299 results about "GPS signals" patented technology

The invention relates to the technical field of unmanned aerial vehicle positioning, discloses an unmanned aerial vehicle accurate positioning method and device fusing multi-source positioning data, electronic equipment, a computer readable storage medium and a computer program product, and is used for solving the technical problem that accurate and stable positioning of an unmanned aerial vehicle cannot be realized in an indoor weak texture scene in the prior art. The method comprises the following steps: acquiring initial positioning data of an unmanned aerial vehicle in real time, the initial positioning data being at least from two information sources; constructing a positioning fusion algorithm based on an unscented Kalman filtering algorithm, evaluating the credibility of initial positioning data from each information source in real time, and calculating a positioning covariance matrix; and adjusting the fusion weight of each initial positioning data based on the positioning covariance matrix, and calling a positioning fusion algorithm to perform weighted fusion on the initial positioning data of each information source to obtain fusion positioning data of the unmanned aerial vehicle. According to the method, the positioning accuracy and stability of the unmanned aerial vehicle in a scene in which GPS signals are lacked and environment textures are weak can be improved.

A drone station according to an embodiment of the present invention may comprise: a station main body (10) having, mounted thereon, a charging device (20) allowing a drone to land thereon so as to be charged with drone power; a cover (30) provided at the station main body (10) so as to open / close the charging device (20) while rotating around an axle (12); and a window panel (34) provided in the cover (30), and made of a nonconductor material to transmit a GPS signal therethrough.

The invention provides an unmanned aerial vehicle positioning method and system, an unmanned aerial vehicle and a medium, and the method comprises the steps: firstly, obtaining inertial motion data and visual perception data in real time, and continuously providing the inertial motion data through employing the characteristic that an inertial sensor is not limited by a signal environment, so as to avoid a positioning interruption problem when a positioning signal is weak; meanwhile, the spatial pose data is generated by performing pose analysis on the visual perception data, and the first position information is generated by combining the preset satellite map database in a matching manner, so that the defect of inertial navigation accumulative errors is made up. The inertial motion data, the spatial pose data and the first position information are fused and calculated to generate the second position information, and the second position information is converted into the GPS signal and accessed to the GPS system of the unmanned aerial vehicle, so that the error defect of a single sensor can be effectively inhibited, continuous and accurate positioning in a complex environment is realized, and the positioning accuracy is improved. The problem of application limitation of a traditional GPS positioning system in special environments with weak positioning signals and the like is fundamentally solved from the technical level.

The invention provides an intelligent positioning method and system for a vehicle rescue state, and relates to the technical field of vehicle rescue positioning. The method comprises the steps that after historical position information of a vehicle-mounted end is received, if a curve corresponding to the historical position information is analyzed to be a zigzag curve of which the amplitude and the vibration frequency exceed threshold values within a set time length, it is determined that the vehicle enters a GPS signal failure area. After a rescue request signal is received, a last effective positioning point, vehicle speed data and historical track information before failure are obtained, a predicted driving distance is determined by combining a vehicle speed and effective time, candidate paths are determined by combining historical tracks, the positioning point and a terrain path map, and a candidate position set matched with the predicted distance is determined on the candidate paths; and determining the nearest final to-be-rescued position in combination with the position information of the ground base station. According to the multi-dimensional information fusion positioning method, intelligent prediction positioning of the vehicle position can be realized under the condition of GPS signal failure, and a reliable position basis is provided for carrying out rescue work in time.

The invention discloses an intelligent surveying and mapping system for catenary equipment in a tunnel, which relates to the technical field of catenary engineering detection and comprises a multi-source data acquisition module, a multi-source data fusion module, an identification and parameter calculation module, a modeling and interaction module and a data management and application output module. According to the invention, by deploying the multi-dimensional sensor, the limitation of traditional manual inspection or dependence of a single sensor is broken through aiming at the environments of insufficient illumination, high dust concentration and the like in the tunnel, the laser scanner can penetrate through the dust to obtain the three-dimensional geometric framework of the equipment, and the Beidou positioning assembly is bound with the collection action in real time; the problem of positioning deviation caused by weak GPS signals in the tunnel is solved, the environment sensor group monitors parameters such as temperature, humidity and illumination in real time, and through a dynamic adaptation fusion coefficient formula, sensor data weight is calculated, original coordinate deviation is corrected, a four-dimensional fusion data set is constructed, and errors caused by environment interference or data isolation in a traditional method are solved.

The invention relates to the technical field of GPS interference source monitoring and troubleshooting, in particular to a civil aviation GPS interference source monitoring and troubleshooting method and system. The method comprises the following steps: acquiring track position data, attitude parameter data, GPS signal receiving data and interference detection performance parameters of a plurality of aircrafts, and establishing aircraft nodes and interference nodes; generating a common edge between any two aircraft nodes, generating a corresponding special edge between each interference node and the aircraft node within a preset influence radius, and constructing a space-time interference propagation topological graph at each moment; calculating an interference behavior characteristic parameter to obtain an interference behavior characteristic parameter vector, and calculating and outputting an interference behavior type identifier and an interference evolution trend vector corresponding to each interference node by using an interference behavior evolution trend inversion algorithm; and predicting an early warning interference source and generating an interference early warning result. According to the invention, real-time positioning, behavior identification and future influence range prediction of multiple types of interference sources in the civil aviation airspace are realized.

The invention provides a low-altitude multi-source equipment data base fusion method and system based on deep learning, and belongs to the technical field of low-altitude data processing. A multi-modal dynamic strategy generation engine is adopted to analyze multi-dimensional context information such as equipment modality, space environment and task intention and generate an optimal fusion strategy instruction; according to the present invention, adaptive adjustment fusion is carried out according to the current situation, when the GPS signal is interfered, the dependence on the GPS signal is automatically reduced, the weight of other reliable signal sources is enhanced, and when the optical sensor fails in the heavy fog weather, the data processing assembly line is dynamically reconstructed, the image processing micro service is bypassed, and the radar data is preferentially processed. The dynamic self-adaption and toughness anti-interference capability of the low-altitude multi-source data fusion process is improved, various interferences can be effectively resisted, high-precision fusion output is always kept in a complex and changeable low-altitude environment, data source selection and weight are flexibly adjusted according to a real-time dynamic environment and task requirements, and the real-time dynamic environment and task requirements are met. And the accuracy and the response speed of low-altitude multi-source data fusion are improved.

The invention discloses an indoor dynamic environment three-dimensional reconstruction method and system based on deep learning, and the method comprises the steps: employing SuperPoint and LightGlue to carry out the extraction and matching of feature points, carrying out the precise recognition of a dynamic target in combination with YOLOv11 and SAM technologies, and carrying out the implementation of an image pyramid feature selection strategy, and can uniformly reserve key feature points layer by layer, thereby achieving the precise recognition of a dynamic target. The feature points of the dynamic object are effectively eliminated, so that the efficiency is further improved, and finally, a high-precision, high-robustness and high-real-time visual synchronous localization and mapping (VSLAM) system for high-precision localization and mapping is realized in an indoor dynamic environment. The method is suitable for the fields of robot navigation, unmanned vehicles, indoor positioning and navigation and the like in indoor environments with weak GPS signals and more dynamic objects, such as large underground parking lots, shopping malls, office buildings and the like.

The invention discloses an autonomous positioning and obstacle avoidance method for ground-approaching flight of a fixed-wing unmanned aerial vehicle, and the method achieves the high-precision positioning through constructing a stereogram and carrying out the feature point matching with a pre-stored base map, and achieves the safe and efficient obstacle avoidance through employing a dynamic constraint hierarchical progressive trajectory planning method. The problems of navigation and obstacle avoidance under the conditions of limited GPS signals and dynamic change of obstacles in ground-approaching flight are solved, and the survivability and task execution efficiency of the unmanned aerial vehicle in a complex environment are improved.

The invention discloses a power transmission tower attitude monitoring and vibration intrusion identification method based on multi-source data fusion, and the method comprises the steps: carrying out the deep fusion of GPS displacement data and accelerometer inclination angle data, employing a dynamic weight fusion mechanism: adjusting a fusion coefficient alpha in real time according to the GPS signal quality, guaranteeing the accuracy and stability of data fusion, and at the same time, carrying out the real-time adjustment of the fusion coefficient alpha, and carrying out the recognition of the vibration intrusion of a power transmission tower. And continuously optimizing the error propagation model by taking the final inclination angle theta final of the power transmission tower as a state variable, updating the attitude estimation of the power transmission tower, and realizing + / -0.01-degree high-precision attitude monitoring. According to the invention, the system can output a high-precision three-dimensional attitude angle in real time by combining the rapid dynamic response of the accelerometer and the long-term stability of the GPS. Besides, an LCD-MPE (feature extraction technology) is introduced, lightweight neural network classification is combined, vibration signal sampling, noise reduction and abnormal signal segment identification, analysis and extraction are carried out on the transmission tower through an MEMS accelerometer, and second-level detection and behavior type discrimination of invasion behaviors around the transmission tower are realized. The core technical barrier from'inability perception 'to'accurate identification' is overcome, and subversive upgrading of power facility safety protection from'passive disposal 'to'active defense' is promoted.

The invention belongs to the technical field of satellite navigation, and particularly relates to a Beidou terminal performance closed-loop test system and method, and the system comprises a multi-system GNSS navigation signal simulation source which is used for simulating a test signal and evaluating the core performance of a navigation terminal in real time; the Beidou RDSS and short message test system is used for simulating RDSS outbound signals and completing capturing, tracking, analysis and parameter measurement of RDSS inbound signals; the satellite navigation signal forwarding system is used for receiving satellite signals and completing work such as machine testing, system debugging, time service and positioning; the portable data acquisition and playback system is used for acquiring four signals, namely, a BDS signal, a GPS signal, a GLONASS signal and a Galileo signal; and the microwave anechoic chamber is used for providing a wireless shielding test environment, supporting the rotary table to spin and simulating the running state of the missile-borne receiver. According to the invention, the comprehensive testing of the basic function performance of various navigation products, such as passive positioning, active positioning, position report and short message, can be realized, and the method has a good market application prospect.

The invention discloses an indoor and outdoor navigation method, device and equipment, and relates to the technical field of computers. The method is applied to an edge device, the edge device is carried on a robot, and the method comprises the steps that after it is detected that the robot is started outdoors, a first planned path is generated based on the initial position of the robot, an outdoor offline map and the indoor entrance position; when it is detected that the intensity of the entrance marker and the GPS signal at the indoor entrance position is smaller than an intensity threshold value, an indoor navigation mode is triggered; constructing an indoor coordinate system by taking the indoor entrance position as an original point; indoor motion data of the robot and image data of the environment where the robot is located are obtained through an inertial measurement unit and image acquisition equipment; determining a current indoor coordinate of the robot based on the indoor map, the indoor motion data and the image data; and generating a second planning path based on the indoor coordinate system, the current indoor coordinate and the target task point. According to the scheme, the navigation efficiency can be improved.

The invention discloses a position detection and cheating device and method for an unmanned aerial vehicle, and the device integrates a position information obtaining module, a GNSS signal receiving module, and a GNSS cheating signal generating and transmitting module, obtains the position of the unmanned aerial vehicle through a Wi-Fi network card or a Bluetooth receiver, and a USRP B210 device and related software collects and processes a GNSS signal and generates a cheating signal. The method comprises three processes of unmanned aerial vehicle position information detection, real GPS signal receiving and position deception signal generation, and the safety of the unmanned aerial vehicle is evaluated through multi-link analysis, signal receiving and simulation signal generation and emission. The unmanned aerial vehicle position and the GPS signal can be acquired in real time, the authenticity of the deception signal is improved, and the test effectiveness is enhanced; and the device has the advantages of high simulation degree, high precision, simplicity and convenience in operation, low cost and the like, and an efficient scheme is provided for unmanned aerial vehicle position safety testing and anti-deception capability evaluation.

The invention relates to the technical field of vehicle-mounted terminals, and discloses a vehicle-mounted terminal control method and system and a vehicle, and the method comprises the following steps: S1, multi-dimensional environment perception and scene judgment: collecting GPS signal intensity, Beidou signal intensity, mobile network signal intensity, vehicle altitude and environment illumination intensity through a perception module; through Beidou + inertial navigation integrated positioning + null shift calibration, the basement positioning precision is higher, the parking position monitoring and vehicle searching requirements are met, the three-level redundant link (mobile network + LoRa + Bluetooth) and distributed gateway design is adopted, the basement information transmission success rate is higher, emergency information is not interrupted, data security is guaranteed through full-link encryption, and the service life of the system is prolonged. Misjudgment of temporary weak signals on the ground is avoided through multi-dimensional sensing (signal + altitude + illumination + duration), the mode switching accuracy is higher, bandwidth waste is reduced through a priority dynamic adjustment mechanism, the emergency information response speed is increased, and link congestion is avoided through a common information caching strategy.

The invention relates to the technical field of satellite navigation and wireless communication, and discloses an indoor positioning method and system based on feeder multiplexing Beidou double-frequency, and the method comprises the steps: receiving outdoor B1I and B2a signals through a high-gain Beidou double-frequency receiving antenna, carrying out the frequency separation and signal conditioning through a double-frequency separation filter, and carrying out the positioning of the B1I and B2a signals; and transmitting the B1I signal to an indoor distributed antenna for broadcasting through the existing communication feeder network, and transmitting the B2a signal to a central directional antenna to establish a time synchronization reference. A mobile terminal receives a dual-frequency signal, capturing and tracking are carried out through digital signal processing, and pseudo-range and carrier phase observation values are extracted. And performing pairing processing on observation data of the same antenna, calculating a dual-frequency pseudo-range difference value, eliminating system errors, and performing position calculation by adopting a weighted least square method and Kalman filtering. The method solves the problem that the traditional GPS signal cannot cover the indoor environment, and has a good application prospect in the scenes of underground commercial complexes, tunnels, parking lots and the like.

A vehicle system includes a RTK hub, a vehicle, and one or more processing circuits. The RTK hub is configured to be positioned at a known location, and includes a first communications interface configured to facilitate acquiring a hub GPS position of the RTK hub based on a hub GPS signal acquired from a GNSS satellite. The vehicle includes a sensor configured to acquire a vehicle GPS position of the vehicle based on a vehicle GPS signal from the GNSS satellite and a second communications interface configured to facilitate communications between the vehicle and the RTK hub. The processing circuits are configured to determine corrective position data based on the hub GPS position and the known location, determine a corrective position of the vehicle based on the vehicle GPS position and the corrective position data, and control an operation of the vehicle based on the corrective position of the vehicle.

The invention discloses an unmanned aerial vehicle cluster autonomous behavior decision-making method in a high-risk police affair actual combat scene, and belongs to the technical field of police affair actual combat unmanned aerial vehicles. Starting an environment sensing system, collecting environment information through an optical camera in the multi-mode sensor unit, and carrying out noise reduction and correction on original data through a data preprocessing module; s2, the environment information obtained in the S1 is transmitted to a data fusion module, the data fusion module uses an extended Kalman filter to carry out fusion processing on data from different sensors, and a comprehensive environment model is generated; and S3, initializing a positioning and navigation system, compensating the GPS signal by a positioning correction unit based on a visual SLAM technology in combination with a geomagnetic navigation mode, and automatically switching to the geomagnetic navigation mode when the GPS signal is unavailable. According to the invention, more comprehensive environment information can be provided.

The embodiment of the invention provides a new energy inspection robot node positioning method and system, and the method comprises the steps: firstly carrying out the dual confidence degree dynamic evaluation of GPS and SLAM data, and quantifying the respective reliability in real time; and then, inputting the confidence into a fusion state machine decision engine, so that the fusion state machine decision engine can intelligently decide that the system should be in an optimal fusion state such as GPS leading, pure SLAM or GPS recovery transition. Finally, the state decision directly guides the adaptive adjustment of the measurement covariance matrix in the extended Kalman filtering, thereby dynamically adjusting the weights of different sensors at the algorithm level. According to the closed-loop design from state perception to strategy decision and then to parameter self-adaption, positioning impact caused by GPS signal sudden change can be effectively restrained, smooth transition during signal recovery is achieved, and therefore it is guaranteed that a positioning result has high precision, high continuity and high accuracy under various working conditions.

The invention belongs to the related technical field of construction safety patrol, and particularly relates to an unmanned aerial vehicle safety patrol method for a dianthus chinensis landform mountain construction area, which comprises the following steps: S1, preparation work before patrol: inspecting patrol equipment and confirming patrol environment conditions on the same day; wherein inspection of the patrol equipment comprises inspection of an unmanned aerial vehicle body and all parts, inspection of battery conditions, inspection of high-definition camera equipment and sensing equipment and inspection of communication and positioning conditions. According to the invention, the unmanned aerial vehicle equipment and the high-definition monitoring camera equipment are checked before the unmanned aerial vehicle carries out safety patrol, so that the situation that the safety patrol is difficult to complete smoothly due to equipment problems in the safety patrol process is avoided to a great extent; and meanwhile, the real scene information and the position information acquired in the safety patrol process can be ensured to be smoothly received by the monitored personnel by checking the transmission communication signal and the GPS signal before safety patrol.

The invention discloses a Beidou space-time signal frequency conversion compatible time service method, and belongs to the technical field of satellite navigation and time service, and the method comprises the steps: obtaining a Beidou space-time signal, monitoring the comprehensive electromagnetic characteristics of the Beidou space-time signal in a current environment, and generating a real-time environment fingerprint; matching the real-time environment fingerprint with an environment strategy knowledge base to obtain a current time service strategy; according to the current time service strategy, dynamically adjusting the receiving frequency and processing parameters of the Beidou space-time signal, and reconstructing a time service signal chain to obtain a reconstructed Beidou time service signal; and reconstructing the Beidou time service signal to establish a unified space-time grid, projecting the pre-received GPS signals of the plurality of GPS systems to the unified space-time grid for alignment correction, and generating a fusion time service result. Signal chain dynamic reconstruction combining environment perception and strategy matching is adopted, and multi-system fusion based on a unified space-time grid is adopted, so that the precision and robustness of time service in a complex electromagnetic environment can be improved.

The invention discloses a driving control method and device for a photovoltaic module installation robot. And generating a global point cloud map of the mounting bracket of the photovoltaic module through a laser mileage calculation method. The method does not need to depend on pavement marks or GPS signals, realizes high-precision map construction of a complex environment, and has relatively high environmental adaptability and anti-interference capability. Acquiring a target area where the mounting bracket is located based on the global point cloud map, extracting a linear contour of the mounting bracket in the target area based on the point cloud map of the target area, and determining a main linear contour; the efficient and accurate identification of the mounting bracket is realized, and the obstacle interference is effectively eliminated. And a driving path is generated in combination with the main linear contour and the position of the photovoltaic module installation robot, and the photovoltaic module installation robot is controlled to drive according to the driving path. A single and fixed preset path is not adopted, so that the photovoltaic module mounting robot accurately generates a path in a complex and changeable environment and stably runs along the edge of the bracket.

The invention discloses a sparse GPS map matching method based on confidence and intelligent fragment retrieval, relates to the technical field of map data processing, and realizes real-time adaptive adjustment of weights along with scenes by constructing a three-dimensional dynamic confidence model fusing road complexity, track point quality and historical matching popularity; by constructing a quaternary composite trigger logic of a confidence threshold value + track point spacing + road section topology complexity + GPS signal quality, false triggering or missing triggering caused by a single threshold value is avoided; data reliability is complemented by establishing a credibility scoring system (fusing matching success rate, use frequency and time freshness) of historical track fragments and performing two-dimensional sorting according to similarity and credibility during retrieval; the method does not need multi-sensor fusion and GPU computing power support, only depends on GPS single-sensor data, realizes balance of high-precision completion and low-redundancy calculation through a dynamic strategy, and adapts to resource-limited scenes such as vehicle-mounted edge equipment.

The invention discloses a GPS-free unmanned aerial vehicle autonomous navigation method based on multi-modal perception and reinforcement learning, and aims to solve the problem of obstacle avoidance failure caused by insufficient perceptibility of a transparent or reflective material. The method comprises the following steps: constructing a dual-channel occupancy graph containing an entity occupancy probability and a permeable layer probability, estimating a local plane normal in a suspected area and establishing an anisotropic safety barrier aligned with the normal, setting normal and tangential speed upper limits, combining safety reinforcement learning active verification and sensor parameter self-adaption, and cooperating with Bayesian updating and local path planning to obtain an anisotropic safety barrier. Reliable identification and safe detour of transparent or reflective obstacles are realized, the collision risk is reduced, and the robustness and stability of autonomous navigation in an environment without global satellite positioning signals are improved.

The invention provides a wireless distributed data synchronous acquisition method and system based on GPS time service. Each acquisition unit receives a GPS signal, acquires a GPS pulse per second signal and real triggering time thereof, and calculates and calibrates the drift rate of a local clock, so that the local clock is homologous with a GPS clock; after the fixed delay is calibrated, structural response data are collected, an original timestamp is generated, and a calibration timestamp is obtained by combining the fixed delay and drift rate correction. And the central computer generates a global unified time axis on the basis of the calibration timestamp, maps the data of each acquisition unit through linear interpolation and outputs a synchronization result. The system does not need wiring, is high in synchronization precision, and is suitable for structural health monitoring of large complex projects such as super high-rise buildings.

The invention discloses a robot motion intelligent control method and system based on machine vision, and relates to the technical field of robot motion intelligent control. Texture details such as ground cracks, splicing seams and special marks can be fully utilized through a visual image acquisition and processing module and a ground texture detail extraction module; and a more stable visual feature basis is formed. Compared with a traditional scheme depending on a GPS or an IMU, the design can still keep high positioning precision and stability in the environment where GPS signals are lost and IMU drifting accumulation is serious, and deviation and misjudgment of the direction of the robot in a narrow space or a repeated track task are reduced. The landmark matching and feature point screening module not only extracts feature points, but also performs stability screening on the feature points in combination with a texture direction consistency index, eliminates weak feature points in an unstable region, and outputs a high-confidence landmark matrix; and the robot can perform continuous self-positioning and track keeping by means of reliable landmarks.

The invention provides a multi-source heterogeneous sensor adaptive fusion positioning and mapping method and device, and relates to the technical field of automatic driving and mobile robots. The method comprises the following steps: for a mobile robot in an indoor environment, acquiring sensor data through a sensor data acquisition module, inputting the sensor data into a front-end data processing module to obtain processed sensor data, and inputting the processed sensor data into an environment scene recognition module to obtain an environment scene type recognition result; and inputting the result into a back-end state estimation and optimization module to obtain a map construction and pose estimation result. Through innovative front-end feature extraction, a rear-end optimization mechanism and a multi-sensor fusion strategy, the problems in the prior art are solved. By introducing a low-cost magnetometer, the robustness of the system in a GPS signal missing or complex electromagnetic environment is enhanced, meanwhile, the calculation efficiency and precision in different environments are improved through a self-adaptive optimization mechanism, and finally, high-precision and high-robustness autonomous positioning and mapping functions are achieved.

The invention discloses a complex sea condition unmanned ship path planning method and system based on a cost grid map. The method and system are suitable for water area navigation tasks of complex environments including ocean currents, wind wave interference, unstable GPS signals and the like. According to the method, firstly, a navigation area is dispersed into a two-dimensional grid map, multi-source environment information such as ocean current velocity, wind speed, GPS signal intensity and visibility is fused, and a comprehensive path cost function is constructed; on the basis, the motion state of the unmanned ship is modeled into a triple containing the position and the course, and course change cost is introduced to represent the steering cost of the unmanned ship. In the path search process, a weighted A * algorithm is adopted, and a multi-target path cost function and a heuristic estimation function are combined to realize efficient path search. According to the method, high-risk areas can be effectively avoided, the steering frequency is reduced, the energy consumption of the unmanned ship is reduced, the safety and feasibility of path planning are improved, and the method has good engineering applicability and robustness.

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 vehicle driving track prediction method, device and equipment and a vehicle, and is applied to the technical field of Internet of Vehicles communication, and the method comprises the steps: obtaining track attribute time sequence data of a target vehicle, driver time sequence data and current vehicle data; performing prediction processing according to the trajectory attribute time sequence data to obtain an initial driving trajectory of the target vehicle in a future time period; performing recognition processing according to the driver time sequence data to obtain driving emotion data of the target vehicle; performing identification processing according to the current vehicle data to obtain driving intention data of the target vehicle; and according to the driving emotion data, the driving intention data and the initial driving track, obtaining a target driving track of the target vehicle in the future time period. According to the invention, the prediction of the vehicle driving track does not only depend on a single GPS signal, so that the prediction precision of the vehicle driving track can be effectively improved.

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.