91 results about "Gps trajectory" patented technology

The invention relates to the technical field of spatial-temporal trajectory data anomaly detection, in particular to a trajectory anomaly route detection method and system based on self-supervised trajectory representation learning. The method comprises the following steps: preprocessing acquired trajectory data; track double-view comparison representation learning is carried out based on a double-view-angle synchronous mask strategy; coding time dynamic based on a space-time fusion mechanism and fusing the time dynamic with spatial features; learning essential representation from the fused spatio-temporal features to perform trajectory reconstruction; and error checking is carried out based on the reconstructed trajectory, and an abnormal trajectory is judged through the reconstructed error. The invention provides a brand new trajectory anomaly detection model. According to the model, a GPS track and a grid-based track feature are fused, so that track representation is enriched; and meanwhile, a double-view synchronous mask mechanism is designed, so that the model can sense local disturbance of space and time dimensions at the same time in a training stage, and thus the sensitivity to local anomaly is improved.

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is 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.

The invention discloses a vehicle fault positioning and rescue scheduling platform and method based on GPS track acquisition, and relates to the field of vehicle fault positioning and rescue scheduling, and the method comprises the steps: uploading vehicle GPS and OBD data obtained by a vehicle-mounted terminal to a cloud database through employing a mobile network technology; according to the vehicle GPS and OBD data in the cloud database, the vehicle fault type is predicted and judged, and the position coordinate information of the vehicle fault is obtained; based on an expert scoring method, processing response levels of different vehicle fault types are set; accessing a high-precision digital map API (Application Program Interface), and marking schedulable rescue points and fixed GPS (Global Positioning System) coordinate data in a digital map; and establishing a vehicle fault rescue scheduling model, and carrying out comprehensive evaluation and scheduling on surrounding schedulable rescue points. The method has the advantages of effectively predicting the state and fault of the vehicle, efficiently responding to the processing of the fault of the vehicle, and preferentially scheduling and screening the schedulable rescue points.

The invention discloses a shared bicycle dangerous behavior identification method based on GPS trajectory data tracking, and belongs to the technical field of traffic safety. Comprising the steps of abnormal point elimination, point supplementation, coordinate system unification and normalization so as to improve data accuracy, and clustering division is carried out on data according to data features; in combination with a road network and POI data, hotspot and non-hotspot areas are determined through track intensity analysis; six dangerous behaviors are defined, historical data are analyzed, a threshold value is set, a predefined method is used for recognizing dangerous driving behaviors, and finally a recognition model is established to achieve accurate recognition of the six dangerous behaviors. Through mathematical statistics, the riding characteristics and dangerous behavior characteristics of the shared bicycle under different conditions are compared and researched; according to the dangerous behavior identification method based on the shared bicycle GPS track data, the utilization rate of the shared bicycle GPS data can be effectively improved, dangerous behaviors can be accurately identified, and the safety of riders and other traffic participants is guaranteed.

The invention relates to the field of intelligent traffic, and discloses a space-time big data-based software planning auxiliary decision-making method and system, and the method comprises the following steps: fusing mobile phone signaling, a vehicle GPS track, weather and traffic event data, constructing a dynamically updated space-time knowledge graph, and defining a space-time association relationship among a demand point, a connection station and a traffic event node; establishing a government, enterprise and passenger three-party asymmetric Nash negotiation model based on the knowledge graph, dynamically adjusting bargaining capability parameters in combination with real-time traffic events, and solving a game equilibrium solution; constructing a mixed integer collaborative optimization model by taking the equilibrium solution as a constraint condition, and solving a connection station layout and departure frequency scheme by adopting a two-stage algorithm; dynamically correcting a target function weight parameter according to the passenger flow prediction error through a PID closed-loop feedback mechanism; and finally, outputting a visual decision scheme. According to the method, the real-time decision-making efficiency and the scheme robustness in a large-scale urban road network scene are improved.

The invention relates to the technical field of intelligent traffic, in particular to a road network operation situation research method based on multi-source data fusion, and the method comprises the following steps: S1, selecting a road section area, and carrying out the collection of traffic data and vehicle data; s2, classifying data collected by a vehicle track layer and a road traffic layer according to two dimensions of space and time to obtain a vehicle GPS track and traffic data, and updating a data set containing a traffic state; s3, filtering and interpolating the updated data, performing feature selection, and performing data balance distribution by using an algorithm; s4, improving the Kalman wave to enable the Kalman wave to read the nearest single sensor; and S5, fusing and outputting the traffic state data. According to the method, data grouping is expanded from one dimension to two dimensions, a vehicle track layer and a road traffic layer are combined, the data mapping accuracy is improved, the Kalman filtering model SCAATF is improved, traffic state data fusion is achieved, and the operation situation of a traffic road network is accurately reflected.

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 present invention relates to a method for detecting synchronous movement patterns of buses at large temporal and spatial scales, and belongs to the technical field of synchronous movement pattern mining of GPS trajectories. First, a special distance mapping function is designed to map the three-dimensional GPS trajectory of a bus to a two-dimensional space. Secondly, the trajectory is divided by time windows in the two-dimensional space, and a hybrid index based on inclination angle and MBR is designed to organize the trajectory fragments. Finally, a verification-filtering framework is designed to accelerate the pattern detection process. The present invention is implemented on the big data computing engine Spark. After testing, the present invention can efficiently process massive bus GPS trajectory data and meet the needs of vehicle-to-vehicle detection in large-scale bus scenarios. The present invention avoids the traditional method based on trajectory clustering and significantly reduces the time complexity of the method.

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 discloses an urban traffic sub-region division method based on overall trajectory clustering, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: carrying out adaptive cutting and matching on a GPS track through a time dimension, and constructing a segmented track data set; based on an improved DTW algorithm, fusing vertical, parallel and angular distances to define trajectory similarity measurement, and retaining overall shape features of the trajectory; carrying out initial clustering by adopting an improved DBSCAN algorithm, and avoiding an over-aggregation problem through a dynamic core trajectory judgment mechanism; and designing a two-stage fine-grained division strategy, and performing secondary classification on unclassified trajectories in combination with polygonal boundaries and trajectory matching information to generate traffic sub-regions with clear boundaries. The method overcomes the defects that a traditional method neglects track space-time characteristics and boundaries are fuzzy, urban traffic zoning with high dynamic performance and high consistency is achieved, an accurate space division basis is provided for real-time traffic control, and the urban road network management efficiency is remarkably improved.

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 strip mine truck trajectory prediction method based on graph prompt tuning space-time pre-training. The strip mine truck trajectory prediction method comprises the steps of obtaining a historical GPS trajectory data set and a road network data set of a strip mine truck; the preprocessed historical GPS track data set is input into a Transform model, and the historical GPS track data set is input into the Transform model; converting the road network data set and the encoded historical GPS trajectory data set into vectors through a graph structure conversion module, inputting the vectors as input items into a graph attention neural network, and performing space-time pre-training to obtain a strip mine truck trajectory prediction model; creating a prompt memory pool, and performing knowledge-oriented prompt optimization on the strip mine truck trajectory prediction model by using the prompt memory pool; and predicting the running track of the strip mine truck by using the optimized strip mine truck track prediction model. The method has sufficient generalization ability, the problems of zero samples and small samples can be solved, a surface mine dispatching system is optimized, the productivity of a surface mine is improved, and the operation cost is reduced.

The invention relates to the technical field of expressway operation management / traffic big data, and provides a freight transport transfer demand prediction method under a differentiated charging policy, and the method comprises the steps: obtaining the GPS track data of a truck, and carrying out the data preprocessing of the GPS track data, and obtaining effective GPS track data; carrying out stop point discrimination on the preprocessed effective GPS track data by adopting a judgment method of combining time sequence DBSCAN clustering with a stop interval, identifying and dividing different travel segments, and constructing a complete truck travel chain; based on a trip chain, identifying the flow of each sample road section through road section matching, and expanding the obtained flow of each sample road section to the flow of the whole sample road section through observation station data and portal frame data of parallel national and provincial trunk roads and expressways; and performing parameter calibration on the constructed travel path selection behavior model by using the flow of the whole sample road section subjected to sample expansion, and predicting the expressway freight transfer traffic demand by using the model based on the adjusted differentiated rate.

The invention discloses a GPS trajectory tracking and dynamic path analysis method for an automobile data recorder. The method comprises the following steps: S1, collecting and preprocessing vehicle position and timestamp data; s2, constructing an initial trajectory point sequence, and generating a trajectory data set; s3, constructing a multi-section trajectory model, and realizing path dynamic tracking and spatial reconstruction; s4, mapping a current vehicle driving path, and identifying an offset point and an abnormal behavior according to a track fragment matching strategy; s5, extracting vehicle track offset characteristics, and calculating an angle offset accumulated value to generate a deviation score in combination with a control point change trend; s6, triggering a rule engine to adjust a path based on the score, and sending an optimization result to the driving assistance terminal; s7, establishing a multi-segment track model and a video timestamp index, and realizing track and video synchronous playback; and S8, uploading an analysis result to a terminal and a background to realize path monitoring and behavior feedback. The method improves the track precision, the recognition accuracy and the path optimization efficiency, and is suitable for intelligent driving dynamic decision making.

The invention discloses a rapid construction method of a strip mine road network model based on GPS trajectory data. The method comprises the steps that 1, an RTK sensor device is installed at the top end of a mine car; 2, track data recording is carried out through an RTK sensor, and track data are obtained; step 3, preprocessing the trajectory data; step 4, performing rasterization on the preprocessed trajectory data to obtain raster data; step 5, acquiring and processing the raster data; 6, extracting road features from the raster data; 7, connecting the road features through time sequence information and generating a road network road; step 8, realizing road network structure construction based on the road network road information; step 9, realizing road network visualization based on the road network structure and the feature information. According to the method, the road network model with relatively high precision can be quickly provided, the road network model is quickly and stably generated, and the road generation precision is high.

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 track representation learning method based on fuzzy coding, and relates to the technical field of track processing, and the method comprises the following steps: S1, collecting an original GPS track, and carrying out the coding of the original GPS track through an encoder of a BLUE model, and obtaining the final input embedding of a GPS point; s2, generating compressed track representation according to the final input embedding of the GPS points; s3, according to the compressed trajectory representation, performing trajectory reconstruction by using a decoder of the BLUE model; s4, determining space-time loss of trajectory reconstruction, and training the BLUE model; and S5, generating trajectory representation by using the trained BLUE model. The BLUE model based on the pyramid structure can learn the hierarchical semantics of the multi-level patch trajectory, is superior to the existing trajectory representation learning method in effectiveness and efficiency, and promotes the tasks of trajectory classification, similarity search and the like.

The invention discloses a reverse processing method and system for an online car-hailing platform. The invention relates to the technical field of online car-hailing platform management. Receiving a data packet from the channel, wherein the data packet comprises order information, complaint content and GPS track data; the received data packet is processed according to a preset responsibility judgment rule, and a responsibility party (a driver or a platform) is automatically judged; the responsibility judgment result comprises a responsibility person identifier, a violation type and a violation degree; through an automatic and intelligent responsibility judgment system, comprehensive data analysis and a preset responsibility judgment rule are combined, illegal behaviors can be accurately recognized, the responsible party can be accurately judged, the possibility of manual misjudgment and prejudgement is greatly reduced, and the responsibility judgment accuracy and fairness are improved. The work order automatic generation and pushing mechanism in the invention ensures the timeliness of work order processing. Meanwhile, due to the standardized work order processing flow and the real-time monitoring mechanism, a work order processing link is smoother, and delay and errors in the processing process are reduced.

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 application discloses a kind of based on four-tree and vehicle GPS track abstract grid road network establishment method, comprising the following steps: (1) according to GPS data logical relationship feature selection four-tree data structure;(2) using spatial connection operation will GPS data and actual road network data store in four-tree data structure;(3) using the GPS data embedding algorithm based on four-tree, according to taxi GPS data division basic grid unit;(4) according to road network topological structure, determine whether the connecting arc between each grid unit exists;(5) according to the shortest path distance between the road network nodes in each grid unit, the connecting arc weight of each grid unit is calculated by average;(6) establish abstract grid road network.The application can provide certain auxiliary reference for empty taxi path planning, reduce its empty rate.

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.