374 results about "Position tracking" patented technology

The invention relates to the field of personnel action monitoring and recognition, and discloses an intelligent human shape trajectory prediction and alarm system and method based on multi-modal video analysis, and the system comprises a video collection module which is used for collecting video stream data of a monitoring scene; the data processing module is used for extracting motion state information of the target human shape; the trajectory modeling module is used for constructing a trajectory model of the target person; the risk assessment module is used for calculating a behavior risk value; the alarm module is used for triggering alarm; and the optimization module is used for optimizing the trajectory model. Target motion state information is extracted through the data processing module, high-precision input is provided for the trajectory modeling module, and the trajectory modeling module generates a prediction trajectory in combination with a dynamic optimization algorithm. Compared with a scheme depending on coarse-grained detection or simple position tracking in the prior art, the method has the advantages that fine modeling and dynamic prediction of the motion state are realized, the problem that complex behaviors cannot be accurately reflected is solved, and meanwhile, the track prediction precision and the system response capability are improved.

The invention belongs to the technical field of position tracking control of rigid multi-joint mechanical arms, and discloses a fuzzy self-adaptive visual servo control method and system for a multi-joint mechanical arm system for placing bobbins in a textile workshop, and the method comprises the steps: constructing an image error model according to the positions of feature points on an image plane; the fuzzy logic system is used for processing uncertain items in the system, the instruction filtering and fuzzy self-adaption technology is used for achieving tracking control over the expected trajectory of the system, the problem of calculation complexity is solved, and adverse effects caused by filtering errors are eliminated. In the process of changing the bobbins, the bobbins are placed on a creel through a multi-degree-of-freedom robotic arm, a clamping jaw at the tail end of the robotic arm is required to accurately grab the bobbins for feeding and discharging, and the requirement for accurate and rapid control of the robotic arm is met, so that research on visual servo control of the robotic arm is of great significance. The method has a good control effect, the problem of calculation complexity in the design process of the controller is solved, the trajectory tracking error is smaller, and the system robustness is higher.

The invention relates to the technical field of position tracking, in particular to an intelligent bracelet real-time position tracking visualization method and system. The method comprises the following steps: acquiring multi-dimensional sensing monitoring parameters of a user based on a smart bracelet, performing real-time motion state evaluation of the user, and performing adaptive adjustment of a position acquisition frequency to generate an adaptive position acquisition frequency; performing real-time position positioning acquisition based on the adaptive position acquisition frequency, and performing frequency-by-frequency spatial position positioning calculation to obtain a high-precision compensation reconstruction trajectory; the method comprises the following steps: acquiring public electronic map information, performing scene key element identification, and performing key attribute cognitive marking to obtain a plurality of scene attribute memory anchor points; and performing relative position distribution analysis on the high-precision compensation reconstruction trajectory based on a plurality of scene attribute memory anchor points, and constructing an anchor point space trajectory visual mapping graph. According to the invention, high-precision real-time position tracking of the user is realized, and clear and visual multi-level map information rendering is presented.

The invention discloses a multifunctional automatic space point location tracking measurement method and device, and relates to the technical field of tracking measurement, and the method comprises the steps: obtaining N monitoring points, carrying out the priority ranking, obtaining the monitoring point sequence information, presetting a monitoring collection frequency, and generating the monitoring region information according to the monitoring point sequence information; a built-in high-precision navigation module automatically navigates to monitoring area information, a target icon is fixed on a target monitoring point, and target monitoring image information of the monitoring area information is obtained; training and constructing a point location tracking module, performing target positioning and coordinate extraction, and determining target pixel coordinate information; and target coordinate calculation is carried out to obtain actual space coordinates of the target, the actual space coordinates of the target are stored in sequence, and N space point location measurement results are generated. The technical problems that the monitoring frequency is difficult to guarantee, the measurement efficiency is low and the positioning precision is insufficient in the prior art are solved, and the technical effect of improving the efficiency and precision of space point location tracking measurement is achieved.

The invention relates to the technical field of motor control, in particular to a control and parameter intelligent optimization method for an anti-impact high-precision PMSM (permanent magnet synchronous motor) feeding servo system, which comprises the following steps of: S1, establishing a physical structure and a dynamic model of a PMSM driving feeding system; s2, designing a variable gain fractional order super-spiral sliding mode controller VGFSTSMC, realizing finite time convergence of position tracking errors by dynamically adjusting and controlling gain coefficients, and inhibiting system jitter; s3, designing an adaptive sliding mode disturbance observer ASMDO, and estimating and compensating unknown disturbance in real time through adaptive gain adjustment and an integral sliding mode surface; s4, identifying a system Stribeck friction model based on a least square method, and inputting the system Stribeck friction model as a friction feedforward compensation FFC to a current loop to reduce the disturbance uncertainty of the system; s5, dynamically adjusting and optimizing control parameters of the VGFSTSMC and the ASMDO by adopting an optimization algorithm so as to minimize a root-mean-square value RMSE of a position tracking error and a maximum instantaneous error; and S6, VGFSTSMC, ASMDO and FFC are combined with an optimization algorithm, and the PMSM is driven to realize high-precision position control.

A preset time robust sliding mode control method for a mechanical arm system comprises the steps that (1) under the condition that parameter perturbation, joint friction and external disturbance are considered, a dynamic model and a tracking error system model of the mechanical arm system are established; (2) a robust preset time controller is designed to ensure that the speed tracking error of the mechanical arm converges to zero within preset time; 3) designing a non-singular fast terminal sliding mode controller based on a preset time reaching law so as to ensure that a position tracking error can also converge to zero within the preset time; 4) the feasibility and effectiveness of the method are verified by using a Lyapunov stability analysis method; aiming at a mechanical arm system with parameter perturbation, joint friction and external disturbance, the invention develops a robust nonsingular fast terminal sliding mode control method in preset time so as to ensure that the mechanical arm system can accurately track an expected trajectory signal in the preset time, so that the tracking speed of a mechanical arm is improved, and the tracking precision is improved. And the tracking accuracy and safety of the mechanical arm are ensured.

The invention belongs to the technical field of computer vision and artificial intelligence, and particularly discloses a video target tracking method and device, a medium and equipment, and the method comprises the steps: synchronously obtaining an infrared image and a visible light image which comprise a to-be-detected target; the infrared image and the visible light image are preprocessed; constructing a to-be-detected target tracking model, and training the model; and inputting the preprocessed infrared image and visible light image into a trained to-be-detected target tracking model so as to carry out identification and position tracking on a to-be-detected target. According to the invention, high-precision, high-robustness and high-continuity position tracking of the to-be-measured target in a complex environment can be realized.

The invention discloses a grid-connected inverter complementary sliding mode control method based on a cascade generalized proportional-integral observer, and the method comprises the steps: firstly building a mathematical model of a grid-connected inverter, and enabling the mathematical model to be used for the design of a controller; and a cascade generalized proportional integral observer is introduced to estimate the lumped disturbance of the hyper-local model so as to improve the anti-disturbance capability and the noise suppression capability of the system. By introducing the complementary sliding mode controller, compared with a traditional sliding mode, the position tracking error is reduced by half. By introducing the improved reaching law, a large reaching speed can be kept when the system state is far away from the sliding mode surface, and the sliding mode reaching speed is weakened when the system state approaches the sliding mode surface, so that the chattering phenomenon of the sliding mode is reduced. According to the control method, the coupling influence of the d axis and the q axis can be obviously inhibited, the harmonic content of the three-phase current is reduced, and stable operation of the grid-connected inverter is facilitated.

The invention discloses a finite time position tracking and synchronous control method and device and a medium, and belongs to the technical field of dual-motor drive servo system control. The method comprises the following steps: constructing a dynamic model of the dual-motor-driven servo system; position tracking errors, speed synchronization errors and acceleration synchronization errors are defined, speed and acceleration states serve as cross feedback variables to be input into the dynamic model, and error signals are generated; introducing a filter and a compensation item through a preset dynamic surface control algorithm to generate a virtual control signal; constructing a finite time adaptive controller based on the error signal and the virtual control signal, and generating a motor control input signal; and estimating and compensating parameter uncertainty and nonlinear gap disturbance of the dual-motor-driven servo system in real time so as to update parameters of the adaptive controller. By means of the method, the technical effects of high-precision position tracking, dynamic synchronization and high-robustness control can be achieved in the dual-motor drive servo system.

A method for greatly reducing data storage requirements of autonomous robots capable of product inventory is described. Instead using high resolution images for label identification and position tracking, a lower resolution map can be searched. Human or machines can identify position of labels, and via a reverse mapping, the corresponding position of a label on the high resolution image can be identified. Except for those portions of the high resolution image showing a label, most of the high resolution image can be discarded. Further processing on the limited image subset can be used to read the labels.

The invention discloses a permanent magnet synchronous motor sensorless control method based on a dynamic position error. A transition stage is provided, per-unit estimation position errors between a first position error signal obtained based on a high-frequency signal injection method and a second position error signal obtained based on a sliding mode observer are compared, an optimal switching speed point is dynamically determined, smooth fusion is carried out on the two position errors by using a nonlinear weighting function, and the optimal switching speed point is obtained. And finally, the estimated position and rotating speed are obtained through a phase-locked loop. And finally, position tracking is carried out by adopting different error signals according to the estimated rotating speed. According to the method, the problem of estimation value pulsation and jump caused by the fact that a traditional method depends on fixed experience switching points is solved, smooth and self-adaptive switching of the two estimation methods in the full-speed domain range is achieved, the control precision and operation stability of the system are remarkably improved, the structure is simple, and engineering implementation is easy.

The invention discloses an electro-hydraulic system adaptive neural network preset time control method, which integrates a neural network real-time learning technology, innovatively introduces a preset time performance function, and designs an adaptive neural network preset time performance controller. For the electro-hydraulic system position tracking control problem, it can be ensured that system output transient performance and steady-state performance converge to a specified performance index range within a preset time, safe and reliable operation of the system is ensured, the unknown dynamic state of the system can be learned in real time through a neural network, high-precision motion control performance is achieved, and the control precision is improved. And the problem of differential explosion in traditional backstepping control of the electro-hydraulic system can be avoided, and the influence of measurement noise on the control precision is reduced.

The bolt is an important connecting fastener in important equipment of a train, the intelligent detection method and system for the train bogie bolt are provided in consideration that the bolt is a small part and the tightness state of the bolt on the train needs to be detected, and a visual detection system and a position tracking system are constructed and carried on an inspection robot. The method is used for monitoring the train number of a train, monitoring the position of a bogie in real time, positioning a bolt and finally carrying out abnormity detection on the bolt based on the improved YOLOv8. The method comprises a system triggering module, a position tracking module, a visual positioning module, an image data acquisition module, a data storage and transmission module and a YOLOv8 improvement module. For the visual part, a binocular high-precision industrial camera is used for carrying out image data acquisition on the train bogie bolt, processing the image data, and transmitting the image data to the improved YOLOv8 for detection; the method is suitable for rail train bogie bolt anomaly detection, point cloud processing and image processing are combined, the detection efficiency is high, and the detection result is accurate.

The invention discloses an unmanned ship safety control method and system based on an adaptive neural network observer, and relates to the technical field of unmanned ship control, and the method comprises the steps: building a second-order dynamic model of an unmanned ship through a kinetic equation, and obtaining the actual position and speed state of the unmanned ship; designing an adaptive observer based on a neural network by using a neural network approximate nonlinear dynamic state so as to estimate a first-order system position state and a second-order system speed state under DoS attack and external interference; determining an actual and estimated position tracking error of the unmanned ship according to a preset expected trajectory in combination with the actual and estimated position states, and constructing a virtual controller; designing a nonlinear filter according to the virtual control signal to obtain a trajectory tracking signal of a second-order speed system, and constructing an adaptive controller in combination with the estimated speed state; based on a virtual controller and an adaptive controller, expected trajectory tracking of the unmanned ship under DoS attack and disturbance is realized.

The invention discloses an under-actuated unmanned ship trajectory tracking control method based on preset time error constraint. The method comprises the following steps: acquiring a three-degree-of-freedom kinematics and dynamics model of an actual underactuated unmanned ship; determining a position tracking error and an azimuth angle tracking error between the underactuated unmanned ship and the reference trajectory; designing a preset time virtual control law; setting a longitudinal speed error and a yawing angular speed error of the underactuated unmanned ship; and designing a time-varying gain auxiliary dynamic system, and designing a preset time controller by using an adaptive self-organizing neural network. According to the invention, through the asymmetric preset performance function constraint error, the self-organizing neural network dynamic approximation disturbance and the model uncertainty, the preset time filter suppression differential explosion and the auxiliary dynamic system compensation input saturation, the high-precision trajectory tracking in the preset time is realized; the method has the advantages of controllable convergence time, unconstrained initial error and high calculation efficiency, and is suitable for autonomous navigation of the underactuated unmanned ship in a complex marine environment.

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 provides a large-scale rotary equipment blade measurement system calibration method based on LM algorithm optimization, and belongs to the technical field of blade three-dimensional measurement. Points in a world coordinate system are converted to a rotary table coordinate system, are rotated in the rotary table coordinate system, and then are converted to the world coordinate system; and projecting the rotated world coordinate to a pixel coordinate system to obtain a theoretical pixel coordinate, comparing the theoretical pixel coordinate with an actual pixel coordinate, and calculating a re-projection error. Constructing a target function by using the re-projection error, and iteratively optimizing the axis parameters of the rotary table through an L-M algorithm until convergence; and outputting the optimized rotary table axis for blade point cloud splicing. According to the method, the problem of linear axis calculation errors on the basis of mark point position tracking is solved, nonlinear optimization of the rotary table rotating shaft is achieved, and technical support is provided for follow-up rotary splicing and all-topography measurement of blade point clouds.

The multi-dimensional precise positioning automatic assembling robot comprises four sliding frames arranged oppositely and a plurality of cross beams, the upper ends of the four sliding frames are connected and fixed through the cross beams, a multi-direction adjusting mechanism is arranged in a frame defined by the cross beams, a mechanical arm is installed on the multi-direction adjusting mechanism, and the mechanical arm is connected with the sliding frames. The multi-direction adjusting mechanism is used for adjusting the industrial manipulator in multiple directions and achieving multi-dimensional movement of the manipulator, and a positioning mechanism is arranged on the side, facing the multi-direction adjusting mechanism, of each sliding frame. The manipulator can be tracked and shot in real time through the positioning mechanisms arranged on the multiple sliding frames, industrial cameras of the multiple positioning mechanisms can deflect in the X-axis direction and the Y-axis direction at the same time through cooperative use of two motors and related parts, the shooting angle of the cameras can be adjusted, the position tracking of the manipulator can be more accurate, and the working efficiency is improved. Therefore, accurate positioning can be achieved when the mechanical arm is used for assembling parts, and the using effect is better.

The invention discloses a novel discrete sliding mode tracking control method, system and device for a multi-motor driving system and a medium. Belongs to the technical field of multi-motor driving system control. According to a feature modeling theory, a multi-motor driving system feature model for position tracking control is established, and the model is lower in order and fewer in parameter compared with a conventional dynamic model; a recursive least square method with a forgetting factor is adopted to carry out on-line identification on characteristic model parameters, and the characteristic model parameters are projected to a parameter range set; a multi-power second-order sliding mode surface is designed to accelerate the convergence speed of the tracking error in different error bands; a novel discrete sliding mode tracking control method of a multi-motor driving system is designed, high-precision tracking control is achieved, and meanwhile sliding mode buffeting is reduced.

The invention relates to a nuclear power plant fire emergency training and drilling system based on a VR technology. The system comprises a user interaction interface for providing an interaction interface for a user so as to realize related operations of fire emergency training drilling. And the VR head-mounted display and interaction equipment is used for providing immersive virtual environment experience for trainees according to corresponding instructions, and performing motion capture and position tracking on the trainees so as to transmit obtained capture and tracking signals to the multi-terminal interconnection system. And the multi-terminal interconnection system is used for accessing the VR head displays and the interaction devices of one or more trainees so as to realize interconnection of various devices, and applying all the accessed interaction devices to the same fire drill scene based on the fire simulation software so as to perform multi-person fire simulation and emergency drill. And the emergency disposal operation system is in communication connection with the multi-terminal interconnection system and is used for enabling trainees to simulate to use emergency equipment to perform emergency disposal operation through VR head-mounted display and interaction equipment according to the capture and tracking signals.

The invention relates to the technical field of mechanical position servo control, and solves the problem of seeding depth fluctuation caused by position response lag and linear compensation failure of a traditional profiling mechanism. The method comprises the following steps: acquiring curvature characteristics of a front terrain through a terrain detection sensor, and predicting vertical displacement offset of a ground contact point by combining structural parameters and speed of an execution mechanism; generating a drive actuator feed-forward position instruction based on the offset and a preset depth target value; a cutting edge load disturbance value is collected in real time, and output thrust (hard soil area nonlinear thrust increasing / soft soil area stepped thrust decreasing) is corrected in a graded mode according to the discrete disturbance gradient; and the position of the actuator is dynamically adjusted by combining ditching depth feedback. Through double closed-loop control of terrain-position feedforward and resistance-position feedback, independent pre-judgment and accurate position tracking of the mobile platform on the surface micro-terrain are realized, so that position output is consistent with a position target value.

The invention discloses a multi-unmanned aerial vehicle formation control method and system based on deep reinforcement learning, relates to the field of unmanned aerial vehicle control, and aims to solve the problem that high robustness, safety and reliability are difficult to ensure when unmanned aerial vehicles in existing formation cope with model uncertainty and multi-source interference. Comprising the steps of 1, building an external airflow interference model by considering external airflow interference faced by an unmanned aerial vehicle when the unmanned aerial vehicle passes through a narrow channel to fly; 2, constructing an unmanned aerial vehicle flight dynamics model influenced by external airflow interference; 3, a PD controller is used as a basic controller, and a UDE-based controller is adopted to estimate and compensate the model uncertainty and multi-source interference; and 4, constructing a state space and an action space for unmanned aerial vehicle robust control optimization based on deep reinforcement learning, and constructing a segmented reward function which focuses on position tracking, attitude stability and control smoothness and safety, thereby improving the adaptive ability of the unmanned aerial vehicle under complex interference.

The invention relates to a multi-unmanned aerial vehicle formation fault-tolerant control method based on an extended state observer. The method comprises the following steps: firstly, establishing a dynamic model of a multi-unmanned aerial vehicle formation; a multi-unmanned aerial vehicle formation communication topological structure is constructed according to the actual demand of a formation task, and information interaction among multiple unmanned aerial vehicles is realized based on a piloting-following method architecture and a distributed control method; establishing a global position tracking error and a speed tracking error by using the states of the adjacent unmanned aerial vehicles; introducing an extended state observer to estimate the unknown state of the formation system in real time; and a sliding-mode adaptive fault-tolerant controller is designed to effectively deal with the influence of actuator faults, system uncertainty and unknown external disturbance on formation performance, and stable control of the multi-unmanned aerial vehicle formation system is realized. According to the method, under the condition that a single unmanned aerial vehicle or multiple unmanned aerial vehicles break down, reliable transmission of task information and effective keeping of the formation can still be guaranteed, and the method has high system robustness and environment adaptability and is suitable for complex flight task scenes.

The invention belongs to the technical field of information technology, and discloses a moxibustion temperature control method and system based on multi-sensor fusion, and the method comprises the steps: obtaining a biopotential signal and position offset data from an acupoint region through a sensor, processing the biopotential signal through a classification algorithm, and obtaining a classified potential distribution mode; on the basis of the classified potential distribution modes, heat conduction influence data between adjacent acupoints are obtained, the position offset data are integrated through an information fusion algorithm, fused physiological state indexes are determined, and the problem of measurement distortion caused by acupoint movement and equipment offset is solved through multi-source data fusion and dynamic error compensation; and meanwhile, the system constructs an accurate potential distribution model by utilizing a spatial calibration and classification algorithm, and temperature control accuracy reaches a millimeter level in combination with real-time position tracking, so that the integrity and reliability of data are remarkably improved.

The invention provides a four-rotor unmanned aerial vehicle visual navigation control method and system suitable for a complex low-altitude environment, and is applied to the field of data processing application. According to the method, a quadrotor unmanned aerial vehicle is taken as an object, and flight-related basic information and environment data of the quadrotor unmanned aerial vehicle are acquired; processing the basic information to establish a flight control basic model, processing environment data and basic model information, establishing a state estimation system, using a depth camera to acquire and process visual information, and combining visual SLAM to realize pose estimation and positioning mapping to obtain environment perception and positioning mapping information; processing positioning mapping and basic model information, transmitting a signal to an embedded controller to control a steering engine, and fusing data to generate a control instruction and positioning tracking information; then, an upper computer monitoring system is constructed, and track planning and monitoring information is generated; and finally, integrating various types of information, and generating target implementation information of the visual navigation unmanned aerial vehicle control system.

The invention discloses a power equipment fault prediction method based on multi-modal data and related equipment, and belongs to the technical field of power operation safety monitoring, and the method comprises the steps: collecting the multi-modal data of a power operation site; inputting the data into a target detection model, determining a target category and position information in combination with a target identity re-association algorithm, and performing position tracking; performing preprocessing according to the channel state information of the real-time position of the target, performing attitude reasoning in combination with a deep neural network to obtain a target attitude, and determining an abnormal state of a target behavior; determining a position area of the target according to the real-time position of the target and an electronic fence technology; and when the target is in the dangerous operation area, determining the abnormal wearing state of the protection equipment of the target personnel based on the image of the target and by adopting a personal protection equipment detection model, and outputting early warning information through edge cloud cooperative early warning. High-precision safety monitoring of an electric power operation site is realized, the personnel behavior identification capability is improved, and the operation risk is comprehensively reduced.

The invention discloses an industrial robot demonstration data acquisition method, a control method and a robot. A demonstration data acquisition system comprises a handheld operation main body and a control processing unit, the handheld operation main body communicates with the control processing unit through the system communication interface; the handheld operation main body is used for an operator to execute demonstration operation, and different operation tools and acquisition units can be installed according to task requirements; the operation tool comprises a glue gun, a clamping jaw, a screw tightening device and the like; the acquisition unit comprises a force / torque sensor used for acquiring force / torque generated in the interaction process of a person and an object, an imaging device used for acquiring RGB-D information of an operation object and a surrounding environment, a position tracking system used for acquiring pose information of the handheld operation main body in a three-dimensional space, and an acceleration used for acquiring the operation process. And an inertial measurement unit for measuring angular velocity and attitude change information. According to the invention, the integrity, accuracy and semantic expression ability of industrial robot data acquisition can be improved.

The invention discloses a multi-unmanned aerial vehicle formation control method based on distributed model predictive control. The method comprises the following steps: describing a motion law of each unmanned aerial vehicle by adopting a state-space equation; modeling a static obstacle as a geometric body, and setting a minimum safe distance; considering position tracking cost, control energy cost, input change cost and formation cost, and establishing an objective function for each unmanned aerial vehicle; adding a CBF security constraint according to whether the security domain has an obstacle avoidance demand or not; constructing an optimal control problem for each unmanned aerial vehicle; in each control period, the optimal control problem is solved; only the first item in the control sequence is adopted as an actual control instruction; and then the system state is updated based on the state-space equation, and prediction and optimization are carried out again in the next period. According to the invention, the problems of obstacle avoidance and formation keeping when the multi-unmanned aerial vehicle formation passes through the obstacle in a complex environment are effectively solved. The method can be widely applied to the field of unmanned aerial vehicle control.

The invention relates to the field of virtual reality, and discloses a multi-user virtual reality experience system, comprising a real-time positioning system; the real-time positioning system is used for capturing space coordinates and action postures of wearing equipment of a plurality of users, and realizing synchronous response of a virtual character and a real action in real time according to action information and position tracking information of each user; the real-time positioning system is also used for driving the virtual role of each corresponding user to move in the virtual scene; a multi-modal environment simulation component; the multi-mode environment simulation assembly is used for simulating various physical environment changes in a virtual scene; a space base; the space construction assembly dynamically adjusts a space topological structure through an electric sliding rail system to restore movement and interaction of a user in a virtual environment in a real environment; the scene generation system generates a virtual scene matched with the user position, the environment and the wall layout in real time. The method has the following effect: the technical problem of large space mapping error in multi-user collaborative interaction is solved.

The invention belongs to the technical field of personnel positioning, and particularly discloses a tunnel construction personnel continuous positioning system based on multi-source data fusion, and the system comprises a positioning tag which comprises a GNSS positioning module, a UWB module, an IMU module, and a main control module; the GNSS base station is used for generating differential correction data; the anchor nodes are used for receiving the UWB signals and recording signal arrival timestamps; the main control module is configured to monitor a GNSS signal in real time, judge that a person is in a ground GNSS available area when the GNSS signal meets a certain condition, and switch to an RTK / PDR fusion positioning mode; and when the GNSS signal is completely lost or the quality is extremely poor, judging that a person enters underground or a GNSS shielding area, smoothly switching to a UWB / PDR fusion positioning mode, and performing positioning by using UWB distance measurement and IMU / PDR. According to the invention, the position tracking continuity of miners moving in different areas of a mining area can be guaranteed, and the coverage capability and adaptability in the whole range of the mining area are ensured.