823 results about "Motion parameter" patented technology

The invention relates to the technical field of model construction, in particular to a method and a system for constructing a sheet metal working three-dimensional process model. The method comprises the following steps: acquiring equipment specification data and material characteristic data; the method comprises the following steps: acquiring geometric parameters and motion parameters of sheet metal processing equipment by using a three-dimensional scanner, and integrating equipment specification data and material characteristic data to construct a sheet metal three-dimensional initial process model; analyzing a material plastic deformation characteristic of the material characteristic data and setting a processing parameter threshold range; the machining parameter threshold range is input into the three-dimensional initial process model for stage machining process simulation, the flange area strain rate, the dangerous section thinning rate and the rebound angle variable quantity are output, and stage machining process simulation comprises plate inflow stage simulation, forming stage simulation and shaping stage simulation. According to the method, three-dimensional scanning, comprehensive machining process simulation, topological optimization and a real-time error feedback mechanism are introduced, so that the construction accuracy of the sheet metal machining process model is improved.

The invention relates to the technical field of robots, and discloses a robot nondestructive testing method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-modal sensing data and motion state parameters; constructing a three-dimensional material feature model and analyzing to generate an initial defect feature evaluation model; loading material attributes and detection precision constraints to generate a comprehensive model, and combining motion parameter simulation to obtain path regulation and control data; training the dynamic defect prediction model to obtain a detection path correction model and generating initial detection parameters; obtaining dynamic correction simulation information based on the initial parameters and the like; constructing a multi-objective optimization model, and optimizing to obtain an optimal detection path parameter; and generating a safety detection probability by combining real-time model reasoning, and adjusting a detection strategy to realize dynamic path matching. The system comprises a data acquisition module, a feature modeling and analysis module, a comprehensive evaluation and simulation module and the like. The method improves the detection precision, efficiency and adaptability, and is suitable for nondestructive detection of complex targets.

The invention provides a numerical control machine tool dynamic precision control method and system based on multi-axis linkage error compensation, and relates to the technical field of mechanical manufacturing, and the method comprises the steps: collecting shaft system motion parameters and real-time cutting load parameters of a multi-axis numerical control machine tool in a linkage machining process in real time; shafting motion parameters comprise displacement, speed and acceleration of each feed shaft, and cutting load parameters are obtained through inversion of main shaft current and servo driving torque; calculating a dynamic following error of each feed shaft in a linkage state based on the shaft system motion parameters; and carrying out load coupling correction on the dynamic following error by fusing real-time cutting load parameters to obtain an initial multi-axis linkage dynamic error. According to the invention, real-time precision control in the high-dynamic machining process can be realized.

Provided in the embodiments of the present application are a robot positioning method and a robot. The robot positioning method comprises: acquiring motion parameters corresponding to a current frame of point cloud which is collected by a robot and motion parameters corresponding to a previous frame of point cloud which is collected by the robot, wherein the motion parameters at least comprise a linear velocity and an angular velocity; at least performing angle filtering, line filtering and voxel filtering processing on the current frame of point cloud, so as to obtain a current frame of optimized point cloud, wherein the angle filtering is used for removing noise points from the current frame of point cloud, the line filtering is used for extracting a structured scene from the current frame of point cloud, and the voxel filtering is used for clustering feature points in the current frame of point cloud; and on the basis of motion parameters corresponding the current frame of optimized point cloud and the motion parameters corresponding to the previous frame of point cloud, calculating a pose corresponding to the robot when same collects the current frame of point cloud, so as to realize positioning. The method can reduce the computation amount of a robot when performing positioning in a complex scenario, thus reducing the computing power requirements for the robot.

The invention relates to a dynamic precision compensation control method based on an intelligent PCB drilling machine, and the method comprises the steps: collecting a dynamic parameter set in real time through a multi-source sensor group disposed on a drilling main shaft, the dynamic parameter set comprises displacement deviation caused by axial deformation of a main shaft, thermal deformation analyzed based on interlayer temperature gradient and frequency spectrum characteristics reflecting tool-material coupling vibration, and according to the multi-dimensional compensation vector and current drilling process parameters, a space trajectory correction instruction and a motion parameter optimization instruction are dynamically synthesized, so that the dynamic parameter set is obtained. Wherein the trajectory correction instruction reconstructs a real-time interpolation path through a B-spline curve, the motion controller receives a space trajectory correction instruction and a motion parameter optimization instruction, at least three times of compensation iteration are completed in a single drilling period, a linear motor and a spindle motor are driven to execute online precision compensation, and the execution delay of the compensation instruction is smaller than 2 ms.

The invention relates to the technical field of two-dimensional motion control, and discloses a high-precision two-dimensional motion error prediction compensation iteration method. The method comprises the following steps: on an operation interface of a motion control system, generating a motion track overview containing a target position sequence and corresponding expected motion parameters according to parameters set by a user, and connecting the motion track overview with an actual motion execution device; measuring the deviation between the actual position and the target position of the motion platform at the current sampling moment, and calculating a motion error vector; constructing a prediction model based on the motion error vector and the expected motion parameter, and predicting a two-dimensional motion error at a future moment through iterative optimization; generating a compensation control instruction according to the prediction result and applying the compensation control instruction to the motion execution device; and monitoring the compensated motion state in real time, updating the prediction model and outputting a compensation log. According to the method, by predicting the error in advance and iteratively optimizing compensation, the two-dimensional motion control precision can be effectively improved, the real-time performance and adaptability of compensation are enhanced, and the method is suitable for a high-precision motion control scene.

The invention provides a distributed robot cooperative control method and system based on edge computing. Environmental space data and dynamic obstacle motion parameters of robots are collected, and cross-robot joint compression is carried out to obtain a distributed sensing data set. Then, based on the spatial density difference of the data set, hierarchical communication links in the group and the priorities of signal transmission paths of the hierarchical communication links are dynamically constructed and adjusted in real time. Then, the adjusted priority is converted into a communication weight parameter, a cooperative control instruction set is generated in combination with an obstacle motion parameter, and cross-node synchronization is carried out on the cooperative control instruction set and obstacle information sensed by each node; and finally, the synchronized instruction set is utilized to drive the robot to execute edge computing resource interaction, and a cooperative motion path is generated. According to the technical scheme provided by the invention, the communication efficiency and the adaptive capability of distributed robot cooperative control can be improved.

The invention discloses a motion error dynamic compensation method applied to a surgical mechanical arm, and belongs to the technical field of surgical mechanical arm control and medical automation. The method comprises the following steps: firstly, acquiring position data of each joint encoder of the surgical mechanical arm, a motor current signal, a position feedback signal of an end effector, load change information and accumulative operation duration in real time; processing the data, fusing the data through adaptive Kalman filtering to estimate a joint dynamic error, inputting a load change rate and an accumulated operation time length into a pre-training error prediction model to output a compensation basic quantity, and fusing the load change rate and the accumulated operation time length to generate a real-time error compensation quantity; and then the compensation amount is superposed to a joint original control instruction to correct motion parameters, and finally the compensated tail end position deviation is analyzed, and a precision identifier or an adjustment prompt is generated and fed back to a control system. According to the method, real-time error compensation is achieved, the compensation precision and working condition adaptability are improved, it is guaranteed that the mechanical arm is stably positioned, and the surgical operation risk is reduced.

The invention relates to a ball action evaluation method, device and equipment based on a large model, and a medium, and the method comprises the steps: collecting the three-dimensional coordinates of the joints of a student and the trajectory data of sports equipment, and generating a standardized action sequence through space-time compensation and noise suppression; based on inverse kinematics and differential kinematics technologies, human body joint rotation features and instrument motion parameters are separated and extracted, and a multi-dimensional motion feature vector is constructed; calculating a dynamic mahalanobis distance and a key joint contribution degree by adopting a comparative learning mechanism and a standard action template, and generating a quantitative evaluation result; and finally, through inverse kinematics optimization and three-dimensional visual rendering, outputting an action correction scheme. According to the method, the limitation of traditional single-mode evaluation is broken through, deep analysis of instrument-human body collaborative motion features is realized, the technical problems of inaccurate action detail capture, single evaluation dimension and insufficient feedback visualization in a high-speed scene are effectively solved, and the scientificity and real-time performance of training guidance are remarkably improved.

In an embodiment, a method for human motion generation with open vocabulary scene-and-text context is provided. The method involves receiving an input that includes a 3D point cloud of a scene containing a goal object with a natural language instruction related to the goal object. A text tokenizer is applied to the text to obtain tokenized text, and a text encoder from a pre-trained vision-language model generates text features. First scene features are generated by applying a pre-trained U-Net scene encoder to the 3D point cloud, which are down sampled to obtain second scene features. A conditional latent is obtained by fusing the second scene features with the text features. A conditional motion generator predicts motion parameters for a parametric human body model over a specific time duration. Finally, 3D human meshes for multiple motion frames are obtained based on the motion parameters and the parametric human body model.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

The invention relates to a human body posture capture-based disabled assisting robot planning method and system, and the method comprises the following steps: S1, a patient is fixed to a system main body through a flexible fixing device, so that the posture capture precision is ensured, and accidental sliding is prevented; s2, a high-definition RGB camera and a depth sensor are used, real-time posture joint point data of the patient are obtained based on a human body posture estimation algorithm, and the real-time posture joint point data comprise the head position, the limb position and the trunk position; s3, resolving the captured data in real time, and referring to a posture database to generate a most suitable initial rehabilitation posture; s4, collecting visual, voice and physiological signals by using a camera, a microphone and other sensors, and performing feature extraction and fusion by using a convolutional neural network to improve the recognition accuracy; s5, judging the satisfaction degree of the patient to the current posture through an emotion recognition technology in combination with voice emotion analysis, and adjusting the system according to the satisfaction degree of the patient; the motion parameters of rehabilitation training of the patient are obtained quickly, the effectiveness and rehabilitation efficiency of rehabilitation training are improved, and the participation degree and enthusiasm of the patient are increased.

The invention provides a cleaning robot multi-mode motion control system, method and equipment and a medium, and belongs to the technical field of intelligent robot control. The system comprises a multi-modal data acquisition module used for performing multi-source synchronous acquisition on an environment image, distance information, a voice signal and a collision signal and outputting original sensing data; the data fusion and processing module is used for receiving the original sensing data, sequentially executing space-time alignment correction, multi-source feature fusion and instruction semantic analysis, and outputting an environment sensing result and a structured control instruction; the motion planning and decision-making module is used for executing global path planning, dynamic obstacle trajectory prediction and motion parameter decision-making based on the environment perception result and the structured control instruction, and outputting a motion control instruction containing a motion mode, a speed and an angle; and the motion control execution module is used for receiving the motion control instruction, converting the motion control instruction into an execution mechanism driving signal, realizing closed-loop control through real-time state feedback and outputting a motion execution result.

The invention belongs to the technical field of robot control, and provides a robot self-adaptive task planning and action adjusting method and system, and the method comprises the steps: responding to a task instruction, and calibrating the motion parameters of a mechanical arm of a robot; acquiring a state vector according to a set frequency, determining whether visual abnormality or motion abnormality exists or not according to a continuous change condition of the state vector, and generating an abnormality mark if the visual abnormality or motion abnormality exists; performing action correction in response to the abnormal mark or the task failure mark, performing cross-modal fusion on the current state vector and the semantic vector of the task instruction, generating a candidate action sequence based on the fused feature and task target, and considering the compatibility of the candidate action and the current environment, the residual step number of the action queue and the task failure mark. An optimal action is screened out, and then an action adjusting instruction is generated; and continuously obtaining the state vector and the action correction process until the robot completes the task instruction. The environment adaptability and the execution success rate of the robot in a complex scene can be improved.

The present disclosure belongs to the technical field of robot control, and relates to a method and apparatus for controlling a robot, and a medium and an electronic device. The method comprises: acquiring a target picture, which is collected by a camera apparatus that is mounted on a robot, and acquiring a target instruction for controlling the robot; inputting the target picture into a trained visual model, such that the trained visual model outputs a visual embedding vector; acquiring a text embedding vector corresponding to the target instruction, and inputting the visual embedding vector and the text embedding vector into a trained language model, such that the trained language model outputs a target numerical value; and decoding the target numerical value, so as to obtain a movement parameter, wherein the movement parameter is used for controlling the movement of the robot. By means of the present disclosure, a robot is driven to perform various motions to complete an instruction of a user, such that human-robot interaction is more intelligent and freer, thereby meeting usage requirements in various application scenarios, increasing the success rate of specific task execution, and enlarging a usage range of the robot.

The invention discloses a railway comprehensive defect detection method and system based on multi-source sensor fusion. The method comprises the following steps: carrying out joint calibration on static external parameters by adopting a multi-modal calibration plate; a high-precision timestamp and vehicle multi-source motion parameters are utilized to construct a motion compensation model to carry out adaptive dynamic distortion correction calibration; synchronously acquiring multi-source sensor data, and based on static external parameters and adaptive dynamic distortion correction, obtaining a corrected visible light image, an infrared image and a laser radar three-dimensional point cloud; complementarity features are extracted from the data of the three modes, adaptive fusion and optimization are carried out through an attention mechanism, and multi-scale fusion features are output; segmenting different railway facility areas by using multi-scale fusion features, extracting corresponding area features, and calling detection algorithms of corresponding facilities to identify defects of different railway facilities; according to the method, high-precision calibration, feature-level fusion and railway facility special defect detection are fused, and the automation level and the detection accuracy of railway inspection are improved.

The invention belongs to the technical field of multi-camera cooperative imaging, and discloses a multi-camera cooperative anti-shake control system and method. The method comprises the following steps: generating a global hardware trigger signal by using an FPGA (Field Programmable Gate Array), synchronizing IMU and multi-camera acquisition, and combining motion parameters to obtain a space-time aligned multi-view image; obtaining a global jitter component through feature extraction, motion vector field generation, depth map analysis and the like; fusing the data with IMU data to generate a trajectory, and inputting the trajectory into LSTM to obtain future compensation amount; and cutting based on deep layered transformation to obtain an EIS optimized frame, dynamically electing a main camera for super-resolution reconstruction, and outputting a final anti-shake video stream. Through multi-source information fusion, layered precise compensation and dynamic optimization, the accuracy and stability of motion estimation are remarkably improved, the jitter influence of the finally output video stream is effectively weakened, and the reliability and visual coherence of the image stabilization effect in a complex scene are ensured.

The invention discloses a video gait analysis-based asthenia syndrome evaluation method, medium and equipment, and the method comprises the steps: firstly, synchronously collecting gait videos of different visual angles through a plurality of cameras, and extracting 2D joint point sequences of all visual angles through a stacked hourglass network; then, a high-precision 3D joint point time sequence is reconstructed through a multi-view fusion algorithm, depth features are extracted through a residual connection network, and data of all view angles are dynamically weighted and fused based on the shielding rate and the confidence coefficient; performing sliding window segmentation and downsampling on the 3D sequence, and performing action classification by using an LSTM time sequence model; and finally, key motion parameters are extracted from a classification result to calculate a weakness score, and weakness grade evaluation is realized. According to the method, the problem of single-view shielding is solved through multi-view 3D reconstruction, the evaluation precision is remarkably improved by combining dynamic weighted fusion and time sequence modeling, and compared with a traditional method, the method has the advantages of being non-contact, low in cost and high in objectivity, and is suitable for large-scale screening of community and family environments.

An AGV robot vision acquisition and avoidance control method based on deep thinking relates to the field of non-electrical variable control or adjustment systems, and comprises the following steps: acquiring real-time motion parameters of an automatic guided vehicle, calculating a key path point set and constructing feature vectors; collecting a visual image and depth data, extracting obstacle features through grid division, and generating a three-dimensional feature model; mapping the path points to a three-dimensional model to generate a dynamic scene sequence, and calculating a distance value with a dynamic target to determine a risk level; analyzing the scene by using a large language model, generating an obstacle avoidance scheme containing an obstacle avoidance track, speed and steering, and storing the obstacle avoidance scheme in a scheme library; current scene features are obtained, candidate schemes are selected through similarity matching, and the optimal scheme is selected for execution through safety scoring. By implementing the method, the obstacle avoidance success rate of the automated guided vehicle can be improved.

The invention provides a circuit board component accurate positioning and mounting method and system based on visual guidance, and relates to the technical field of mounting, and the method comprises the steps: obtaining the multi-angle image information of a to-be-mounted component, determining a three-dimensional point cloud model and a depth map sequence, and determining the type information; segmenting the point cloud model through a region growing algorithm to extract features for pose estimation; the mounting parameters are called and combined with the posture deviation information to control a mechanical arm; and motion parameters are optimized based on a self-adaptive optical flow algorithm until the precision requirement is met. According to the invention, the component mounting precision and efficiency are improved, and the mounting failure rate is reduced.

The invention discloses a motion control method based on an empty-wall cross-medium robot and a related device. The method comprises the following steps: constructing a kinetic equation and a mixed control matrix of the empty-wall cross-medium robot in different modes; actual motion parameters and target parameters of the empty-wall cross-medium robot are obtained, calculation operation is conducted through the controller according to the target parameters and the actual motion parameters, control parameters of the controller are adjusted through the kinetic equation, and a control signal is generated; and state mapping is carried out according to the control signals through the mixed control matrix, and the motor driving state of the empty-wall cross-medium robot is output. According to the invention, the motion stability of the empty-wall cross-medium robot in a complex scene can be improved, and flexible switching control of the motion modes of the robot on the ground, in the air and on the wall surface is realized.

The invention provides a reference line generation method, an automatic driving method and device, equipment and a vehicle, and relates to the field of artificial intelligence, in particular to the field of automatic driving. According to the specific implementation scheme, for at least one obstacle located in a target area in a road, the category of the obstacle is determined according to a first motion parameter of the obstacle in a historical time period, and the category of the obstacle represents the relative relation between the obstacle and a target vehicle; under the condition that the at least one obstacle comprises the target obstacle belonging to the target category, determining any candidate node in a plurality of candidate nodes determined for the position of the target vehicle, and determining the total cost value of the candidate nodes according to the historical driving track of the target obstacle and the second motion parameter of the candidate node; determining a target region reference line according to a target node determined by using the total cost values of the plurality of candidate nodes; wherein the target area reference line is used for planning a target driving track of the target vehicle.

The invention relates to the field of automatic driving test, in particular to an automatic driving panoramic test scene generation system based on a large language model, which mainly comprises a motion encoder used for encoding a vehicle motion parameter sequence into a low-dimensional latent feature by adopting a lightweight neural network motion encoder, injecting the U-Net network of the potential diffusion model through residual connection; the panoramic adapter is used for extracting and fusing multi-scale spatial-temporal features on four resolution levels by adopting spatial-temporal decoupling convolution processing scene overall motion conditions to obtain multi-modal features, and injecting the multi-modal features into a U-Net network of the potential diffusion model; and the multi-modal labeling system is used for realizing labeling of the panoramic image based on a large language model and generating a general scene description. According to the invention, high-fidelity generation and multi-dimensional verification of the automatic driving panoramic test scene can be realized, and the key problems of the existing scene generation technology in the aspects of physical rationality, space-time consistency and panoramic continuity are effectively solved.

The invention relates to a flexible combined machine tool control system. The system comprises a multi-channel user interface module, a servo parameter monitoring module, a workpiece coordinate system management module and the like. The multi-channel user interface module is associated with a work station by means of a dynamic binding technology, automatically generates an interactive interface according to configuration parameters and visually configures an enabling state. And the servo parameter monitoring module displays and adjusts the shaft motion parameters in real time and adjusts the shaft motion parameters on line, the deviation is corrected through a self-adaptive algorithm, coordinate synchronization of multiple workstations is achieved through the algorithm and a batch writing function, and mixed analysis of an NC program and a user-defined instruction is supported. The hardware abstraction layer module encapsulates a heterogeneous control card interface, dynamically allocates hardware resources, and coordinates time sequence linkage of each part by using a state machine model. The system also combines visual inspection to correct cutter compensation parameters, and double safety protection is set. According to the invention, the problem of low equipment cooperation efficiency in multi-station flexible processing is effectively solved, and the system compatibility and the processing precision are improved.

The invention discloses a three-axis moving part control method and system based on machine vision. The method comprises the following steps: obtaining a trajectory feature data set from a motion trajectory original data stream; analyzing the difference between the trajectory feature data set and a theoretical trajectory to obtain a deviation distribution report, and calculating the variation amplitude of deviation in combination with historical data and environmental fluctuation conditions to obtain a deviation value matrix; extracting spatial displacement and angle offset data from the deviation value matrix to obtain a basic adjustment amount, and calculating an initial parameter adjustment scheme of each axis in combination with a multi-axis cooperative control constraint condition to obtain a parameter optimization result; and fusing a compensation factor into the parameter optimization result, recalculating the compensation factor for the parameter of which the parameter adjustment result exceeds a preset precision range to obtain a final parameter adjustment scheme, and adjusting the motion parameter of each axis according to the scheme. The method can effectively improve the motion precision and stability of the multi-axis motion part.

The invention discloses an unmanned ship intelligent search and rescue method and system, and the method comprises the steps: deploying an unmanned ship cluster driven by a multi-agent algorithm, collecting environment parameters, generating an initial cooperative path, starting an improved PV-RCNN model to detect a target in real time, and transmitting a result to an algorithm state space to adjust motion parameters. And repeatedly detecting and adjusting until a surrounding track is generated. The system comprises a distributed dynamic environment parameter acquisition unit, a multi-agent decision control unit and the like. According to the method and the system, the cooperative path is dynamically adjusted through the multi-agent algorithm, the target features are accurately extracted in combination with the improved target detection model, closed-loop linkage is formed, the defects that the adaptability to the dynamic environment is insufficient and target detection and control are disjointed in the traditional technology are overcome, and the search and rescue efficiency and accuracy are improved.

The invention relates to the technical field of wearable exoskeleton assistance, in particular to a lower limb exoskeleton knee joint assistance control method and system. The lower limb exoskeleton comprises a pair of swing arms respectively connected with thighs and shanks and an actuator, and the method comprises the following steps: processing data of a motion parameter sensor, acquiring detection data input by the motion parameter sensor arranged on the lower limb exoskeleton, and processing the detection data to extract motion features; gait phase detection is conducted, the current gait phase is determined according to the motion characteristics, the gait phase comprises a supporting phase and a swinging phase, the supporting phase corresponds to the state that the lower limb touches the ground, and the swinging phase corresponds to the state that the lower limb leaves the ground; activity identification: motion features are extracted in the supporting phase to determine activity types, and the activity types comprise at least one of flat walking, upward walking, downward walking, running, squatting, backward walking and unknown; according to the control strategy, the corresponding control strategy is selected according to the gait phase and the activity type so as to control an actuator.

The invention discloses a video generation method and device, and belongs to the technical field of electronics. The method comprises the following steps: determining a motion feature of a camera based on an acquired first motion parameter and a first image, the first motion parameter being used for representing a rotation angle and a translation distance of the camera; and inputting the motion feature and the first image into a first video generation model, and generating a first video based on the motion feature and the image feature of the first image through the first video generation model.

The invention relates to an AR projection interaction treadmill virtual scene generation method and device, equipment and a medium. The method comprises the following steps: acquiring motion inertia data and running belt motion parameters of a user on a running machine; respectively carrying out motion state extraction and gait phase extraction according to the motion inertia data and the running belt motion parameters to obtain a current motion state vector and gait phase information of the user; inputting the motion state vector and the gait phase information into a pre-trained motion state prediction model to obtain a preset predicted motion state of a plurality of frames in the future; based on the predicted motion state, performing layered scene generation through a scene generator to obtain a corresponding virtual scene; and performing projection mapping transformation processing on the virtual scene, and obtaining real-time AR projection data in combination with low-delay compensation. By adopting the method, a highly immersive and interactive virtual training environment can be provided in an AR projection mode.

The invention discloses a high-altitude parabolic positioning traceability analysis method and system, and particularly relates to the technical field of positioning traceability. Motion parameters of an object are calculated by adopting a multi-frame image analysis method, rotary motion is detected by utilizing a deep learning algorithm, if rotation exists, a Magnus effect compensation model is introduced to correct trajectory calculation, meanwhile, wind speed and wind direction data are collected in real time, airflow change is simulated in combination with a computational fluid dynamics model, and the influence of wind power on the trajectory is corrected. Based on the corrected trajectory data, the initial throwing position of the object is reversely deduced in combination with a building three-dimensional structure model, a target person in charge is determined, and a complete traceability report is generated by comparing an analysis result with a monitoring video, so that the accuracy and traceability of traceability analysis are ensured. The method can effectively reduce the calculation error caused by the rotation effect, wind field interference and other factors, improves the precision of high-altitude parabolic traceability, and provides a scientific basis for responsibility determination.