211 results about "Gps data" patented technology

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention relates to the field of rocket recovery pose estimation design, and particularly discloses a multi-mode cooperative perception driven rocket recovery high-precision pose estimation system, which comprises a multi-scale Marker layout module, a multi-scale cooperative perception driven rocket recovery high-precision pose estimation module and a multi-scale cooperative perception driven rocket recovery high-precision pose estimation module, and is characterized in that the multi-scale Marker layout module is used for arranging three-level height detection bands around a recoverer device, and each band is configured with Marker type layout modes of different sizes; the multi-view cooperative detection module is used for collecting images through a plurality of high-resolution cameras installed on the side face of the rocket and outputting visual poses in combination with scale adaptive detection process optimization and a space consistency constraint strategy; the multi-sensor fusion module is used for performing fusion optimization on the visual pose, the IMU and the GPS data through a factor graph to construct a factor graph joint optimization framework; accuracy and stability of a rocket recovery pose estimation system are realized, and real-time performance, accuracy and anti-interference capability of a rocket recovery pose estimation scheme are improved.

The invention is suitable for the technical field of electric vehicle energy consumption management, and provides an electric vehicle energy consumption prediction method and system using GPS data, and the method comprises the steps: obtaining a GPS original data stream in real time, carrying out the preprocessing, and fusing a digital elevation model to generate an altitude sequence; calculating an instantaneous slope value and a slope change rate based on the altitude sequence, and quantifying the topographic relief intensity; calculating additional acceleration and additional energy consumption by combining the gradient change rate; based on the altitude sequence, the speed data and the real-time state of the kinetic energy recovery system, recoverable electric energy is predicted; and combining the reference energy consumption, the additional energy consumption and the recyclable electric energy to calculate and predict net energy consumption, and triggering model dynamic calibration through a continuous error. According to the invention, the energy consumption prediction precision is improved, and reliable support is provided for electric vehicle energy management.

The invention discloses a traffic jam detection and early warning method based on an unmanned aerial vehicle video stream, and belongs to the technical field of intelligent traffic. The system is combined with a floating vehicle triggering model, collects road videos in real time through an unmanned aerial vehicle, and realizes high-precision vehicle detection and tracking in combination with improved YOLOv8 and DeepSort algorithms; unmanned aerial vehicle data and floating vehicle GPS data are fused, three elements of traffic flow are quantified, and a dual-mechanism congestion detection model is constructed; respectively utilizing a GRU cooperative traffic wave theory to predict the congestion dissipation duration and utilizing an improved GRU + GCN model to predict the traffic situation; the signal lamps are dynamically regulated and controlled through multi-agent reinforcement learning, and active congestion relieving is achieved. The method has the advantages of wide-area coverage, high-precision perception and intelligent response, and the urban traffic control efficiency is remarkably improved.

The invention relates to a manual driving vehicle overspeed behavior identification and intervention method based on a detection carrier, and the method comprises the steps: building a detection carrier system, collecting adjacent HV point cloud data through a laser radar, carrying out the median average filtering, K-Means and DBSCAN combined clustering preprocessing, and achieving the multi-target dynamic tracking through a Point Pill + DeepSORT model; and calculating the real average speed of the HV by using a 1-3s self-adaptive speed measurement window, and comparing the road speed limit to complete the overspeed identification. And when overspeed is judged, dynamically triggering a corresponding high-speed camera to capture, extracting license plate information, fusing speed and GPS data to generate illegal evidence. The method comprises the following steps: quantifying the influence of a detection carrier on an HV individual overspeed behavior, training an HV swarm agent based on VAE-WGAN data enhancement and a double-branch deep Q network, simulating multiple traffic scenes in SUMO, and determining an optimal deployment scheme of the detection carrier by using a dynamic Gaussian mixture Bayesian network and a double-layer double-target optimization model. The method breaks through the limitation of traditional speed measurement, improves the recognition precision and deterrence effect, and balances the traffic safety and efficiency.

The invention, which relates to the technical field of mechanism material distribution, discloses a mechanism material distribution real-time collaboration and traceability system based on a unified data platform, comprising a data specification module, a dynamic routing module, a traceability code generation module, a storage verification module, a nutrition calculation module, a report generation module and an abnormal response module. The data specification module adopts a unified JSON Schema to define data specifications of a supplier end production batch number and a quality inspection report, a distribution end warehouse temperature and humidity and a vehicle GPS, and a school end acceptance image and student feedback; the dynamic routing module is used for realizing state change and pushing to the three-party system in real time through a dynamic routing engine based on the output of the data specification module; the traceability code generation module is used for generating a four-segment traceability code according to the structured data of the data specification module and storing a key data hash value of each link; and the evidence storage verification module is based on the output of the traceability code generation module.

A method for collecting road geometry information includes obtaining gyroscope data from a gyroscope of a personal device fixed to a moving vehicle; aligning the gyroscope data with GPS data from a GPS module of the personal device to generate aligned data; matching the aligned data to a nearest road segment based on a preexisting map; determining a time of stable dynamics of the vehicle based on the map and the matched nearest road segment; and correcting a drift of a gyroscope using data from an accelerometer of the personal device during the determined time of stable dynamics.

The invention provides an intelligent driving scene reconstruction method and system and a storage medium, and the method comprises the steps: obtaining an OpenStretMap map file based on the real vehicle GPS data, and carrying out the lane-level road network matching based on the OpenStretMap map file; the OpenStreetMap map file obtained after matching of the lane level road network is converted into an OpenDR IVE map file; based on real vehicle GPS data, generating a main vehicle track by using a FastDTW algorithm; based on the environment vehicle information, generating a dynamic interaction object track through a Hungary matching algorithm; and fusing the OpenDRIVE map file, the main vehicle track and the dynamic interaction object track, and reconstructing a scene file in an OpenSCENARIO format in preset simulation software. According to the method, automatic reconstruction is carried out by utilizing the real vehicle data, the real vehicle data can be automatically stored as a universal OpenSCENARIO scene file, simulation testing can be carried out in different types of automatic driving simulation software, a real driving scene can be accurately simulated, and a reliable simulation environment is provided for development, testing and verification of an intelligent driving algorithm.

The present invention provides a high-speed fault identification method based on Internet of Vehicles big data, comprising the following steps: S1, acquiring vehicle status data, behavior data, and GPS data of an in-vehicle infotainment terminal, transmitting same to the cloud, and preprocessing the vehicle status data, the behavior data, and the GPS data to obtain vehicle speed, sharp acceleration, sharp deceleration, sharp turn, activation status of hazard warning lights, driver exit status, an anti-lock braking system (ABS) fault related trouble code, an engine malfunction indicator lamp related trouble code, a tire pressure anomaly related trouble code, a coolant temperature warning related trouble code, and GPS point positioning data. Thus, whether a vehicle has stopped on a highway due to a fault can be effectively and rapidly detected, and location information of the vehicle is acquired on the basis of the GPS point positioning data, without the need to mount an aftermarket sensor, thereby achieving the advantages of low costs and high accuracy. Multi-dimensional identification of whether the vehicle has stopped on the highway due to the fault provides a basis for an original equipment manufacturer to implement after-sale care and for ensuring driver safety.

The invention provides a radar system error estimation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining radar data and GPS data; performing time registration processing on the radar data and the GPS data to obtain time-aligned GPS data; performing filtering preprocessing on the radar data and the time alignment GPS data to obtain filtered radar data and filtered GPS data; according to the filtered GPS data, scaling the filtered radar data through anisotropic scaling based on scale transformation to obtain scaled radar data; carrying out coordinate conversion on the filtered GPS data, and converting the filtered GPS data into a radar polar coordinate system; and in a radar polar coordinate system, registration is carried out on the scaled radar data and the filtered GPS data through an improved iterative nearest point algorithm, and a radar system error is estimated. According to the method, the estimation precision of the radar system error is remarkably improved, and the problem of non-rigid deformation and rigid error coupling is effectively solved.

The invention relates to the technical field of data processing, in particular to a cleaning fusion system for urban traffic multi-source heterogeneous data, which comprises a space-time correlation graph construction module used for creating a unique node identifier for each GPS data record, roadside camera data record and coil data record according to multi-source heterogeneous traffic data, and establishing a spatial adjacent relation edge by judging whether road segment identifiers to which multiple nodes belong are the same or not. According to the method, context awareness evaluation of data source reliability is achieved, meanwhile, when traffic time series data are processed, the data are decomposed into a low-frequency approximation coefficient representing a long-term trend and a high-frequency detail coefficient capturing a sudden change event, and the approximation coefficients of the multi-source data are fused through the obtained context awareness credibility score; meanwhile, a maximum value selection strategy is adopted for detail coefficients, key signal features caused by real traffic events can be reserved, and the fidelity of fused traffic data is improved.

The invention discloses an unmanned vehicle positioning method and system based on multi-sensor fusion, and belongs to the technical field of unmanned driving and robots. The method mainly comprises the following steps: fusing laser radar and IMU data through an LIO module, generating a laser radar odometer, and aligning the laser radar odometer with GPS data through SVD decomposition so as to deduce latitude and longitude information in a GPS-free environment; a deep learning model is introduced to enhance the robustness of label detection through an improved AprilTag visual module, and the precise longitude and latitude of a camera are calculated through nonlinear optimization in combination with IMU attitude data and the prior position of a label; and finally, according to error characteristics of the two modules, performing dynamic weighted fusion on positioning results, and performing optimization by using an error state Kalman filter (ESKF) to obtain a smooth and high-precision final pose. According to the invention, the problem of continuous and accurate positioning of the unmanned vehicle in a GPS signal interruption or complex environment is effectively solved, and the reliability and robustness of the system are improved.

The invention discloses a ship pier anti-collision early warning method and system based on a laser radar, and relates to the field of ship collision early warning, and the method comprises the steps: judging the movement condition of a ship based on GPS data and imu data, and collecting the three-dimensional point cloud data in front of a ship driving route when detecting that the ship moves towards a collision object; converting the three-dimensional point cloud data from a radar coordinate system to a ned coordinate system, and filtering and segmenting the three-dimensional point cloud data after coordinate conversion to obtain a point cloud cluster of the collision object; based on the distance from each point in the point cloud cluster to the ship contour, a preset distance threshold value and a preset number threshold value, executing multi-level collision risk assessment so as to output an early warning signal of a corresponding level; according to the method, the three-dimensional point cloud data is filtered and segmented, noise point clouds are effectively eliminated, the point cloud cluster representing the collision object is accurately extracted, and refined and graded early warning of the collision risk is further realized through a multi-stage risk assessment mechanism.

The invention provides an intelligent scene classification method and device based on a vehicle track, equipment and a medium. The problems that an existing intelligent driving scene classification labeling method is low in labeling efficiency and cannot give full play to the advantages of track data are effectively solved. The method comprises the following steps: starting a micro-service for scene intelligent classification of the vehicle; the micro-service downloads mcap data according to an mcap data list corresponding to the data updating information, extracts GPS data and IMU data in the mcap data, and combines the GPS data and the IMU data to obtain an initial vehicle track; intercepting a target vehicle track from the initial vehicle track, extracting vehicle state information and road information in the target vehicle track, and labeling corresponding labels; and integrating the labels of the multiple segments of target vehicle tracks to classify a target scene where the vehicle is located, and performing intelligent classification on the scene of the vehicle based on the target scene.

The invention relates to a digital intelligent trajectory prediction method and system based on logistics transportation order tracking. The method comprises the steps of collecting multi-source transportation data including vehicle GPS data, AI telephone interaction data, ETC passing record data and freight station transfer operation node data; performing space-time alignment, data cleaning and map matching based on road network information on the multi-source transportation data; utilizing the processed multi-source data to generate a continuous digital intelligent track through an interpolation algorithm and a path deduction method; calculating the latest predicted position of the vehicle through a prediction algorithm model based on the digital intelligent track in combination with real-time traffic information and a historical driving mode; residual paths, real-time road conditions, weather factors and driver behavior modes are comprehensively considered, and the predicted arrival time is calculated through a dynamic ETA model. According to the invention, the GPS signal blind area problem is effectively solved, the whole-course blind-area-free tracking and high-precision arrival time prediction are realized, and the transparency and intelligent management level of logistics transportation are greatly improved.

The application is suitable for the field of spatio-temporal data mining technology, and provides a taxi supply and demand imbalance road section detection method, which comprises the following steps: extracting a GPS data entry sequence corresponding to each license plate from a taxi GPS original data set in chronological order; determining a taxi supply point and a taxi demand point according to all GPS data entry sequences; determining a correction point of the taxi supply point and the taxi demand point in a city road network; finely dividing the city road network to obtain a final city road network; taking a road section with a taxi demand correction point in the final city road network as a seed road section, and expanding the seed road section along the final city road network through an iterative optimization method to obtain a plurality of candidate taxi supply and demand imbalance road sections; performing significance test by using a Monte Carlo simulation method, and taking a candidate taxi supply and demand imbalance road section that passes the significance test as a taxi supply and demand imbalance road section. The application can improve the accuracy of taxi supply and demand imbalance road section detection.

To provide a system for preventing an accident by detecting a recognition discrepancy or an operation mistake between a controller and a pilot in real time.SOLUTION: A system comprising: means for receiving voice communication data between an air traffic controller and a pilot of an aircraft; means for converting the received voice communication data into text data; means for extracting instruction content from the converted text data; means for generating a predicted action based on the extracted instruction content; means for collecting current position data of the aircraft from a GPS; means for monitoring an actual action of the aircraft based on the collected GPS data; means for comparing the monitored action of the aircraft with the predicted action to detect an abnormality; and means for generating and notifying an alert when the abnormality is detected.SELECTED DRAWING: Figure 1

The invention provides a traffic participant road condition early warning method and system based on a smart phone, and relates to the technical field of active safety. The method comprises the following steps: acquiring a video stream through a smart phone camera, and synchronously acquiring data of a gyroscope, an accelerometer, an ambient light sensor and a GPS; identifying a user motion state and an environment scene based on the sensor data; carrying out target detection, semantic segmentation and distance estimation on the video frame by adopting a lightweight neural network; performing risk assessment according to the target type, the distance and the movement speed of the user, and generating an early warning instruction; a multi-mode warning is sent to a user in a voice, vibration, vision or interface switching mode; and dynamically adjusting the analysis frequency and the power consumption mode according to the environmental complexity. Existing hardware of the smart phone is fully utilized, extra equipment is not needed, low-cost and high-universality real-time environment perception and active early warning are achieved, and the travel safety of traffic participants such as walking and riding is remarkably improved.

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.

Embodiments of the present disclosure disclose a mapping method and device, electronic equipment and storage medium. The method comprises: determining a laser radar odometry pose based on a plurality of sensor data; determining a center coordinate of a target road mark according to the laser radar odometry pose; determining a odometry constraint condition according to the laser radar odometry pose; optimizing the laser radar odometry pose associated with the target road mark according to at least the laser radar odometry pose associated with the target road mark, the center coordinate of the target road mark and the odometry constraint condition, to obtain an optimized laser radar odometry pose associated with the target road mark; and obtaining a high-precision map based on the optimized laser radar odometry pose associated with the target road mark. The present disclosure improves the precision and consistency of laser mapping, especially for long-distance GPS data missing or indoor large-scale closed-loop application scenarios.

This application proposes a GPS-based method, device, and application for calculating real-time bus traffic conditions, including the following steps: S00, obtaining vehicle GPS data and road segment data for the current period, using the vehicle's road mileage as the actual mileage for the current period; wherein the road segment data is data consisting of a series of connected coordinate points; S10, performing exception processing on the vehicle GPS data to obtain valid vehicle GPS data; S20, using the Viterbi algorithm to match each GPS point to the coordinate point based on the distance between the GPS point in the vehicle GPS data and each coordinate point in the road segment data, and calculating the road segment speed for the current period based on the matching results; S30, using the average speed of each vehicle passing through the road segment as the average speed of the current road segment and outputting it. This application can produce a more accurate analysis of real-time bus traffic conditions.

The system according to the embodiment aims to reduce the burden on bus drivers and achieve efficient operation. [Solution] The system according to the embodiment includes a reception unit, a generation unit, an analysis unit, an announcement unit, an acquisition unit, and an adjustment unit. The reception unit receives questions from passengers. The generation unit generates answers based on the questions received by the reception unit. The analysis unit analyzes camera footage inside the bus. The announcement unit makes warning announcements based on the results of the analysis by the analysis unit. The acquisition unit acquires GPS data. The announcement unit announces the next bus stop based on the data acquired by the acquisition unit. The acquisition unit acquires temperature sensor data. The adjustment unit adjusts the air conditioner settings based on the data acquired by the acquisition unit.

The application discloses a kind of high-precision vector map automatic generation method, mainly according to laser point cloud intensity, geometric structure feature extraction lane marking, through ground filtering, ground, non-ground point cloud is segmented;Adopt block grating to convert three-dimensional point cloud into two-dimensional projection image;Using image filtering, edge extraction operator, dynamic adaptive threshold segmentation, morphological analysis and other processing extraction road marking pixel, based on point cloud image mapping relationship extraction marking point cloud;Using the point cloud contour matching method based on knowledge template library carries out marking classification.Based on deep learning convolutional neural network, road sign detection classification is carried out to road image, and the point cloud region of interest is established by image point cloud fusion;Using clustering screening candidate point cloud, based on principal component analysis carries out local projection, based on knowledge template library point cloud matching extraction traffic sign;Finally, based on GPS data, calibration, parameter fitting, vectorization, data association are carried out, and the construction of high-precision vector map is completed.

Techniques for multi-level device verification for mobile gambling applications are described. For example, when an application executing on a smartphone attempts to access a gambling activity of the gambling service, the location of the smartphone is attempted to be determined based on GPS data. If the GPS data is not determinable, the location of the smartphone may be determined using another technique, such as triangulation. Based on the determined location, the application receives an indication that the smartphone is in a U.S. state where gambling is allowed, and the application is enabled to access the gambling service to engage in the gambling activity.

The application discloses a kind of unmanned aerial vehicle delivery authentication method and device based on implicit gait behavior, and user places order through mobile phone delivery application program.Reaching specified place, unmanned aerial vehicle hovers in the air, informs user its arrival, then establishes key protection communication channel with user mobile phone.User hand-held mobile phone goes to specified position.Unmanned aerial vehicle confirms the real-time position of user by sharing GPS data, and searches user using camera.At the same time, unmanned aerial vehicle captures user video and processes in real time, extracts the time series of user body key point.At the same time, the mobile phone of user constantly collects time series data from its IMU sensor.Then, the two data are exchanged between unmanned aerial vehicle and user mobile phone, so as to start mutual identity authentication process.The application utilizes hand movement mode when user walks to unmanned aerial vehicle to realize mutual identity authentication, without additional hardware or specific identity authentication operation, and shows strong resistance to various fraud attacks.

The invention discloses a quick-response riding slope resolving algorithm and system, and the method comprises the steps: obtaining riding data, initializing an attitude angle according to the riding data, and obtaining an initial quaternion; filtering the acceleration data and the gyroscope data, calculating corresponding attitude angles, and performing complementary combination on the attitude angles to obtain a fused attitude angle and a fused quaternion; calculating a real-time gradient according to GPS data and air pressure data, and performing self-iteration or fusion on the real-time gradient by using a relationship between the real-time gradient and the fusion attitude angle to obtain a target gradient; and outputting the target gradient, and correcting the fused quaternion according to the target gradient. According to the method, the high response speed of the inertial sensing unit is utilized, on the premise that initial gradient calibration exists, according to the resolving results of the gyroscope and the acceleration, rapid iterative updating is carried out, real-time fusion of the gradient and the attitude angle is achieved, and the gradient resolving speed and the resolving precision of a gradient rapid change scene can be greatly improved.

A method is disclosed. A data set including: (a) identifiers of a set of incidents occurring within a defined geographic region to which at least one service provider responded during a first time period and (b) address data identifying a location within the geographic region of each said incident of the set is retrieved over a network. An instruction to generate a heat map of the incidents occurring within the geographic region during the first time period is received from a user via a user interface generated to a display device. In response to the instruction to generate the heat map, the address data is converted to GPS data. A heat map of an aerial view of the geographic region based on the GPS data is generated. The heat map is displayed to the display device in a user interface.

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.

Computer-implemented method for communicating emergency information to a vehicle, including: Receiving at least one emergency notification with a geographic attribute on a vehicle computer, wherein the at least one emergency notification includes location information related to an emergency; Receiving GPS data on the vehicle computer; Determining the geographical location of the vehicle based on GPS data on the vehicle computer; Select at least one emergency notification based on the vehicle's geographic location and the geographic attribute on the vehicle computer and Issuance of at least one selected emergency notification, such that a notification is presented to a vehicle occupant, characterized in that the procedure further comprises: Determine, if the vehicle is not within the geographical location of the emergency, whether a direction of movement of the vehicle leads towards the geographical location of the emergency, holding the at least one emergency notification in a message queue of the vehicle computer if the direction of movement of the vehicle leads towards the geographical location of the emergency, and present the at least one emergency notification in the queue when the vehicle has reached the geographical location of the emergency. Determine, if the vehicle is within the geographical location of the emergency, whether the vehicle's direction of movement is away from the geographical location of the emergency, and issue at least one selected emergency notification until the vehicle is outside the geographical location.

A device for providing a spatial information convergence solution comprising: a data collection module configured to receive a plurality of spatial information data and user requirement data; a selection module configured to select needed data required for generating the requirement data from among the plurality of spatial information data; a fusion module configured to generate a spatial information convergence solution by fusing the needed data; and an output module configured to provide the spatial information convergence solution to a user terminal for the user,wherein the plurality of spatial information data include at least one of CCTV data, drone data, GPS data, satellite imagery data, and aerial data.