598 results about "Motion parameter" patented technology

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.

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 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.

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 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.

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 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 provides a PLC unmanned truss control method and system, and relates to the technical field of truss control, and the method comprises the steps: S1, an upper computer inputs a target coordinate, a PLC generates a three-axis motion track based on an interpolation algorithm, and dynamically adjusts the speed curve of each axis; s2, the upper computer reads real-time state data of the PLC and writes a control instruction into the PLC according to the real-time state data; s3, if the sudden stop signal is detected to be triggered, cutting off the power supply of the servo driver, recording the fault type and the triggering time, generating a log file, and otherwise, entering the next step; s4, the PLC receives a task instruction of the upper computer, and motion parameters are dynamically adjusted according to feedback of the sensor; and S5, controlling each axis to micro-move through the upper computer, and recording the coordinates of the teaching points to generate a reproducible motion sequence. Through cooperative control of the PLC and the upper computer, a centralized management and decentralized execution mode is realized, and the risk of single-point failure is reduced.

A method for motion synchronization-based head pose estimation, by a head mounted display (HMD) device and an HMD device for performing the same are provided. The method includes receiving, by the HMD device, motion data from a plurality of motion sensors of the HMD device, receiving, by the HMD device, a plurality of image frames from at least one simultaneous localization and mapping SLAM camera of the HMD device, estimating, by the HMD device, a plurality of motion parameters of head movements of a user from the plurality of image frames received from memory, generating, by the HMD device, a filtered subset of the motion data received from the plurality of motion sensors based on the plurality of motion parameters of the head movements, synchronizing, by the HMD device, the plurality of image frames received from the memory and filtered subset of motion data, and estimating, by the HMD device, the head pose based on the synchronized plurality of image frames and motion data.

The invention relates to a multi-user holographic sand table collaborative resolving system and method based on 360-degree omni-directional perception gesture recognition. The method comprises the following steps: acquiring continuous multi-frame multi-view hand image data; hand instance separation and key point extraction are carried out on the multi-view hand image data of each frame, and spatial 3D coordinates of 21 key points are independently solved for each hand instance; based on the space 3D coordinate sequence of the continuous frames, space-time action features are extracted, and a gesture template library is matched to recognize gesture types and motion parameters; analyzing the gesture type, matching an interaction intention in combination with a predefined gesture-instruction mapping table, and generating an interaction control instruction according to a matching result; generating a three-dimensional holographic sand table image cooperatively operated by multiple persons in real time according to the interaction control instruction; through a multi-hand parallel processing technology, different hand instance feature extraction paths are separated, the problem of multi-hand ID jump is solved, occlusion area coordinate prediction is optimized, and interactive use experience is improved.

The invention discloses a physical object three-dimensional positioning method and system based on video data, and relates to the technical field of three-dimensional positioning. The method is used for solving the problems of low object space positioning precision, unstable pose estimation and poor time sequence continuity in a video scene. Firstly, an input video stream is analyzed, an object segmentation mask is generated, feature points are extracted, camera motion parameters are calculated through inter-frame matching, and a scene sparse three-dimensional point cloud is reconstructed; a candidate three-dimensional bounding box is generated according to the segmentation mask and the point cloud, an optimal bounding box is selected by combining geometric matching degree and feature similarity evaluation, and a preliminary three-dimensional positioning result of the object is obtained; a positioning result and historical frame motion data are fused, a space-time constraint optimization model is constructed, and the six-degree-of-freedom pose of the object is solved; and finally, neural radiation field representation is established based on the pose, the pose and neural parameters are optimized through combination of micro rendering and back propagation, continuous and accurate updating of the three-dimensional position of the object is realized, and the positioning stability and robustness in a dynamic scene are improved.

The invention provides a fan blade defect detection method and device based on dynamic inspection of an unmanned aerial vehicle, and relates to the technical field of fan detection. The method comprises the following steps: based on a blade motion parameter, a flight state parameter of an unmanned aerial vehicle and a shooting time parameter, carrying out deblurring processing and splicing processing on an initial blade image collected in dynamic inspection of the unmanned aerial vehicle to obtain a second image; performing feature extraction on the second image by adopting an SIFT feature matching algorithm to obtain a third image; and performing defect detection on the third image by adopting a dual-channel convolutional neural network. The initial images are subjected to motion compensation and splicing, the blurring effect caused by blade rotation can be eliminated, and the problem of image dislocation in dynamic shooting is solved. According to the method, feature extraction and defect detection are carried out on the basis of the images after motion compensation and splicing are completed, the dynamic inspection efficiency and the defect detection rate are remarkably improved, and efficient and accurate inspection of the fan blade is achieved when the fan is in the running state.

The invention discloses a neural signal-based wounded rehabilitation effect evaluation model construction method, and relates to the technical field of medical rehabilitation, and the method comprises the steps: synchronously collecting electroencephalogram, myoelectricity and motion parameters, extracting multi-source features to construct feature vectors corresponding to training actions, and according to the feature vector data of multiple training actions of a wounded, constructing a neural signal-based wounded rehabilitation effect evaluation model; and capturing dynamic changes of neural signals and action control ability in the training action sequence based on the sequence model, and constructing an evaluation model. According to the invention, time-dependent learning is carried out on the corresponding characteristics of each training action by using the sequence model, dynamic changes of neural signals and action control ability along with the training actions can be captured, an improvement trend in a rehabilitation process is embodied, and an evaluation model aiming at an action training effect is constructed; the action indexes output by the evaluation model can reflect the instant effect and gradual improvement condition of each action in training, and help to identify rehabilitation inflection points and training bottlenecks.

The invention provides a multi-station cooperative control method based on event triggering and look-ahead synchronization. The multi-station cooperative control method comprises the following steps: S1, defining nominal motion parameters for each station; s2, when the physical completion event of the current station is detected, calculating to obtain an accumulated time deviation up to the current station; s3, on the basis of the cumulative time deviation, executing online optimization calculation in a prediction time domain containing a plurality of stations in the future so as to determine an optimal target operation duration used for compensating the cumulative time deviation; and S4, according to the optimal target operation duration, generating and issuing a new motion curve for a first station in the prediction time domain, taking the station as a new current station, and circularly executing the steps S2 to S4. According to the method, the system can actively eliminate tiny time deviation caused by uncertainty factors such as material difference and mechanical wear, and the accumulative effect of errors is avoided.

The invention relates to the technical field of artificial intelligence, in particular to a multi-dimensional tennis action analysis system, which comprises a data acquisition module, a data preprocessing module, an evaluation analysis module and a feedback interaction module, and is characterized in that the data acquisition module comprises a posture data acquisition unit, a ball path and walking tracking unit and a motion parameter acquisition unit; the data preprocessing module carries out space-time calibration on posture, track and motion parameters based on a timestamp synchronization mechanism, a fusion data set is formed through data cleaning and standardization processing, and the evaluation analysis module is used for distinguishing action types, integrating scores of posture dimensions, power generation dimensions and drop point dimensions and generating a comprehensive evaluation report; and the feedback interaction module displays an evaluation result in real time on an interface based on characters and visual icons and outputs historical analysis. According to the invention, the data validity is guaranteed, the error interference is eliminated, the analysis efficiency and the professionality of the evaluation result are improved, the use threshold is reduced, and the universality and practicability of the system are improved.

The invention relates to the technical field of aerial target identification, and discloses an aerial dynamic target accurate identification method and system. The method comprises the following steps: acquiring multi-dimensional spectral data of a target through a multi-spectral imaging device, extracting spatial distribution characteristics, establishing a three-dimensional motion track model according to the spatial distribution characteristics, and calculating an instantaneous velocity vector; a motion deviation index set is generated in combination with preset reference motion parameters, abnormal behavior categories are divided through clustering analysis, and a key frame sequence is marked; reconstructing a local motion characteristic spectrum based on the key frame sequence, extracting morphological change parameters, and inputting the morphological change parameters into a pre-trained target recognition network to obtain a type recognition result and a confidence score; when the confidence coefficient is lower than a threshold value, activating a supplementary recognition process, acquiring high-resolution texture data, and fusing the high-resolution texture data with the initial recognition result to generate a final recognition tag; and updating the feature database and adjusting the network weight according to the final identification tag.

The invention relates to the technical field of slope seismic resistance, and discloses an engineering slope seismic stability optimization method and system, and the method comprises the steps: collecting flood discharge tunnel outlet slope data and seismic oscillation parameter values, and carrying out the partitioning according to the flood discharge tunnel outlet slope general situation data; analyzing rock mass weathering and unloading and physical geology phenomena in each area to obtain physical mechanical parameter suggestions; establishing a three-dimensional model of the flood discharge tunnel outlet slope based on the physical and mechanical parameter suggestions and the seismic oscillation parameter values; inputting the non-uniform seismic dynamic load into the three-dimensional model of the flood discharge tunnel outlet slope for analog calculation; establishing a side slope geomechanical model, and performing simulation verification; and comparing the simulation verification result of the side slope geomechanical model with a preset rule threshold value, if the simulation verification result of the side slope geomechanical model is greater than the preset rule threshold value, performing early warning, uploading the simulation verification result of the side slope geomechanical model to a cloud platform, and performing visual output. According to the invention, comprehensive analysis and management of the flood discharge tunnel outlet slope under the earthquake effect are realized.

This invention discloses an automatic robot instruction inspection method and apparatus based on the PDPS platform. The method involves acquiring offline program data of the robot and the corresponding design rules, configuring a program inspection tool according to the design rules, inputting the program data into the inspection tool, and storing safety information from the first and second instructions into corresponding variables. The information in each variable is compared or judged to obtain a first judgment result. Chinese characters and punctuation marks in the program data are judged to obtain a second judgment result. Based on the motion parameters in the program data, the margin value of each joint when the robot performs each action is calculated, and collision risk is judged based on the margin value to obtain a third judgment result. Finally, the robot's compliance with safety rules is judged based on the motion radius to obtain a fourth judgment result. This method improves the accuracy of offline program inspection.

The invention relates to the technical field of offshore medical equipment, in particular to a wave compensation operation cabin control method and system for an offshore medical ship, and aims to analyze and predict wave motion by detecting motion parameters of a ship body of the medical ship under the action of waves in real time and adopting a control algorithm. The motion quantity and the target value of adjustment of an adjusting mechanism used for adjusting the position and the posture of the operation cabin are calculated according to the wave motion parameters, the position and the posture of the operation cabin are adjusted according to the motion quantity, finally, the actual position and the posture of the operation cabin are detected in real time, and the detected actual value is compared with the expected target value. The control algorithm is adjusted according to the comparison result, feedback adjustment is achieved, the influence of waves on the operation cabin can be effectively counteracted, a stable operation environment is created for doctors, the operation risk is remarkably reduced, and then the operation success rate is increased.

The invention discloses a control method and system for bucket throwing operation of a bridge type grab ship unloader, and relates to the technical field of port machinery intelligent control, and the control method comprises the following steps: based on ship body real-time pose data, calculating ship body six-degree-of-freedom motion parameters and trolley follow-up error amount, and generating ship body compensation parameters and trolley deviation compensation amount; based on the bucket throwing action parameter instruction, an initial grab bucket motion control instruction is obtained, dynamic track correction is carried out by superposing the hull compensation parameters and the trolley deviation compensation amount, and a grab bucket motion control instruction is generated; and executing the grab bucket motion control instruction, evaluating the bucket throwing operation effect by adopting a dynamic time warping algorithm, and dynamically executing secondary dynamic track correction. According to the method, the space-time difference between the actual track and the target track is compared by adopting the dynamic time warping algorithm, the effect score is calculated in combination with the material residual quantity, and secondary correction is triggered, so that an execution-evaluation-optimization closed-loop control mechanism is formed, and the operation precision under complex disturbance is ensured.

The invention provides a sperm automatic detection and tracking method, sperm automatic detection and tracking equipment and a storage medium. The method comprises the following steps: firstly, preprocessing a sperm image, performing rotation bounding box labeling on a head and a tail, and generating a training set containing a center point coordinate, a width, a height and an angle; training a target detection model by using the training set to obtain a model capable of automatically identifying sperms and outputting a rotating bounding box; detecting video frames by using the model, and extracting the position, orientation and size of each frame of sperm; a multi-dimensional fusion scoring system is constructed through position, direction, speed, color and track continuity, adjacent frame targets are matched, a comprehensive score is generated, the targets are associated, and a continuous track and a unique identifier are formed and used for calculating motion parameters; and judging whether the track is static or low active according to the speed and displacement of the latest N frames, sealing the track to eliminate subsequent matching, and only reserving a non-sealed track to calculate motion parameters. According to the method, interference is effectively avoided, and the tracking accuracy and the reliability of sperm motility parameter calculation are remarkably improved.

The invention relates to a crane operation safety protection method based on personnel trajectory prediction in the technical field of crane safety control. The crane operation safety protection method comprises the following steps: S1, acquiring personnel position and attitude data of a crane operation area in a multi-view manner; s2, predicting a future movement track of the person by adopting a space-time fusion neural network; s3, dynamically calculating a minimum safety distance and generating a safety boundary according to the crane motion parameters and the predicted trajectory; s4, adjusting the running state of the crane through a hierarchical control strategy, and triggering a manual intervention mechanism when a safety boundary is exceeded; the safety protection method can improve the protection effect and the operation efficiency and reduce the accident rate.

The invention discloses a multi-information fusion ghost point elimination and target tracking method. The target tracking precision is improved by eliminating false associated point locations by using the scattering characteristic of target angle cooperative positioning. Aiming at the problem that the tracking precision is reduced due to a large number of false associated ghost points, the invention provides a two-stage ghost point elimination and target tracking algorithm based on angle measurement and target motion characteristic fusion. According to the algorithm, a cooperative positioning strategy of first correlation and then estimation is adopted, and a vision field grid map and an energy accumulation matrix are constructed by establishing a mapping relation between angle measurement noise and positioning errors; the method comprises the following steps: analyzing spatial geometric distribution characteristics between a real target and false correlation ghost points in a vision field, designing a novel elimination criterion based on the idea of Hough transformation, and realizing primary coarse elimination of the ghost points; by researching the distribution characteristics and motion characteristics of a target positioning fuzzy region, a prediction tracking gate is constructed by using motion parameter identification, and secondary fine elimination of ghost points is realized from the kinematics level.