1634 results about "Position fixing" patented technology

The invention discloses a quadruped robot inspection method based on multi-modal sensing fusion. The method comprises the following steps of: 1, initializing a global task, loading a basic navigation map, and generating the basic navigation map comprising topographic features, forbidden areas and parking positions; 2, autonomous navigation and dynamic correction of positioning deviation are realized according to laser radar SLAM data, and the positioning deviation is dynamically corrected according to real-time observation data; 3, dynamically switching or adjusting the gait strategy according to the terrain category, training the gait strategy of the quadruped robot to be matched with the terrain category, and generating a self-adaptive motion control instruction to adapt to the terrain in real time; and step 4, synchronously realizing parking area structured data acquisition and dynamic abnormal information perception through multi-sensor fusion, and realizing target state monitoring and abnormal event response in the inspection task. According to the invention, through collaborative innovation of the bionic motion platform and multi-mode intelligent detection, all-terrain coverage, total-factor perception and full-process autonomous intelligent inspection in a complex parking lot environment is realized.

The invention provides a road inspection target positioning method based on multi-source data fusion, and relates to the field of road daily inspection and infrastructure performance monitoring, and the method comprises the following steps: obtaining multi-source data based on a preset multi-source sensor; through deep coupling of satellite positioning carrier double-difference observation and inertial navigation solution of an inertial sensor, error correction is carried out in combination with Kalman filtering recursion, and fusion navigation data is determined; according to the fused navigation data, carrying out fusion processing on the two-dimensional visual observation data and the three-dimensional space point cloud data to obtain a forward view depth map; determining the national geodetic coordinates of the disease by combining a depth value back calculation method and utilizing a forward view depth map; and obtaining a starting kilometer pile number in a road inspection process, and determining a mileage pile number of the disease by combining the national geodetic coordinate of the disease and utilizing a Gaussian positive algorithm. According to the invention, centimeter-level geographic coordinate positions of diseases and corresponding mileage stake numbers can be output.

The invention discloses a security guarding method and system based on a communication-guide-remote fusion technology, and the method achieves the self-adaptive selection and redundant transmission of communication links in a weak network, shielding and other environments through the construction of a multi-link communication system, an indoor and outdoor integrated high-precision positioning mechanism and a multi-mode intelligent sensing cooperation mechanism. Cross-scene continuous positioning is realized by combining multi-source information such as GNSS, inertial navigation, geomagnetism and remote sensing image backbone maps, and inertial navigation, video, sound and environment sensing data are fused to generate an abnormal behavior recognition result, so that the whole-process and whole-scene safety guarding capability for all personnel is formed, and the accuracy of abnormal behavior recognition is improved. The problems of easy communication interruption, easy positioning misalignment and unreliable abnormity identification in the prior art are effectively solved, and the method is suitable for a plurality of application scenes such as crowd safety guarding and the like.

The invention discloses an intelligent navigation method and device of an unmanned aerial vehicle, equipment and a storage medium. The method comprises the following steps: acquiring point cloud data and motion data, and calibrating to obtain a relative position and time synchronization parameter; performing map construction on the point cloud data and the motion data to generate a three-dimensional point cloud map; analyzing the boundary coordinates of the flight area, the flight height and the route step length, and generating route planning parameters; the boundary of the target inspection area is defined by combining the three-dimensional point cloud map, and an optimized route and corresponding waypoint sequence data are generated through route planning; acquiring real-time pose data of the unmanned aerial vehicle, comparing the current position with the waypoint sequence, and triggering image acquisition when a preset waypoint reaches a tolerance range; inspection data is obtained; and integrating the real-time pose data, the three-dimensional point cloud map, the flight state and the inspection data to form an intelligent navigation route. According to the invention, high-precision positioning and linkage of waypoints and data acquisition are realized, and the navigation precision, the inspection efficiency and the operation safety are improved.

The invention belongs to the crossing field of space geodetics, satellite navigation positioning technology and atmospheric science, and discloses a real-time troposphere delay modeling method for enhancing a global forecasting system. The method comprises the following specific steps: firstly, acquiring zenith fluid static delay and zenith wet delay predicted by earth surface GFS, and constructing a regional grid difference time sequence by taking a fusion result of ERA5 data and a GNSS troposphere delay product as a reference; secondly, fitting the difference to form a GFS enhanced core model; meanwhile, a troposphere delay correction model from the grid height to the target site height is constructed based on ERA5 stratified atmosphere data, time sequence modeling is carried out on model coefficients, and finally refined troposphere delay output at different heights is achieved. The method has the advantages that (1) the real-time troposphere model is high in precision; (2) real-time application can keep robust performance without depending on a GNSS network; (3) expansion and deployment are facilitated; and (4) the real-time performance is strong, and seamless integration precision positioning application can be realized.

The invention discloses an AGV navigation system used in a storage dynamic environment, relates to the technical field of storage robot control, and is used for solving the problem of poor stable navigation in dynamic storage. According to the method, a closed-loop navigation link of state convergence, confidence evaluation, corridors, setting generation and cooperative gating is constructed, state frames are packaged in unified time, and positioning confidence and availability marks are generated based on the consistency of pose predictive quantity and pose observed quantity, so that credible hierarchical control is realized in shielding, light reflection and personnel forklift passing scenes, and the positioning accuracy is improved. A time sequence occupation constraint set is formed through obstacle tracking, a forbidden area mark is fused, a passable corridor boundary is generated, a speed set value sequence and a curvature set value sequence are generated in a corridor, acceleration change rate constraint and steering change rate constraint are applied, and stable driving control and steering control instructions are output. And the way-giving sequence and the recovery sequence are matched, and the execution state and the occupation state are written back, so that the navigation safety, the stability and the operation continuity are improved.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.

The invention discloses a positioning method, device and equipment based on ionosphere error correction and a medium, and the method comprises the steps: obtaining a real-time observation data set of a target CORS system, calculating the real-time ionosphere delay of each observation satellite in each reference station, and obtaining a real-time ionosphere delay set; calculating longitude and latitude correction parameters of each reference station through the real-time ionosphere delay set and longitude and latitude change rate reference coefficients pre-acquired by each reference station, and updating a pre-acquired original ionosphere correction model through the longitude and latitude correction parameters to obtain a new ionosphere correction model; and generating a corrected ionosphere error through the new ionosphere correction model, and correcting positioning data acquired by the target CORS system through the corrected ionosphere error. According to the method, the problem that the change rule of the ionosphere on the longitude and latitude is ignored in the traditional positioning technology can be solved, so that the accuracy of the CORS system satellite positioning is improved.

The invention discloses an adaptive filtering Beidou navigation and inertial navigation positioning and attitude determination method and device, and the method comprises the steps: constructing a short-baseline carrier phase and pseudo-range double-difference observation equation through BDS satellite second pulse signals received by a first BDS receiver and a second BDS receiver, and solving the short-baseline carrier phase and pseudo-range double-difference observation equation to obtain the positioning information of a carrier where a second antenna is located; assigning the positioning information of the carrier to the inertial navigation system as an initial positioning value, carrying out positioning and attitude determination on the carrier through the inertial navigation system based on the initial positioning value, and outputting the latest positioning and attitude determination information of the carrier; carrying out Kalman filtering processing on the positioning information of the carrier and the latest positioning and attitude determination information of the carrier, and outputting the optimal attitude and positioning information of the carrier; the Kalman filtering is used for adaptively adjusting noise by using a carrier state vector through innovation analysis; the state vector is constructed by using the positioning information of the carrier and the latest positioning and attitude determination information of the carrier. The problem of error accumulation in the navigation process can be effectively solved, and the positioning precision is improved.

The invention provides three-dimensional vehicle-mounted navigation equipment and a navigation method based on the equipment. The method comprises the following steps: carrying out equipment initialization and reference calibration, arranging communication equipment on a vehicle roof, arranging acquisition equipment on a vehicle head grid, and arranging a calibration and calculation module in a vehicle cabin; synchronously acquiring multi-source scene and body state data by using hardware; identifying an operation scene through a CNN-LSTM hybrid model, and dynamically allocating a sensor fusion weight based on a reinforcement learning algorithm; a BP neural network is utilized to construct nonlinear mapping of the vibration frequency and the positioning error, and a dynamic error value is calculated; the dynamic error is substituted into extended Kalman filtering for real-time correction, and vibration influence is eliminated; the method comprises the following steps: pre-judging a GNSS signal trend by using an LSTM model, and initializing a backup module in advance to realize multi-mode seamless switching; dynamically adjusting map updating frequency according to environment change and compressing data by using an octree; and outputting a three-dimensional navigation instruction on the display screen. The problems of high-precision positioning and smooth switching of the engineering vehicle under strong vibration and complex scenes are effectively solved.

The invention discloses a Beidou user machine high-precision time service and positioning method based on a low-orbit high-stability reference, and relates to the field of vehicle-mounted electronic communication. The method comprises the following steps: firstly, receiving a low-orbit satellite time service signal through an SBOX, generating a high-stability time service reference signal through anti-interference enhancement and delay compensation, and transmitting the high-stability time service reference signal to a Beidou user machine; the Beidou user machine calibrates drifting and fluctuation of an original time service signal of the Beidou user machine according to a low-orbit high-stability time service reference, Beidou high-precision and low-orbit high-stability time service output is achieved through double-source fusion, and finally high-precision positioning calculation is completed and packaged into CAN data; and finally, the information is transmitted to a vehicle-mounted information unit through a vehicle-mounted CAN bus, so that navigation and position reporting are realized. According to the invention, the high stability of the low earth orbit satellite and the high precision of the Beidou are complemented and cooperated, the time service precision and continuity are considered through Kalman filtering fusion and extended Kalman positioning solution, the problems of insufficient stability and poor equipment collaboration of a traditional Beidou user machine are solved, and the high stability and precision requirements of military and commercial use are met.

The invention relates to a TDCP enhanced visual inertial odometer and a navigation positioning method thereof, the method aims at multi-sensor fusion positioning, uses an extended Kalman filter (EKF) to integrate visual features, IMU data and TDCP observation values, improves local pose estimation precision, and suppresses VIO error accumulation through absolute scale and course constraint provided by TDCP; meanwhile, the local estimation result and the global position of the GNSS are optimized and fused through a global pose map, and high-precision pose estimation which is globally consistent and within a global range is achieved. According to the method, optimization can be completed only through intermittent global positions, the problem of positioning failure of a traditional scheme in a GNSS challenge environment is solved, the problems of cycle slip accumulation and gross error interference when TDCP is independently used are effectively solved, and the method is suitable for automatic driving, unmanned aerial vehicles and other scenes with high real-time performance requirements.

Localization and navigation systems and techniques are described. An electronic device comprises a processor configured to maintain a state vector storing estimates for a position of the electronic device at poses along a trajectory within an environment along with estimates for positions for one or more features within the environment. The processor computes, from the image data, one or more constraints based on features observed from multiple poses of the electronic device along the trajectory, and computes updated state estimates for the position of the electronic device in accordance with the motion data and the one or more computed constraints without computing updated state estimates for the features for which the one or more constraints were computed.

The invention discloses a dynamic optimization method for a USV-AUV collaborative navigation network topological structure based on a consensus algorithm, and relates to the technical field of collaborative navigation of an ocean unmanned system. Comprising the steps that according to a positioning equation between the unmanned surface vehicles and the autonomous underwater vehicles, the relation between the positioning error of the autonomous underwater vehicles and the distance measurement error between the unmanned surface vehicles and the distance measurement error between the autonomous underwater vehicles and the position error between the unmanned surface vehicles is constructed through a partial differential method; designing a positioning precision evaluation function by using an optimal estimation criterion; solving an optimal solution for the evaluation function by using an alternating direction multiplier method, and performing consistency estimation on a formation center and adjusting dynamic formation parameters through a consensus algorithm to obtain an optimal topological structure; and acquiring the number of the formation aircrafts in real time, if the number of the formation aircrafts changes, performing iteration, and if the number of the formation aircrafts does not change, keeping a topological structure. The method is high in dynamic adaptability, constraint collaborative optimization is achieved, the safety margin is improved, and robustness is enhanced.

The invention discloses an infant airway management system and method based on navigation and high-precision dynamic registration, and the method comprises the steps: constructing a personalized airway deformation model through a physical constraint-based few-sample transfer learning network, generating an optimal instrument matching scheme, and providing a semantic weighted tight coupling visual inertial odometer; the confidence coefficient of feature points is dynamically adjusted by segmenting airway anatomy semantics in real time, data are fused under a factor graph optimization framework, and sub-millimeter robust positioning is achieved. Based on the high-precision trajectory, the system introduces a space-time attention fusion network, captures a long-range time sequence dependency relationship between operation micro-actions and physiological parameters, calculates a real-time dynamic risk score, and compensates airway soft tissue deformation in real time through a non-rigid deformation field model. The technical problems that an existing navigation system is prone to being lost in a microcosmic dynamic environment and risk early warning lags behind are effectively solved, and the safety and the success rate of the infant airway management operation are remarkably improved.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The embodiment of the invention provides a navigation state synchronization method, device and equipment and a computer storage medium, and belongs to the technical field of positioning. Receiving first GNSS satellite data, including a first timestamp; searching first IMU data corresponding to the first timestamp from an IMU data queue; carrying out fusion calculation, and determining a real-time navigation state under the first timestamp; moving a queue head position of the IMU data queue to second IMU data, and initializing an increment integral state; performing incremental integral calculation based on the second IMU data and a target IMU data set in the IMU data queue to obtain a real-time incremental pre-integral; and performing navigation state calculation according to the real-time navigation state and the real-time increment pre-integration under the first timestamp, and determining the current navigation state. According to the method disclosed by the embodiment of the invention, the purposes of accurately fusing navigation combinations and improving the navigation accuracy are achieved.

The invention discloses an engineering surveying and positioning method and system based on Beidou. The method comprises the following steps: S1, establishing an oil field operation environment three-dimensional model, and performing satellite visibility and multipath prediction; according to the invention, by constructing a multi-path geometric model and a dual-antenna array discrimination and weighted suppression algorithm, reflection interference caused by a metal facility dense environment in a well site and a station site is effectively weakened; by fusing a local DEM terrain model and earth surface reflection characteristics and introducing vertical Kalman filtering, the stability and precision of Beidou elevation calculation are significantly improved; under the scene of insufficient visibility such as shielding of a drilling machine and large equipment, continuous and high-reliability positioning capability is kept through a tight coupling fusion strategy of IMU and vision assistance, so that high-robustness and high-precision Beidou engineering measurement positioning can be realized in a complex environment of an oil field.

A monorail hoist positioning method combining rail joint beacons and inertial navigation. An odometry kinematic model for a monorail hoist is established by means of an encoder on the monorail hoist, and an extended Kalman filter is used to fuse a heading angle obtained from a kinematic model solution and a heading angle obtained from a strapdown inertial navigation solution. Beacons are arranged at rail joints for the monorail hoist, and vibration signals are acquired by a vibration sensor. By combining a strapdown inertial navigation system, the encoder, and a rail installation diagram, a rail joint determination model is constructed to perform filtering, so as to obtain impact signals at rail joint beacons that meet a requirement. Beacon identifiers are used to match the identified rail joint beacons against specific beacon coordinates in the rail installation diagram, so as to obtain a time-position coordinate sequence for the monorail hoist arriving at the rail joints. A combined positioning model integrating rail joint beacons and strapdown inertial navigation is constructed to realize integration of local relative positioning and global absolute positioning of the monorail hoist. The method can improve the positioning accuracy of the monorail hoist while reducing positioning costs.

Mechanism on location verification is proposed. One or more devices at known location(s) are deployed for verifying location information of a UE. The one or more devices perform a positioning measurement and report the positioning measurement. A core network device verifies the location information based on the reported positioning measurement and positioning measurement from the UE. In this way, it ensures that the location provided by the UE is trusted.

The invention discloses a target positioning method and system based on target height correction monocular distance measurement, and the method comprises the steps: carrying out the calibration of a ship-borne camera through a data set, employing a YOLO target detection model, carrying out the detection of video data collected by the ship-borne camera, and obtaining the bounding box information of a target; and secondly, according to bounding box information, calculating a yaw angle of the target relative to a main point of the shipborne camera, and obtaining an azimuth angle by combining an orientation angle of the main point of the shipborne camera. And then height correction is carried out to measure the distance between the target ship and the shipborne camera. And finally, according to the azimuth angle and the distance, combining the latitude and longitude coordinates of the shipborne camera to obtain the actual geographic latitude and longitude coordinates of the target ship. According to the method, the goodness of fit between distance measurement and actual image geometry is improved, the actual object distance is reflected more accurately, and accurate and efficient target positioning is carried out.

The present application discloses a positioning method and apparatus, which are used to achieve positioning of any one of two ends of an established sidelink relative to the other end, thereby realizing a fast positioning solution for sidelink scenarios. The positioning method according to the present application includes: transmitting a first positioning reference signal to a peer end for which a sidelink with a local end has been established, and determining a first positioning measurement quantity by performing positioning measurement on a second positioning reference signal transmitted by the peer end; and obtaining a second positioning measurement quantity transmitted by the peer end, where the second positioning measurement quantity is obtained by the peer end by performing positioning measurement on the first positioning reference signal; and determining positioning information by using the first positioning measurement quantity and the second positioning measurement quantity.

The invention discloses an adaptive positioning correction method for a tunnel environment, and the method comprises the steps: preliminarily restoring the position of a mobile device to a tunnel axis through inertial navigation, and employing inherent static features (such as a cable support, a fireproof door, and a duct piece joint) in a tunnel as a reference; and the automatic correction of the SLAM accumulative error is realized by measuring the distance from the feature object. And meanwhile, an intelligent filtering algorithm is adopted to effectively suppress dynamic object interference, so that the positioning precision and the system robustness are remarkably improved, and reliable technical support is provided for intelligent operation and maintenance of the cable tunnel.

The invention discloses a high-precision satellite attitude correction method, system and equipment for a maximum initial positioning error, and belongs to the technical field of machine vision image matching. A proper reference region is selected to prepare a first reference image and a second reference image, and feature descriptors of the reference images are extracted offline; performing down-sampling on a remote sensing image acquired by a satellite remote sensing camera, and performing image matching on a real-time image after down-sampling and the first reference image; calculating the optimal window position of the ground calibration point of the reference area in the real-time image according to the matching result; cutting the real-time image according to the window position to obtain a high-resolution calibration point real-time image; performing image matching on the high-resolution calibration point real-time image and the second reference image; and calculating latitude and longitude coordinates of the center of the real-time image according to a matching result for satellite attitude correction. The position error of the target area is gradually reduced in a two-stage remote sensing image matching mode, the longitude and latitude coordinates of the center of the real-time image are accurately positioned, and high-precision satellite attitude correction is realized.

The invention discloses an indoor positioning track tracing and area early warning method and system, and relates to the technical field of indoor positioning, the method comprises the following steps: receiving a positioning signal, and synchronously collecting inertial navigation data of an inertial measurement unit; filtering processing is carried out on the positioning signal, non-line-of-sight interference and multipath effect interference components are identified, and interference compensation parameters are generated; after a positioning signal is calibrated by combining the parameters, real-time position coordinates are calculated; running a pedestrian dead reckoning algorithm on the basis of inertial navigation data to obtain continuous relative position coordinates, and correcting by taking the real-time position coordinates as reference to form initial movement track data; presetting an early warning area boundary and a triggering condition, and if the condition is met, starting an early warning response and recording an early warning event; and integrating the continuous real-time position coordinates, the initial movement track data and the early warning event records, and constructing a complete movement track file to realize track tracing. The system is matched with the method, the positioning reliability in the indoor complex environment can be improved, and continuous track tracing and accurate area early warning are supported.

The invention provides a variational Bayesian adaptive iterative Kalman filtering positioning method and system based on LiDAR and IMU in complex weather, aiming at non-Gaussian measurement noise caused by residual noisy points after LiDAR point cloud denoising in complex weather conditions and limitation of a fixed noise covariance matrix in traditional Kalman filtering. Belongs to the technical field of automatic driving. According to the method, LiDAR feature points and IMU original data are directly fused instead of loosely coupled pose fusion, so that the robustness of state estimation is improved; in order to overcome the problem of negative definite or non-positive definite possibly occurring in a classical adaptive filtering method and meet the requirements of adaptive capacity to time-varying noise, high positioning precision and the like, a variational Bayesian method is adopted to approximate noise point distribution. The method is suitable for the automatic driving technology, the positioning precision under the complex weather condition is remarkably improved, and important technical support is provided for achieving the positioning technology under the complex weather condition.

The invention relates to the technical field of vehicle safety monitoring, and discloses a safety detection early warning method and system based on vehicle OBD data and a medium, and the safety detection early warning method based on the vehicle OBD data comprises the steps: collecting vehicle OBD real-time data, GPS positioning track data and environment data, and carrying out the preprocessing to form a time-space aligned multi-dimensional data set; oBD time position three-dimensional correlation features are extracted through a tensor decomposition technology; learning a mapping relation between OBD parameter change modes and safety risks in different driving scenes; constructing a space-time semantic association graph, and performing high-risk region identification through a graph convolutional network; according to the current driving situation, dynamic weighted fusion of the OBD data and the environment information is achieved, and graded early warning information is generated; a risk assessment method with context adaptability is provided, three-dimensional correlation features are extracted through a tensor decomposition technology, and complex interaction among parameters is effectively captured.

The invention discloses an AGV navigation technology based on a normal-temperature quantum sensing chip, and belongs to the crossing field of AGV navigation and quantum sensing technologies. According to the technology, environment magnetic field characteristics or passive magnetic field marking signals are captured through a normal-temperature quantum sensing chip, and high-precision autonomous navigation of the AGV is realized by combining magnetic field map construction, multi-modal data fusion and a dynamic path planning algorithm. The system mainly comprises a normal-temperature quantum sensing module, a magnetic field characteristic processing unit, a path planning system and a positioning correction module, does not need to depend on entity guide media such as a magnetic stripe and a two-dimensional code, and solves the problems of poor flexibility, high maintenance cost and weak interference resistance of traditional fixed path navigation. Experiments show that the positioning precision of the technology can reach the millimeter level, the technology adapts to complex environments such as industrial workshops and warehousing, route adjustment does not need ground modification, the flexibility and stability of AGV navigation are remarkably improved, and the technology is suitable for multi-scene application such as flexible production and intelligent warehousing.

The invention relates to the technical field of high-precision positioning, and discloses an anti-vibration interference optical fiber inertial navigation and vision fusion positioning system, which comprises a data acquisition module, a first identification module, a positioning compensation module, a second identification module and a positioning correction module, wherein the data acquisition module is used for acquiring inertial navigation data and a positioning image sequence of an aircraft; the first identification module is used for identifying interference fluctuation of the inertial navigation data; the positioning compensation module is used for constructing an attitude constraint curve and performing constraint compensation on the inertial navigation data with interference fluctuation; the second identification module is used for identifying course drift of the inertial navigation data; and the positioning correction module constructs a course reference sequence based on the positioning image sequence, and performs course correction on the inertial navigation data with course drift. According to the invention, the positioning precision of the aircraft under vibration, shaking and complex maneuvering conditions is improved.

The invention belongs to the field of computer vision, particularly relates to a riverway garbage recognition and positioning method, system and equipment based on deep learning, and aims to solve the problems of low efficiency, high cost, inaccurate positioning, low intelligent degree and the like. The method comprises the following steps: collecting multi-source image data of fixed monitoring and unmanned aerial vehicle inspection, and extracting physical characteristics such as water surface flow velocity; and inputting the preprocessed data into a double-track deep learning model in parallel, fusing a detection result and a feature vector through a space-time correlation and coordinate mapping mechanism, and outputting accurate geographic coordinates, refined classification attributes and continuous drift trajectories of the garbage in combination with a water surface flow velocity field. According to the invention, all-weather, automatic and high-precision monitoring and tracking of the floating garbage in the river channel are realized, and the intelligent level and decision-making efficiency of water environment treatment are remarkably improved.