loading and unloading activities, and connecting the key staying points according to a time sequence to form a truck travel chain. According to the method, the actual running path and the logistics movable chain of the truck can be clearly restored, and high-precision technical support is provided for freight path analysis and industrial chain space structure recognition.

The invention discloses a method for calculating road network traffic operation carbon emission by using vehicle trajectory data. The method comprises the following steps: firstly, segmenting vehicle GPS trajectory data according to timestamps and serial numbers to obtain a trajectory subset of each vehicle; determining a vehicle stop interval based on the minimum instantaneous speed threshold value, and dividing the trajectory data into driving sections to obtain a driving trajectory; constructing an urban road network model, calculating a spatial distance between a track and a road section, screening candidate road sections according to a shortest distance threshold value, obtaining a matched road section in combination with a minimum difference value of an azimuth angle, mapping track points to the road section, and obtaining re-expanded driving section track data; according to the timestamps and the road section serial numbers, segmenting according to the road section serial numbers to obtain each road section track subset; idle speed and normal driving intervals of a road section are identified through spatial-temporal clustering, a MOVES model database is introduced to perform classified calculation on carbon emissions of the corresponding intervals, the carbon emissions are summarized to obtain road section emissions, and road network traffic carbon emissions are summarized to obtain road network traffic carbon emissions. According to the method, macrotopography and microscopic real-time working condition data are fused, and a road section carbon emission measuring and calculating method is perfected.

The invention is suitable for the technical field of intelligent traffic systems, and provides a trajectory anomaly detection method based on structure enhancement and comparison pre-training. The method comprises the following steps: firstly, carrying out road network matching on an original GPS track, extracting attributes such as a road type and a road length, coding the attributes into a structure lexical element sequence, and constructing a structured Prompt text; then, through structure-semantic comparison pre-training, enabling the model to learn an alignment relationship between path structure features and text description thereof, and obtaining stable shared representation; in the fine tuning and reasoning stage, the aligned path structure vectors are used for carrying out structure guiding fusion on large language model coding representation, track behavior categories are output through a classifier, and meanwhile key evidence fragments are given according to attention weights so as to enhance interpretability. Compared with an existing method, the method has the advantages that road structure information is explicitly fused, cross-modal alignment is achieved, and the accuracy and interpretability of track anomaly detection and the generalization ability in a city-wide cross-regional scene are remarkably improved.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

This application relates to an offline map matching method, apparatus, computer device, and storage medium. The method includes: starting from the starting position of the complete GPS trajectory to be matched, selecting a sliding window according to a set sliding window length, and searching for the shortest path set for each sliding window in the road network based on candidate starting and ending points; matching the local GPS trajectory corresponding to each shortest path to the corresponding road segment in the road network to generate the shortest matching path without backtracking, calculating the score of each matching path, and retaining the top k matching paths with the highest scores; performing local non-shortest path travel detection on the top k matching paths, and using a split matching algorithm to correct the travel trajectories of existing local non-shortest paths. The embodiments of this application effectively enhance the robustness of road network matching for local non-shortest path travel, improving the overall efficiency and trajectory matching accuracy of offline map matching.

The present application belongs to the intelligent traffic technical field, specifically is a kind of trajectory restoration system based on urban traffic road network.The system of the present application includes traffic trajectory data acquisition module, GPS trajectory feature extraction module, GPS trajectory restoration learning module;Wherein traffic trajectory data acquisition module collects the latitude and longitude data of the vehicle driving on urban road in driving process, GPS trajectory feature extraction module extracts space-time feature to the low sampling rate trajectory point input neural network, and GPS trajectory restoration learning module inputs the output of last stage into new neural network model to predict the latitude and longitude information of missing sampling point in input low sampling rate trajectory.The present application can greatly improve the utilization of low sampling rate trajectory data;At the same time, combined with the topological structure of urban road network, the restored GPS trajectory is more in line with the real trajectory, effectively improves the utilization efficiency of trajectory data.

This invention provides a target positioning correction method and system applicable to vehicle-mounted inspections. The method includes: acquiring the installation error angle of a GPS device; calculating the north direction angle using the installation error angle; a camera on the inspection vehicle tracking multiple inspection targets separately using a target tracking algorithm to obtain multiple sets of raw GPS data for each target; calculating multiple sets of target intersection coordinates for each target using the multiple sets of raw GPS data, the north direction angle, and the GPS trajectory data of the inspection vehicle; and clustering the multiple sets of target intersection coordinates for each target to calculate the center point position of each target. This invention considers multiple factors affecting positioning accuracy and achieves accurate positioning of the inspection targets through installation correction, target tracking, and clustering algorithms, effectively improving target positioning accuracy to 0-3m.

The invention provides an intersection vehicle queuing prediction method and system, and the method comprises the steps: determining the moment when a vehicle leaving a parking line enters a target road section at the end of a green light in a current period through combining a vehicle GPS track with the data of a road fixed detector, thereby determining the number of initial remaining vehicles, and laying a precise data foundation for subsequent prediction; the number of future driving-in vehicles is predicted based on a historical traffic flow sequence, the number of future driving-out vehicles is estimated in combination with an intersection traffic signal timing scheme, the number of remaining vehicles in a road section at the future moment is calculated, and finally the future queuing length is predicted according to the number, so that the prediction precision is improved; and the method has important practical application value for improving urban traffic operation efficiency and reducing vehicle delay.

The application relates to the technical field of space-time trajectory data anomaly detection, in particular to a trajectory anomaly route detection method and system based on self-supervised trajectory feature learning. The method comprises the following steps: preprocessing trajectory data obtained; constructing trajectory double-view comparative representation learning based on a double-view synchronous mask strategy; encoding time dynamics based on a space-time fusion mechanism and fusing with space features; learning essential features from fused space-time features to reconstruct trajectories; performing error inspection based on reconstructed trajectories, and judging abnormal trajectories through reconstruction errors. The application proposes a brand-new trajectory anomaly detection model. The model fuses GPS trajectories and grid-based trajectory features, enriches trajectory representation, and designs a double-view synchronous mask mechanism, so that the model can simultaneously perceive local disturbances in the space and time dimensions during the training stage, thereby improving the sensitivity to local anomalies.

The invention discloses a space-time perception road network path generation method and system, an electronic device and a storage medium. The method is characterized by comprising the steps of obtaining device GPS trajectory data and city road network information, matching a trajectory to a road network based on a path matching algorithm to obtain discrete path data and the like. The system comprises a path matching module, a spatial diffusion module, a time diffusion module, a reverse diffusion module, a decoding module and a constraint module. The electronic device comprises a processor and a memory. Computer readable instructions are stored in the storage medium. Compared with the prior art, the method has the advantages that a high-quality space-time path conforming to a road network structure and a traffic law is generated, the accuracy and diversity of path generation are improved, and the method can be applied to traffic simulation and data enhancement on the premise of protecting user privacy; the problems that the modeling capacity of sparse data or low-frequency paths is limited and accurate and diversified path information is difficult to generate are effectively solved, and the application prospect is good.

A multi-view dynamic spatiotemporal semantic embedding method for predicting transportation time of open-pit mine trucks is presented, belonging to the field of open-pit mine transportation. The method involves preprocessing the collected raw GPS trajectory data of open-pit mine trucks to form a gridded trajectory sequence; extracting the velocity and structural features of the gridded trajectory sequence through a velocity feature extraction module and a grid structure feature extraction module; embedding the semantic information features through a semantic information embedding module; and then extracting the contextual spatiotemporal embedding of the gridded trajectory sequence using a contextual spatiotemporal embedding extraction module to train the model. The prediction results are obtained by inputting the start and end points to be predicted into the trained prediction model. Advantages: The method fully extracts the dynamic spatiotemporal information of the trajectory data and utilizes velocity and semantic information, improving the accuracy of time prediction, which is beneficial for the scheduling of open-pit mine trucks and improves transportation efficiency.

The invention discloses a wild animal trajectory tracking method and system based on trajectory perception and motion correction, and relates to the technical field of wild animal monitoring and computer vision, and the method comprises the steps: carrying out the preprocessing of the collected GPS trajectory data of corresponding wild animals; mapping the preprocessed GPS trajectory data to a high-dimensional feature space by using a feature encoder; converting the feature coding vector into a representation sequence for capturing time sequence dependence and context information through a constructed track encoder; inputting the representation sequence into the constructed trajectory sensing network for processing to obtain scores of candidate positions in a preset candidate list of a next time step; performing real-time track correction on the candidate position with the highest score in the preset candidate list, and taking the corrected candidate position as the track position of the wild animal in the next time step; sensor noise and environmental interference are effectively suppressed, and the precision and robustness of wild animal trajectory tracking are improved.

The application discloses a GPS static drift detection method and system based on trajectory morphology analysis, and belongs to the field of satellite navigation data processing; the method comprises the following steps: acquiring an original GPS trajectory point sequence, and performing plane projection conversion on coordinates to obtain plane coordinate trajectory data; a fixed length sliding window is used to extract local trajectory morphology features, and the confidence of the window to normal driving and static drift is respectively evaluated, normalized state probability is output, and preliminary judgment is performed according to a preset decision threshold to obtain a window level state label; based on the window level state label, context-driven iterative timing smoothing and decision mechanism are used to eliminate state jitter and determine a final window state label; the final window state label is mapped back to the original GPS trajectory point, and is aggregated into an initial trajectory segment to obtain a driving segment or a drift segment with a preliminary state label; and heuristic review and correction are performed on the initial trajectory segment to output a final state labeling result.

The invention discloses a geographic road network topology consistency self-checking and repairing method based on a multi-mode large language model. The method comprises the following steps: obtaining geographic road network vector data, a high-resolution satellite remote sensing image, a vehicle GPS track sequence and a street view image of a key position of an area to be detected; extracting each modal feature by using a multi-modal encoder, and projecting the modal features to a unified language marking space through a space projection operator to realize semantic alignment; the aligned multi-modal features are input into a multi-modal large language model for self-check reasoning, and a diagnosis report containing road network topology errors is output; converting the diagnosis report into a solution diagram by using the model, and calling a code generation module to automatically write a repair code script; and executing the repair script to modify the road network data, feeding back the modified data to the model for closed-loop verification of anti-factual reasoning and virtual traversal semantic verification, and updating the production library after verification is passed. According to the invention, the accuracy of road network topology automatic repair is improved.

The present application relates to the field of positioning, and provides a positioning data processing method, device and equipment, the method comprising: synchronously collecting radar target data and GPS trajectory data, selecting a target data sequence from the radar target data that coincides in time with the GPS trajectory data; selecting a time-continuous target coordinate point sequence from target coordinate points converted from the GPS trajectory data; determining a first coordinate sequence corresponding to the target coordinate points from the target data sequence, determining a coordinate pair sequence based on the target coordinate point sequence and the first coordinate sequence; determining a coordinate conversion parameter based on the coordinate pair sequence, and converting radar coordinates to be processed into GPS coordinates through the coordinate conversion parameter. The present application realizes automatic conversion between radar coordinates and GPS coordinates, avoids tedious manual operation, and improves coordinate conversion efficiency and accuracy.

The application discloses a task adaptive trajectory representation learning method, and relates to the technical field of traffic big data analysis, and comprises the following steps: acquiring GPS trajectories and corresponding road network data, and obtaining road section trajectories through map matching; constructing an alignment matrix based on the GPS trajectories and the road section trajectories, inputting the two types of trajectories into pre-trained GPS trajectory encoders and road section trajectory encoders respectively, and obtaining GPS trajectory embedding sequences and road section trajectory embedding sequences; constructing a task prompt vector based on a target downstream task, taking the task prompt vector as task condition information, fusing the two types of embedding sequences through a hierarchical fusion module, and obtaining task-specific fusion representations; finally, inputting the fusion representations into a prediction module, outputting downstream task prediction results, and updating parameters based on task loss. The application realizes adaptive fusion of multi-modal trajectory features under different downstream task requirements, and improves the task pertinence and adaptation ability of trajectory representation.

The invention discloses a rural public space collaboration evaluation method and system based on continuous track electroencephalogram, and relates to the technical field of human settlement environment space quality measurement, and the method comprises the steps: receiving related data of a rural public space, carrying out the preprocessing of the related data of the rural public space, and obtaining the processed related data of the rural public space, the related data of the rural public space comprises walking sightseeing real-time electroencephalogram data, continuous GPS track data and environment basic geographic space data; based on the processed rural public space related data, extracting a walking brain perception cooperation index, inputting the walking brain perception cooperation index into a pre-established Gaussian process regression model based on a perception cooperation attenuation kernel function, and outputting to obtain a distribution result of the rural public space cooperation degree; and obtaining hierarchical vector transfer data and weight data of the environmental elements, projecting the hierarchical vector transfer data and the weight data of the environmental elements to the rural public space grid units, and performing superposition in combination with the distribution result of the rural public space collaboration degree to obtain a rural public space environmental element weighted collaboration degree distribution result. And clustering to obtain a village advantage collaboration space and a disadvantaged collaboration space.

The embodiment of the invention discloses a trajectory repair model training method, a trajectory repair method and related equipment, and relates to the technical field of trajectory repair, and the trajectory repair model training method comprises the steps: obtaining multiple pieces of target GPS trajectory data in a target region; converting each piece of target GPS trajectory data in the plurality of pieces of target GPS trajectory data into an S2 grid index sequence to obtain a plurality of S2 grid index sequences; constructing a training sample set based on the plurality of S2 grid index sequences; each training sample in the training sample set is formed by splicing two S2 grid index sequences, the two S2 grid index sequences are connected through a segmentation mark, and at least one S2 grid index of at least one S2 grid index sequence is replaced by a mask mark; training the initial trajectory repair model by using the training sample set to obtain a trained trajectory repair model; the initial track repair model is a neural network model based on a Transform encoder.

This invention discloses an automatic cropping and correction method and system for airborne hyperspectral data processing. The automatic flight path cropping technology uses algorithmic analysis of GPS trajectory data, combined with information such as latitude, longitude, heading, and altitude, to extract and crop effective flight paths. On the one hand, it can remove redundant spectral data, significantly reduce the amount of subsequent calculations, and significantly improve the data processing speed, solving the problem of low efficiency in traditional manual cropping. On the other hand, by accurately extracting effective flight paths, it avoids the omission of effective data or redundant residues caused by manual operation, providing high-quality basic data for subsequent correction processing and ensuring the accuracy of overall data processing.

The application provides a kind of fusion surveying and mapping and dynamic modeling housing construction engineering construction optimization system, comprising: dynamic surveying and mapping module: obtain point cloud data and position data, collect the environmental data of each scanning area;Structure analysis module: determine the geometric deviation of completed structure, determine the concrete early hardening risk area in combination with environmental data, and then determine the maintenance priority of each scanning area and the structure acceptance report;Instruction generation module: determine the GPS trajectory of equipment, and then predict the time when concrete tank truck arrives each scanning area, generate sprinkler route, determine vehicle scheduling instruction set;Cloud chart construction module: generate three-dimensional progress deviation cloud chart based on structure acceptance report and preset BIM model;Signal generation module: generate early warning signal based on vehicle scheduling instruction set, three-dimensional progress deviation cloud chart and preset early warning strategy. Effectively improve the accuracy of construction quality, progress and resource scheduling, and optimize the construction management process.

According to the multi-view space-time complementary trajectory prediction method, a systematic multi-view trajectory modeling mechanism is constructed, trajectory features are comprehensively modeled from multiple aspects of topology, semantics, motion behaviors and the like, and other view information is uniformly aligned by using a GPS trajectory, so that the trajectory prediction accuracy is improved. The efficient fusion modeling of the multi-source heterogeneous trajectory data is realized, the trajectory prediction precision and the model robustness are improved, and the problems that the prediction precision needs to be improved due to the single view angle of the trajectory information and the multi-source trajectory data are difficult to effectively fuse due to the strong isomerism are solved.

The invention relates to a comprehensive rod patrol business digitalization and patrol coverage rate algorithm, which comprises the following specific processes: (1) data acquisition and preprocessing: specifically including vehicle GPS track data and comprehensive rod GPS point location data; and (2) a dynamic elimination mechanism matching algorithm. And (3) a coverage rate calculation formula. The method is high in real-time performance, data are collected and calculated per second, coverage rate data can be dynamically updated, the coverage progress can be mastered in real time in the inspection process, and batch processing after the event is not needed. And through a dynamic elimination mechanism of'covering, namely removal ', the number of rod pieces to be calculated is continuously reduced along with routing inspection, repeated calculation is avoided, and the computing power load of the server is remarkably reduced. A coverage judgment standard is flexibly configured through a'statistical accuracy 'variable, the accuracy requirements of different inspection scenes are met, and the reasonability of coverage rate calculation is improved.

The application discloses a kind of road network topology updating method based on GPS data, comprising: S1. the GPS trajectory data of vehicle is collected;S2. all road sections of GPS trajectory data are traversed, and each road section in all road sections is processed as follows: the road section in GPS trajectory data is regarded as target road section ID_re, from GPS trajectory data, the road section sequence (ID_1, ID_re, ID_2) including target road section ID_re is extracted;The topology relationship of the road section sequence including target road section ID_re is judged, and the update of road network topology relationship is carried out according to the judgment result;S3. repeat step S2 until road network topology relationship is no longer updated.The application can accurately update road network topology information in real time, and provides technical support for road traffic statistics, road network simulation, the construction of comprehensive traffic model.

This invention relates to the field of traffic flow prediction technology, specifically a method for predicting freight vehicle traffic flow based on GPS trajectory data. The method includes: acquiring GPS trajectory data of freight vehicles within a target area and preprocessing it; performing unsupervised clustering to form a regional traffic flow time series; constructing a regional transition matrix and obtaining a regional transition map representing the functional connectivity between regions based on the regional transition matrix; performing time series decomposition on the regional traffic flow time series; performing input embedding, temporal embedding, and spatial embedding on the original sequence branch and the low-frequency trend branch respectively, and inputting the embedding results into a spatiotemporal coding network to extract temporal and spatial dependency features; constructing a freight vehicle traffic flow prediction model to obtain the freight vehicle traffic flow prediction result; regularizing the spatial embedding using the regional transition map, and training the freight vehicle traffic flow prediction model based on real traffic flow data to achieve freight vehicle traffic flow prediction.

The invention discloses a GPS incomplete track completion method and device based on mask language model post-training, a medium and equipment, and the method comprises the steps: carrying out the token processing of each GPS track point in a historical GPS track data set, and obtaining a token track sequence; performing trajectory data adaptive fine tuning on the pre-trained MLM model based on the tokenized trajectory sequence to obtain a fine-tuned MLM model; generating a candidate token based on the model; performing spatial constraint filtering processing on the candidate tokens, and reserving the candidate tokens which conform to the physical motion law and are free of circulation; performing multi-point complementation on the candidate tokens subjected to spatial constraint filtering by adopting a bidirectional beam search algorithm to obtain a complete token sequence; according to the invention, the method can carry out the complementation only based on the historical trajectory data, and avoids the error caused by the incomplete or outdated road network. The method also comprises the steps: carrying out the token removal processing, and converting the hexagonal unit corresponding to each token into a GPS point, and obtaining the complemented dense GPS trajectory, and can carry out the complementation only based on the historical trajectory data, thereby avoiding the error caused by the incomplete or outdated road network.

The application provides a kind of urban road congestion evaluation method considering macro-micro data coupling, comprising the following steps: S1: obtaining GIS geographic information data, modeling traffic network using complex network theory;S2: preprocessing vehicle GPS trajectory data, extracting speed information of each road section, obtaining traffic state index;S3: obtaining percolation threshold according to the speed information of each road section;S4: obtaining the running state threshold of each road section according to the percolation threshold;S5: obtaining the running state index according to the traffic state index, obtaining the system congestion index SCI in combination with the running state threshold.The application provides a road congestion evaluation method coupling macro and micro, aiming at the problem of urban road congestion evaluation, avoids the type and quality difference of detector brought by introducing multi-source data, cost benefit and the uncertainty of data real-time, and solves the problem of losing detailed information of specific road section caused by describing traffic condition from macro perspective only.

The invention discloses a crowd-source GPS track data aggregation method, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining crowd-source GPS track data, the crowd-source GPS track data comprising a plurality of pieces of GPS track data; track preprocessing is carried out on each piece of GPS track data of the public source GPS track data, a preprocessed track data set is generated, and each piece of preprocessed track data comprises a plurality of target sampling points; obtaining target sensing semantic information of each target sampling point in the preprocessed track data set; determining a road boundary of each target sampling point based on the target perception semantic information; and performing iterative aggregation processing on all the target sampling points based on the road boundaries of all the target sampling points to generate a road skeleton road network.

This invention discloses a destination prediction method and system based on a hierarchical spatiotemporal embedding transformer model. The invention includes: dividing a portion of the trajectory into a coarse-grained grid and spatiotemporally encoding it to obtain a coarse-grained spatiotemporal trajectory image representing the approximate direction of the trajectory; using a CNN to predict and extract the destination grid region; extracting GPS trajectory points; dividing the destination grid region into a fine-grained grid and spatiotemporally encoding it to obtain a refined destination region trajectory image; using a Feature Pyramid Transformer (FPT) to extract the motion features of the GPS trajectory points in the destination region; and using a fully connected network and a Softmax classifier to predict the probability of candidate destinations and determine the output candidate destination. This invention aims to extract spatiotemporal information from trajectory data to predict destinations, addressing the problems of data sparsity, missing temporal information, and insufficient multi-scale feature extraction faced by existing methods.

This invention relates to the field of traffic safety application technology and discloses a truck warning status recognition method based on convolutional neural networks, comprising the following steps: processing GPS trajectory data; analyzing truck warning data features; and determining the optimal network structure. This truck warning status recognition method based on convolutional neural networks establishes a mapping relationship between truck warning status and trajectory segment features by constructing a kinematic feature index system. It proposes a truck warning status recognition technology to provide technical support for identifying safety risks through abnormal trajectories, reducing the probability of accidents, improving truck operation safety and driving efficiency, and ensuring road traffic safety.