108 results about "Gps data" patented technology

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 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 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.

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 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.

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.

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.

This application discloses an autonomous navigation and deception defense system and method for unmanned aerial vehicles (UAVs) in GPS-denied environments. The system includes a multi-source sensor array, and parallel deception detection and isolation threads and a tightly coupled multi-source fusion navigation thread. The deception detection and isolation thread performs multi-dimensional consistency checks on received navigation data. When a dual comparison of geomagnetic fingerprints indicates a poor match between GPS position and geomagnetic readings, but a good match between visual position and geomagnetic readings, the GPS data is isolated from the fusion module. The tightly coupled fusion navigation module employs an error state Kalman filter with an IMU as its core. A geomagnetic deviation term is introduced into the state vector, and the original observation geometric information is directly fed into the filter for tight coupling. The difference between the geomagnetic vector obtained from a geomagnetic vector map query and the measured value from the magnetometer is used for fusion and updating, achieving high-precision continuous navigation in GPS-denied environments.

The application discloses an emergency rescue vehicle auxiliary positioning method fusing multi-source data, and the method is: collecting ETC issuing data, transaction data and service area data and performing multi-source fusion; extracting a mobile phone number and an alarm time from an alarm event, binding a vehicle license plate number and generating a unique identifier; extracting a vehicle time sequence passing track based on an ETC gantry passing database, taking the nearest gantry node as an upstream reference point; fusing service area data and road network topology information to calculate an actual passing time; generating a path probability distribution and a prior context vector by using a global topology attention mechanism through a path prediction module, and predicting a speed reference line through a task condition recursion unit based on a speed estimation module; finally, combining the upstream gantry post number, the actual passing time and the predicted speed to calculate the current position of the vehicle and fusing mobile phone GPS data to perform abnormal correction positioning to obtain route number, downstream gantry and post number information. The application realizes lane-level accurate positioning and effectively improves rescue efficiency.

Systems and methods to predict and apply regenerative braking using GPS (global positioning system) and IMU (inertial measurement unit) data. Techniques described herein may be utilized to use a vehicle's current GPS position and IMU sensor data to determine charging availability for electric or hybrid vehicles. IMU and / or GPS data is used to determine a next path prediction and detect next route elevation level. If the next route elevation level is above a threshold, value, the vehicle's motor may be automatically swapped for regenerating energy. Accordingly, recharging operations may be performed in a wide change of scenarios, including those where a driver has not engaged the brake pedal.

A computer-implemented method for classifying road intersections by means of GPS data, in particular from a plurality of vehicles in a fleet. The method includes: using existing GPS data points of vehicle movements, which were captured using GPS sensors of the vehicles at at least one road intersection; converting the captured GPS data points into a 2D contour by determining a center point of the road intersection and creating a polygon that represents a 2D contour of the road intersection; encoding the 2D contour of the road intersection using elliptic Fourier descriptors using a Fourier transform method and classifying the encoded data into different intersection types.

The invention provides a vehicle panoramic portrait construction method and device and a server, and relates to the technical field of vehicle supervision, and the method comprises the steps: obtaining multi-source vehicle data which comprises vehicle static attribute data, vehicle dynamic GPS data, vehicle real-time CAN bus data and vehicle key module state data; performing data association fusion processing on the multi-source vehicle data by taking the vehicle ID and the timestamp as a unified association key to obtain target vehicle data; and on the basis of the target vehicle data, a vehicle panoramic portrait of a three-layer structure for real-time supervision is generated, a unified supervision view is constructed by using the vehicle panoramic portrait, vehicle-road collaborative visual display is carried out, and the vehicle panoramic portrait comprises an attribute layer, a dynamic layer and a state layer. According to the invention, the accuracy and efficiency of vehicle supervision can be significantly improved.

The present application belongs to the technical field of unmanned aerial vehicle monitoring, and particularly relates to a method and device for filtering hidden danger targets of unmanned aerial vehicles, an electronic device and a storage medium. The method comprises: obtaining real-time data of unmanned aerial vehicles and preprocessing the real-time data to obtain target data; performing target reasoning and scene segmentation reasoning on scene images in the target data respectively, and correspondingly outputting hidden danger targets containing label information, confidence and target detection frame information and scene region masks, and constructing the hidden danger targets and the scene region masks into a first data set; obtaining GPS coordinates and feature vectors of the hidden danger targets based on unmanned aerial vehicle GPS data, unmanned aerial vehicle three-axis attitude angles, unmanned aerial vehicle mounted camera parameters and target detection frame information of the hidden danger targets, and constructing the GPS coordinates and the feature vectors into a second data set; and filtering the hidden danger targets based on the first data set and the second data set. The present application aims to solve the problems of single filtering dimension, poor multi-model coordination and insufficient real-time performance of unmanned aerial vehicle targets in the prior art.

The invention discloses a bus OD derivation and road network flow matching method based on multi-source data and electronic equipment, and the method comprises the steps: obtaining and preprocessing bus multi-source data in a target area, and building a station-line-vehicle three-dimensional mapping relation with direction information; the bus multi-source data comprises bus GPS (Global Positioning System) data, station GIS (Geographic Information System) data and IC (Integrated Circuit) card swiping data; determining passenger types and historical travel laws according to the IC card swiping data, analyzing travel chain features according to the historical travel laws, and then deducing getting-off stations and time of passengers in combination with POI types of stations; according to the deduced get-off station of the passenger and the corresponding route, determining a station sequence interval of the travel path of the passenger on the bus route; then, according to the corresponding relation between the line station and the road section number in the road network, the passenger travel path is mapped to the actual bus road network, the total passenger flow of each road section is counted, and flow statistical data with the road section as the unit is generated.

The application discloses a global positioning system spoofing detection and positioning recovery method based on pseudo-range residual error, and comprises the following steps: S1, initializing system parameters, determining initial position information of a receiver and clock error of the receiver, and acquiring latest navigation ephemeris information; S2, using measurement information of an inertial sensor to perform prior estimation on the current position of the receiver; S3, acquiring global positioning system measurement information, and calibrating satellite pseudo-range information; S4, acquiring an estimated value of the satellite pseudo-range by using the navigation ephemeris information and the estimated position of the receiver; S5, using the residual error of the satellite pseudo-range measurement value and the estimated value to judge whether the currently received satellite data is spoofing data; and S6, using real satellite information and measurement information of the inertial sensor to realize positioning recovery. The method can detect GPS spoofing behavior, fully utilize effective information in GPS data, and comprehensively optimize positioning error.

The application discloses a large-scene meta-universe space superposition method and device. The method comprises the following steps: acquiring visual images, IMU data, geomagnetic data and GPS data of a terminal device of a current large-scene environment in real time; extracting visual features; obtaining pose data of the terminal device; obtaining geographical position plane coordinates of the terminal device based on the GPS data and the geomagnetic data; constructing a three-dimensional AR digital space based on the visual images and the pose data of the terminal device; performing visual positioning based on the visual features, the geographical position plane coordinates and a virtual three-dimensional space model to obtain real space three-dimensional coordinates; determining a virtual space position of the terminal device in a meta-universe virtual space based on the real space three-dimensional coordinates and current pose data of the terminal device; and fusing the meta-universe virtual space into the AR digital space according to the virtual space position. According to the application, the meta-universe space can be quickly and accurately superposed in a large-scene environment with few visual features, and the response speed is fast and accurate.

The invention relates to a positioning track fusion processing method, system and device for a vehicle and a gun, and belongs to the technical field of gun positioning, and the method comprises the steps: obtaining a vehicle attitude angle of the vehicle at the current moment, and calculating the vehicle displacement according to the vehicle attitude angle; according to the vehicle displacement and the vehicle position initial value, the track point estimation position of the vehicle is obtained, and according to the track point estimation position of the vehicle, the gun estimation position is calculated; when the global navigation satellite system data of the gun is not received, the current position of the gun is calculated according to the triaxial acceleration of the gun; obtaining fused gun trajectory points according to the estimated gun position and the current gun position; when the global navigation satellite system data of the gun is received, the track point of the gun is the position of the gun; and projecting the gun track point to the road network data, searching the nearest road line segment, and correcting the gun track point to the road direction. The gun positioning method has the beneficial effect that the gun positioning track precision is improved.

A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may process the video data, with an object detector model, to identify objects and to generate a first feature vector. The device may process the GPS data and the IMU data, with a first CNN model, to generate a second feature vector, and may process the objects and the video data, with a tracking model, to identify positions and classes of the objects and to generate a third feature vector. The device may utilize a second CNN model to generate a matrix of object features based on the first, second, and third feature vectors, and may utilize a spatiotemporal attention selector model or a max pooled model with the matrix of object features to identify a classification of a maneuver of the vehicle. The device may perform actions based on the classification.

The application discloses a traffic congestion state real-time monitoring method based on Internet of Things multi-source data fusion, relates to the technical field of traffic congestion state monitoring, and comprises the following steps: Step 1: setting up geomagnetic coil detectors, roadside microwave radars, floating car GPS data acquisition terminals and intersection video monitoring cameras on each monitoring road section in a city road network, collecting traffic state data of each monitoring road section according to a preset collection period of each, analyzing all kinds of data in the same analysis time window, obtaining standard data records of each monitoring road section, evaluating the credibility of each data source in real time through a mutual verification matrix method, dynamically calculating fusion weights for weighted fusion, then mapping the fused speed and density to a two-dimensional plane to analyze and determine the traffic flow state through trajectory moving direction, and simultaneously calculating the speed space gradient between adjacent road sections to detect congestion boundaries and calibrate positions for tracking movement.

Exemplary embodiments of the present disclosure are, directed towards a system and method for securing financial transactions using geotagged and timestamped machine-readable codes. The system includes computing device with financial transactions securing module that enables user device registration with elements such as IMEI, SIM, IP address, and GPS data to create secure device profile. Unique machine-readable code is, generated for each transaction, embedding real-time geolocation, timestamp, and transaction data to ensure location-based authenticity. Biometric authentication further verifies the user's identity. The system transmits transaction data to server with financial transactions verification module, which performs real-time location verification by cross-referencing GPS data and validating timestamps to prevent expired code reuse. The system detects device-specific mismatches, anomalies, and unauthorized access attempts, generating alerts and automatically blocking transactions if inconsistencies arise. Fallback OTP verification is, triggered when primary verification fails; ensuring only authorized users complete the transaction. This system enhances security, transparency, and fraud prevention in digital transactions.

The application discloses a kind of based on multi-source data fusion's passenger OD space-time distribution identification method and system, comprising: processing passenger bus GPS data, obtain the parking point sequence of passenger bus;Passenger bus line ticketing data and passenger departure data are associated, and the start and end point sequence of ticketing data is obtained;The parking point sequence of passenger bus is associated with the start and end point sequence of ticketing data, and passenger OD space-time distribution data is obtained.The application can realize the accurate identification of passenger OD, so as to accurately grasp the real arrival position of passenger.

This invention discloses a real-time traffic congestion monitoring method based on multi-source data fusion of the Internet of Things, belonging to the field of traffic congestion monitoring technology. The method includes the following steps: First, setting up geomagnetic coil detectors, roadside microwave radars, floating car GPS data acquisition terminals, and intersection video surveillance cameras on each monitoring road segment in the urban road network. Traffic status data for each monitoring road segment is collected according to their respective preset collection cycles. All types of data are unified into the same analysis time window for analysis to obtain standard data records for each monitoring road segment. The credibility of each data source is evaluated in real time using a cross-validation matrix method, and fusion weights are dynamically calculated for weighted fusion. Then, the fused speed and density are mapped onto a two-dimensional plane, and the traffic flow status is determined through trajectory movement direction analysis. Simultaneously, the speed spatial gradient between adjacent road segments is calculated to detect congestion boundaries and mark positions for tracking movement.

The application discloses a GPS driving track correction method, device and equipment and a storage medium, and the method comprises the following steps: detecting whether a vehicle is in an idle state according to current GPS data and historical GPS data of the vehicle; when the vehicle is in the idle state, updating GPS record time according to the current GPS data, obtaining target GPS record time, taking GPS coordinates in the historical GPS data as target GPS coordinates, and correcting GPS driving records of the vehicle according to the target GPS record time and the target GPS coordinates. Since the application judges whether the vehicle is in the idle state according to the current GPS data and the historical GPS data of the vehicle, updates the GPS data of the vehicle in the idle state, corrects the original driving track of the vehicle according to the updated GPS data, filters out the track deviation of the vehicle on the GPS route map caused by the non-subjective purpose deviation due to the idle state, and obtains higher-quality GPS data track information.

The application 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, which comprises the following steps: acquiring a GPS original data stream in real time, 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 to quantify the degree of terrain undulation; calculating additional acceleration and additional energy consumption loss in combination with the slope change rate; predicting recoverable electric energy based on the altitude sequence, speed data and real-time state of a kinetic energy recovery system; calculating predicted net energy consumption in combination with reference energy consumption, additional energy consumption and recoverable electric energy, and dynamically calibrating a model through continuous error triggering. The application improves the energy consumption prediction accuracy and provides reliable support for electric vehicle energy management.