543 results about "Gps data" patented technology

The invention discloses a concrete bridge crack detection method based on computer vision, and relates to bridge detection, and the method comprises the steps: collecting a crack image, GPS data and a depth image of a concrete bridge; according to the GPS data, establishing a mapping relation between the pixel coordinates and the fracture physical space coordinates; based on feature matching of ORB features and a registration optimization algorithm based on mutual information, obtaining alignment of the crack image and the depth image at a pixel level, and obtaining a color crack image and a depth crack image; a MaskR-CNN model is adopted to extract a pixel-level crack segmentation mask of the crack region; according to the crack segmentation mask and the registered depth crack image, depth information of a crack region is estimated through a depth completion algorithm based on a conditional generative adversarial network (CGAN), and a crack depth distribution map is obtained; according to the crack depth distribution map, geometric parameters of the crack are extracted through a morphological algorithm and a central axis transformation algorithm. Aiming at the low concrete bridge crack detection precision in the prior art, the detection precision is improved.

The invention discloses an unmanned aerial vehicle trajectory tracking system based on multi-sensor fusion, which relates to the technical field of unmanned system autonomous navigation and control, and comprises an unmanned aerial vehicle data acquisition module, an unmanned aerial vehicle feature extraction module, a trajectory prediction deviation scoring module, a trajectory dynamic correction module and a trajectory monitoring module. The unmanned aerial vehicle data acquisition module acquires and preprocesses unmanned aerial vehicle and GPS data, the unmanned aerial vehicle feature extraction module constructs a trajectory tracking feature sequence table, the trajectory prediction deviation scoring module outputs three-dimensional trajectory prediction coordinates and deviation scores, and the trajectory dynamic correction module generates correction parameters. Based on the judgment logic, the judgment logic is converted into an unmanned aerial vehicle correction instruction and attitude feedback quantity, and the track monitoring module constructs a visual interface. According to the method, the trajectory tracking precision of the unmanned aerial vehicle in a complex environment is remarkably improved, real-time detection and dynamic correction of abnormal deviation are realized, the state monitoring capability is enhanced by a three-dimensional visual interface, the trajectory tracking error is reduced, the misjudgment rate is reduced, and the flight safety and the control efficiency are guaranteed.

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.

A self-adaptive multi-sensor fusion method using radar, laser radar, IMU and GPS in a complex environment comprises the following steps: receiving radar point clouds, and preliminarily distinguishing dynamic point clouds and static point clouds in a single frame; the dynamic point cloud and the static point cloud are processed respectively, and the absolute speed of the robot is obtained; receiving point cloud data through the laser radar, and performing time synchronization and space calibration on each frame of data of the millimeter wave radar and the laser radar; dynamic points in each frame are removed in real time, and laser radar point cloud data only containing static points and static millimeter wave radar point cloud data are obtained; geometric feature extraction is carried out on the static laser radar point cloud data, and whether the robot enters a degradation area or not is judged; laser radar or millimeter-wave radar point cloud data are selected according to the number of feature points of the laser radar point cloud for matching of scanned sub-maps; the pose and the speed of the robot are obtained through a GPS, the reliability of GPS data is judged through the speed estimated by the millimeter wave radar and the pose obtained by the front-end odometer, the weight of the GPS data in rear-end optimization is determined by calculating the residual error of the speed estimated by the millimeter wave radar and the pose obtained by the front-end odometer, and the weight is added into a factor graph to be jointly optimized with the front-end odometer.

The invention relates to the technical field of image processing, in particular to an irrigation prescription map inversion method based on unmanned aerial vehicle spectrum data. The method comprises the following steps: carrying out unmanned aerial vehicle data acquisition on a farmland area to obtain a multispectral image, DEM data and meteorological interpolation data; performing micro-landform parameter calculation according to the multispectral image, DEM data and unmanned aerial vehicle GPS data, and constructing a terrain-spectrum coupling model to obtain a terrain relief degree map and the terrain-spectrum coupling model; performing vegetation multi-dimensional state analysis by using a terrain-spectrum coupling model to obtain a vegetation index map, a canopy temperature map and a water stress preliminary judgment map; performing net radiation calculation by using the meteorological interpolation data to obtain a net radiation diagram; according to the net radiation diagram, the vegetation index diagram and the topographic relief degree diagram, soil heat flux estimation is conducted, and a soil heat flux diagram is obtained. According to the method, collaborative inversion is carried out by eliminating the terrain effect, and the precision and reliability of irrigation prescription map inversion are improved.

The invention discloses an integrated navigation method based on deep learning and improved Kalman filtering, belongs to the technical field of artificial intelligence and integrated navigation, and is used for processing integrated navigation data, including using GPS / INS integrated navigation when a GPS signal is available, and using angular velocity and acceleration output by an inertial measurement unit IMU to determine whether the GPS signal is available or not. Calculating and estimating the attitude, position and speed of the object, and fusing the attitude, position and speed with GPS data to obtain a final combined navigation result; and when the GPS signal is unavailable, predicting a pseudo position and a pseudo speed when the GPS signal is unavailable through the neural network model, and inputting the pseudo position and the pseudo speed into the adaptive square root volume Kalman filter for data fusion to obtain a predicted position and a predicted speed when the GPS signal is unavailable. According to the invention, the prediction precision of the neural network model is improved, the robustness of the filtering algorithm is optimized, and the method has real-time performance; the influence of noise on the system is reduced, and data fusion of the GPS and the INS and a high-precision solution are realized.

The disclosed invention provides a system and method for processing air traffic control (ATC) communications to ensure real-time validation of aircraft operations and improve pilot logbook accuracy. The system receives ATC voice communications through a digital radio system, converts them to text using a natural language processing model trained on aviation terminology, and extracts key operational data, including aircraft identifiers, instructions, and corrections. This data is correlated with Automatic Dependent Surveillance-Broadcast (ADS-B) and GPS data to verify compliance with ATC instructions. The system creates a continuous transcript of ATC-pilot communications, stitching together conversations across multiple frequencies. Deviations from ATC instructions are detected, generating alerts and logging the events. The method further provides automated reporting, archiving of communications, and enhanced safety metrics in immutable pilot logbooks, using blockchain technology for secure storage. This invention improves operational safety, transparency, and flight data integrity.

The invention discloses a dynamic public transport scheduling optimization system based on artificial intelligence, and the system comprises a mobile application program which is used for collecting the feedback of passengers, and displaying the real-time bus information, predicted arrival time, route suggestions and ticket price information to the passengers; the background management module is used for monitoring the state of the optimization system, adjusting a scheduling strategy and managing for feedback, and regularly maintaining and updating the optimization system; the data collection module is used for collecting vehicle GPS data, traffic flow monitoring data, weather conditions, special event information and historical and real-time data fed back in real time on social media; and the multi-modal traffic data fusion processing module is used for fusing the historical and real-time data collected by the data collection module and preprocessing the historical and real-time data. The invention also discloses a dynamic public transport scheduling optimization method based on artificial intelligence. According to the invention, the operation efficiency and the passenger satisfaction degree of public transportation are improved.

Disclosed herein is a method of transmitting a BSM message of V2X communication device provided in a vehicle in an autonomous driving system. The method of transmitting a BSM message of a V2X communication device according to an embodiment of the present invention may multiplex sensor data and GPS data received from in-vehicle sensors, respectively, and use the multiplexed sensor data to generate BSM data. As a result, it is possible to enhance stability of the V2X system and more efficiently correct errors or the like occurring between sensors.An autonomous vehicle according to the present invention may be linked with an artificial intelligence module, a drone (unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services and the like.

The invention relates to a positioning method and system based on adaptive multi-sensor fusion, belongs to the technical field of positioning, and aims to solve the problems of low positioning precision and poor stability in a complex environment in the existing positioning technology. According to the method, a VIW system and a GPS are deeply fused, and the advantages of all sensors are effectively integrated. The method comprises the following steps: firstly, evaluating, correcting and screening GPS data through an improved GPS precision factor model and an anomaly detection model based on fuzzy logic and statistical learning; and secondly, acquiring a local pose estimation result by using the VIW system, and realizing time synchronization of the GPS and the VIW system through a time difference compensation strategy. And finally, constructing an optimization objective function containing a VIW system residual term and a GPS residual term, and updating the transformation matrix of the VIW system and the GPS coordinate system. The method is suitable for the positioning requirements of intelligent robots, pilotless automobiles and other equipment in urban canyons, tunnels, indoor and long-distance and high-dynamic scenes.

The invention relates to the technical field of livestock health management and early warning, and particularly discloses a livestock health management and early warning method and system for animal husbandry based on the Internet of Things, and the method mainly comprises the steps: obtaining the GPS data and monitoring data of livestock, the GPS data comprising a timestamp and a GPS coordinate; calculating an activity distance of the livestock in a specified time period based on the GPS data; judging whether the activity distance belongs to a preset interval or not, and if yes, identifying the livestock from the monitoring data of the specified time period as the livestock to be monitored; and extracting behavior characteristics of the to-be-monitored livestock from the monitoring data of the specified time period, and judging state information of the behavior characteristics. According to the system, the Internet of Things and the data analysis technology are adopted, intelligent monitoring of the livestock activity state is achieved by obtaining the GPS data and the monitoring data of the livestock in real time, the activity and behavior characteristics of the livestock can be effectively monitored, abnormal conditions can be found in time, early warning is made, the burden of manual monitoring is reduced, and the overall efficiency of pasture management is improved.

The invention provides an urban road carbon emission calculation method for dynamically calibrating working conditions, which comprises the following steps: referring to an MOVES model, constructing basic carbon emission factors of working condition units of different vehicle types, calculating second-by-second working conditions based on small sample GPS data, dividing an optimal speed interval by adopting an improved NSGA-II algorithm, and obtaining working condition distribution of different speed intervals. And dynamically updating the working condition correction parameters of the carbon emission factor library. And on the basis of the dynamic carbon emission factor library, urban road dynamic carbon emission calculation is realized in combination with the travel speed and the statistical flow. According to the method, working condition correction factors of different speed intervals are dynamically calibrated through small sample GPS data, traffic state characteristics are reserved, meanwhile, the requirement for vehicle operation data during dynamic carbon emission calculation is lowered, and the problem that working condition data in dynamic characteristics of a carbon emission factor library are difficult to obtain in the past is solved.

The invention provides a traffic state data interpolation method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining original GPS data, carrying out the preprocessing of the original GPS data to obtain a preprocessed vehicle track data set, and generating a traffic speed tensor based on the preprocessed vehicle track data set; based on the traffic speed tensor, constructing a heterogeneous graph attention network model considering space and time factors in space and time perspectives; utilizing the heterogeneous graph attention network model to extract potential features of a space view angle and a time view angle, and performing feature fusion on the potential features to perform interpolation of missing traffic state data; and outputting an interpolation result of the traffic state data so as to realize accurate estimation of missing data. According to the invention, through fusion of space and time heterogeneous features, robust interpolation can be provided for the traffic state under different missing proportions, so that the traffic state can be predicted more accurately.

A battery control component of an electric vehicle may limit charging of a battery on the electric vehicle to a target state of charge (SOC), such as to reserve battery capacity for regenerative braking during downgrades. A computing device associated with the vehicle may calculate an estimated gradient profile and / or an estimated total energy consumption of a predicted upcoming route of the vehicle based on one or more data items, such as GPS data, altitude data, and / or map data. The computing device may determine a target SOC based on the estimated gradient profile. The computing device may determine a confidence factor associated with the estimated gradient profile, where the confidence factor depends on the quantity of data items used to calculate the estimated gradient profile. The computing device may adjust the target SOC based on the confidence factor.

The invention relates to a vehicle track recovery method, computer equipment and a storage medium. Single-source track data sets for at least two types of traffic perception data in GPS data, vehicle snapshot data and toll station gate data are constructed respectively; performing fusion processing on each single-source trajectory data set to obtain a first multi-source trajectory data set; cross-data-source matching is carried out on vehicle identities corresponding to trajectory fragments in the first multi-source trajectory data set based on road network space-time constraints, and a second multi-source trajectory data set is obtained according to a matching result; filling space-time missing information in the second multi-source trajectory data set based on road network space-time constraints, and obtaining a third multi-source trajectory data set according to a filling result; the problem of fragmentation of the multi-source vehicle track is solved, and the completeness of vehicle track recovery is improved.

The invention discloses an unmanned aerial vehicle delivery bidirectional authentication method and device based on implicit gait behaviors. A user places an order through a mobile phone delivery application program. After arriving at a designated place, the unmanned aerial vehicle hovers in the air, notifies a user of arrival of the unmanned aerial vehicle, and then establishes a key-protected communication channel with a mobile phone of the user. And the user holds the mobile phone and walks towards a specified position. And the unmanned aerial vehicle confirms the real-time position of the user through the shared GPS data, and searches the user by using a camera. Meanwhile, the unmanned aerial vehicle captures a user video and processes the user video in real time, and extracts a time sequence of user body key points; meanwhile, the mobile phone of the user continuously collects time series data from the IMU sensor. The two data are then exchanged between the drone and the user's mobile phone, thereby initiating a mutual authentication process. According to the invention, mutual identity verification is realized by using the hand movement mode when the user moves to the unmanned aerial vehicle, additional hardware or specific identity verification operation is not needed, and strong resistance to various spoofing attacks is shown.

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.

Methods, apparatuses, and computer-readable media for providing a pedestrian-oriented navigation app for a mobile device. Destination information is received from a pedestrian using a mobile device. The mobile device retrieves pedestrian-oriented mapping data and routing for the navigation of the pedestrian to the destination, determines a current location and direction of the pedestrian based upon GPS data and compass data retrieved from the device, and displays the current location and direction of the pedestrian along with a route for the pedestrian to the destination on the user interface of the mobile device based on the pedestrian-oriented mapping data and routing. If the mobile device detects that the pedestrian has deviated from the route, the mobile device alters the display on the user interface to alert the pedestrian to the deviation.

Techniques are described for using data capture devices at a building to automatically generate a building floor plan and to determine associated absolute location data for the generated floor plan, such as by associating separately captured GPS data or other absolute location data with the floor plan. In some situations, a building floor plan is automatically generated by analyzing visual data of images captured at multiple image acquisition locations by a camera device to determine room shapes of surrounding rooms, and GPS absolute location data is associated with the generated floor plan using additional data captured at other data capture locations at the building by a separate mobile device that moves independently from the camera device, such as by extending the absolute location data from the mobile device to the camera device's image acquisition location and its surrounding room shape.

A method for identifying, locating, and mapping targets of interest using unmanned aerial vehicle (UAV) camera footage in GPS-denied environments. In one embodiment, the method comprises obtaining UAV visual data, passing the UAV visual data through a convolutional neural network (CNN) in order to detect targets of interest based on visual features disposed in the UAV visual data, wherein the detection by the CNN defines reference points and pixel coordinates for the UAV visual data, applying a geometric transformation to known and defined pixel coordinates to obtain real-world orthogonal positions; and projecting the detected targets of interest onto an orthogonal map based on the obtained real-world orthogonal positions, all without GPS data.

The invention relates to the technical field of geo-fence triggering, in particular to a multi-mode positioning data fused GEO fence triggering method and triggering system. The method comprises the following steps that according to the distance between equipment and the fence boundary, a layered positioning strategy is adopted to demarcate a safe area, a transition area and a critical area; when the device is located in the safe area, outputting a coarse position coordinate of the device and a corresponding coarse positioning confidence coefficient by adopting a low-power-consumption coarse positioning algorithm; when the equipment is located in the transition area, fusing the GPS data and the IMU data by adopting an adaptive fusion algorithm to obtain an intermediate fusion position, an intermediate fusion speed and an intermediate positioning confidence coefficient; fusing the multi-modal data when the equipment is located in the critical zone, and predicting the probability that the equipment invades the fence by combining a trajectory risk assessment algorithm based on the equipment acceleration and the distance between the equipment and the fence boundary. Through hierarchical positioning strategy and multi-modal data fusion, the problems of insufficient precision and high power consumption of a single positioning mode in a complex scene are solved.

A system receives, from a plurality of connected vehicles, respective GPS data indicating an absolute location of a respective connected vehicle and on-board sensor data indicating locations of nearby vehicles and objects relative to the respective connected vehicle. The system processes the data to create a shared-world model that includes at least the locations of the plurality of connected vehicles and the nearby vehicles and objects. Some implementations of the shared-world model further include the speeds and trajectories of each of the plurality of connected vehicles and the nearby vehicles and objects. The system transmits a representation of the shared-world model to one or more of the plurality of connected vehicles, which may utilize the shared-world model to provide advanced warnings for drivers or to provide improved path planning for autonomous vehicles. Some representations of the shared-world model include lane-level traffic functions such as traffic density, traffic speed, and traffic throughput.

Methods, apparatuses, and computer-readable media for providing a pedestrian-oriented navigation app for a mobile device. Destination information is received from a pedestrian using a mobile device. The mobile device retrieves pedestrian-oriented mapping data and routing for the navigation of the pedestrian to the destination, determines a current location and direction of the pedestrian based upon GPS data and compass data retrieved from the device, and displays the current location and direction of the pedestrian along with a route for the pedestrian to the destination on the user interface of the mobile device based on the pedestrian-oriented mapping data and routing. If the mobile device detects that the pedestrian has deviated from the route, the mobile device alters the display on the user interface to alert the pedestrian to the deviation.

The invention relates to the technical field of online water quality intelligent monitoring scheme design, in particular to an online water quality intelligent monitoring method and system and electronic equipment. The method comprises the following steps: acquiring GPS data, water temperature data, PH data, dissolved oxygen content data and conductivity data of a target point location of a target water area within a preset duration according to a preset time interval through a GPS sensor, a water temperature sensor, a PH sensor, a dissolved oxygen sensor and a conductivity sensor which are arranged at the target point location of the target water area, and acquiring timestamp data; the online water quality intelligent monitoring model is trained, whether the water quality data of the target water area is abnormal or not is judged, overall online evaluation and judgment on the overall water quality data of the target water area are achieved, whether the overall water quality data of the target water area is abnormal or not is accurately judged, and the safety, reliability and intelligent degree of water quality detection are improved to a great extent. The intelligent degree and usability of the system are improved to a great extent, and the application scenarios of the system are greatly expanded.

A work machine includes a frame, a front axle coupled to the frame with front wheels, a rear axle coupled to the frame with rear wheels, and a work vehicle GPS receiver coupled with the frame. An agricultural implement is coupled via a connection mechanism to the frame. An implement GPS receiver mounted on the agricultural implement. A controller is coupled to each of the frame, the work vehicle GPS receiver, and the implement GPS receiver. The controller receives the work vehicle GPS data from the work vehicle GPS receiver and the implement GPS receiver data from the implement GPS receiver. The controller determines an implement profile of the agricultural implement and a shifted implement profile that corresponds to lateral and longitudinal distances of the implement GPS receiver relative to the connection mechanism.

A system and method attest the veracity of audiovisual depictions by inferring motion of the device recording those depictions from the audiovisual data, and comparing this inferred motion with contemporaneously sampled sensor data from that device, such as accelerometer or GPS data. If the inferred motion sensibly corresponds to changes in the other sensor data, then the video is determined to be authentic. Otherwise, an anomalous condition is signaled, altering the viewer that the video may have been digitally altered.

The embodiment of the invention provides a geographic information system (GIS) modeling method and device, electronic equipment and a medium, and the method comprises the steps: collecting video image data of a target area, recording the latitude and longitude coordinates of a collection position, carrying out the extraction of a key frame, carrying out the denoising processing, extracting the image feature points of the key frame, and carrying out the feature matching, determining a relative position between the key frames; the method comprises the following steps of: calculating a space coordinate of each image feature point by utilizing matched image feature points and GPS data of acquisition equipment, generating point cloud data, performing 3D Gaussian Splitting operation on each point in the point cloud data by applying a Gaussian weighting function to obtain an integral Splitting operation result, and reconstructing a three-dimensional model through the integral Splitting operation result to obtain a three-dimensional model. Smooth processing is carried out to optimize the surface quality of the three-dimensional model; and importing the reconstructed three-dimensional model into a GIS (Geographic Information System), managing model data by using a spatial database, and placing the three-dimensional model at a corresponding position in the GIS by using latitude and longitude coordinates.

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 discloses a near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data, and relates to the technical field of monitoring and early warning, and the method comprises the steps: building the point cloud data of a power line through collecting the image data and GPS data of the power line; coarsely sampling and finely sampling the point cloud data of the power line to obtain coarsely sampled data points and finely sampled data points; a real-time positioning device is installed on a crane to obtain three-dimensional positioning data of the crane, the accurate shortest distance from the crane to a power line is calculated based on rough sampling data points and fine sampling data points, multiple levels of early warning threshold values are set, the early warning threshold values are triggered in the shortest distance to give out corresponding early warning, and safe path planning is conducted. According to the near-electricity early warning method, the near-electricity early warning method with high precision, high stability and high real-time performance is realized by processing the point cloud data of the circuit and the three-dimensional positioning data of the crane.

In some implementations, a controller may obtain global positioning system (GPS) data from a GPS unit of an enrichment device. The controller may determine habitat information identifying a habitat for animals, of a plurality of habitats of animals, using the GPS data. The controller may determine a geofence of the habitat for animals based on the habitat information. The controller may determine a movement profile associated with the habitat for animals using the habitat information. The movement profile identifies one or more motions of the enrichment device. The controller may cause the enrichment device to move, within a boundary of the geofence, in accordance with the one or more motions of the movement profile. The enrichment device may move in accordance with the one or more motions to stimulate movement of one or more animals of the habitat for animals. The controller may monitor the movement and / or animal interaction.