103results about How to "Stable tracking" patented technology

The invention discloses a flight control method and system of an unmanned aerial vehicle tracking technology, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the following steps: before an unmanned aerial vehicle takes off, starting an airborne sensor in advance, executing parameter initialization and calibration operation, and installing a magnetic marker on a tracking target; acquiring the body position of the unmanned aerial vehicle in the flight process, and sensing the target position of the tracking target in real time by adopting a multi-sensor data fusion algorithm; obstacles existing in the flight path are detected in real time, a three-dimensional environment model is integrated, and the motion state of a tracking target is captured in combination with a follow-up visual tracking algorithm; target tracking data are stored and fed back in real time, and the battery state and the energy consumption speed of the unmanned aerial vehicle are monitored. By combining the magnetic guidance technology, multi-sensor data fusion and a follow-up visual tracking algorithm, high-precision target tracking can be realized in a dynamic complex environment, meanwhile, obstacles are effectively avoided, and the flight stability and tracking precision of the unmanned aerial vehicle are ensured.

The invention relates to a multi-modal target identification and tracking prediction method based on a laser radar and a binocular camera, and belongs to the field of robot / unmanned system perception identification. Comprising the following steps: preprocessing a target two-dimensional image acquired by a binocular camera and target point cloud data acquired by a laser radar to obtain a preprocessed two-dimensional image and point cloud data; s2, performing two-dimensional target detection and tracking detection based on the preprocessed two-dimensional image to obtain a two-dimensional target detection and tracking result; s3, performing three-dimensional target detection based on the preprocessed point cloud data and the two-dimensional target detection and tracking result to obtain a three-dimensional detection result; and S4, based on the pixel coordinates in the two-dimensional target detection and tracking result and the pixel coordinates of the vertexes of the target point cloud boundary in the three-dimensional detection result, target fusion tracking prediction is carried out, and the target ID, the position, the category and the target point cloud boundary are obtained. And accurate identification and tracking of the target are realized.

The invention discloses a high-robustness target loss detection and rapid tracking method, and belongs to the technical field of calculation, reckoning or counting. By combining the YOLO algorithm and the MOSSE algorithm, the method can actively search the target and realize re-detection and rapid recovery tracking of the target after the space target is lost for a long time. And when the target moves out of the visual field, the MOSSE algorithm cannot continue tracking, and at the moment, the YOLO algorithm is automatically started to carry out target detection so as to confirm that the target appears again. Once a target is detected, the features of the target are immediately transmitted to the MOSSE algorithm so as to quickly switch back to the tracking mode, thereby ensuring the continuity and stability of tracking. The key point of the method is that a switching mechanism can quickly respond between target loss and reoccurrence, and time delay in the detection and tracking process is remarkably reduced.

The invention discloses a spacecraft attitude tracking sliding mode control method based on an all-drive system method, and belongs to the technical field of spacecraft control. The method is based on an all-drive system method, and a second-order all-drive system model of the system is established for a spacecraft attitude tracking system with external disturbance and actuator saturation. And an anti-saturation auxiliary system is designed to reduce the influence of saturation of the actuator. Meanwhile, a disturbance observer based on a fixed-time multivariable superhelix system is designed and used for estimating external disturbance. Based on an all-drive system method, a linear steady closed-loop system is obtained through a state feedback controller, and a sliding mode controller based on the all-drive system method is designed, so that the system can stably track a target attitude under the influence of external interference, and effective control of a spacecraft attitude tracking system is realized.

The invention discloses a fault-tolerant control method for the rotating speed of a driving motor of a deep-sea robot. The fault-tolerant control method comprises the following steps: S1, establishing a discrete state space model based on electrical and mechanical equations of the motor; s2, determining a fault characteristic parameter to describe the loss probability of the control signal when the control signal is attacked and interfered; s3, calculating a feedback control matrix according to the fault characteristic parameters; s4, calculating a feedforward control matrix according to the fault characteristic parameters; s5, constructing a controller based on the feedback control matrix and the feedforward control matrix, calculating a control signal online and transmitting the control signal to an execution mechanism; and S6, the execution mechanism receives the control signal, and if the signal is lost, the stored control signal is adopted to replace the control signal, and control is executed in real time. According to the invention, fault-tolerant control of the motor rotating speed can be realized under the condition that the control signal is randomly lost, and the motor rotating speed can stably track a predetermined trajectory.

The invention belongs to the technical field of particle motion track recognition, and particularly relates to a method for tracking a large number of moving particles based on image recognition, the moving particles suitable for the method are particles undergoing active Brownian motion, the same color mark is made on each moving particle, and image acquisition is performed through a microscope or a video recorder; identifying a pixel point cluster corresponding to the color mark on each moving particle by setting an RGB value; determining moving particles corresponding to the color marks by setting a threshold value in the pixel point cluster; taking a pixel point corresponding to the average value position of all the position data as the position of the moving particle; and recording the motion trail of the moving particles in a period of time. According to the method, the marks are introduced to the particles, the algorithm is developed to identify the marks, calculate and record the average positions of the pixel points corresponding to the marks, the particles are tracked, the colored marks can exist stably and do not interfere with one another, and it can be ensured that a large number of particles can be tracked for a long time.

The invention discloses a human-machine motion mapping control system and method based on a hybrid LM-GA algorithm, and relates to the field of human-machine motion mapping control. According to arm joint position information obtained by an optical motion capture system, a corresponding arm joint rotation angle is obtained by using a geometric solution in inverse kinematics; secondly, joint angle data of the mechanical arm are obtained through an inverse kinematics iterative algorithm, and the joint angle data are verified in the virtual environment; then track data of the tail end of the mechanical arm is obtained through an optical motion capture system, the motion reproduction precision of the mechanical arm can be evaluated by comparing the track data of the tail end of the human arm and the track data of the tail end of the mechanical arm, and the accuracy and reliability of a human-machine motion mapping system are ensured; by continuously approaching the optimal solution, the iterative algorithm can gradually adjust the angle of the robotic arm joint until a predetermined end effector position is reached.

The invention discloses a composite loop tracking method of an anti-interference navigation signal receiver. The method comprises the following steps: copying a pseudo code and a carrier corresponding to a satellite signal based on a roughly captured signal; performing coherent operation to obtain a coherent integral value; performing FLL frequency discrimination, PLL phase discrimination and FFT frequency discrimination on the coherent integral value, and performing weighting processing to obtain a frequency error; carrying out carrier loop filtering on the frequency error to output a carrier loop tracking result; performing incoherent accumulation to obtain an incoherent integration result, and performing DLL phase discrimination to obtain an epoch code loop phase difference; performing code loop filtering on the epoch code loop phase difference to output a code loop tracking result; calculating a first frequency control word corresponding to the code loop according to the wave loop tracking result; and controlling code loop updating based on the first frequency control word and the code loop tracking result. According to the invention, stable tracking of navigation signals in different dynamic and different interference scenes can be realized.

The invention discloses a phased array radar for unmanned aerial vehicle detection based on multi-target tracking, which comprises a radar antenna array, a signal processing unit, a control unit and a display unit, and is characterized in that the control unit comprises a motion state judgment module; a filtering switching module; a Kalman filtering module; a particle filtering module; and a data association module. The method can achieve the high-precision detection and positioning of an unmanned plane target, has a multi-target tracking capability, and improves the detection efficiency and accuracy of the unmanned plane.

The invention discloses an automobile stamping part dynamic grabbing and boxing method and system based on a flexible manipulator, and the method comprises the steps: obtaining the three-dimensional point cloud data of a target stamping part on a conveyor belt in real time, extracting the key geometric features of the target stamping part, and determining the type, position and posture of the target stamping part; the type, the key geometric features and the posture of the target stamping part are input into a pre-trained contact point prediction model, and the contact point of the flexible manipulator is predicted; according to the position and posture of the target stamping part and the predicted contact point of the flexible manipulator, trajectory planning of the flexible manipulator is conducted, and a planned trajectory is obtained; and target stamping parts are grabbed and boxed according to the planned track of the flexible manipulator. The stamping part grabbing device can flexibly meet the grabbing requirements of stamping parts of different shapes, sizes and positions and has good adaptability to special-shaped stamping parts of complex shapes, and the grabbing precision and the boxing efficiency are improved.

The invention provides a multi-source fusion positioning processing method, device and equipment for a vehicle terminal, and relates to an automatic driving technology, and the method comprises the steps: obtaining inertia measurement data, wheel speed data, laser point cloud data and other source data of the vehicle terminal in a current frame in a driving process of the vehicle terminal; performing pre-integration on the inertia measurement data and the wheel speed data to generate a pre-integration result; and performing feature matching on the laser point cloud data of the current frame and the laser point cloud data of the previous frame of the current frame, and if the current frame is determined as the key frame, determining the point cloud of the key frame. Fusing the pre-integration result, the point cloud of the key frame and other source data to generate a current pose of the vehicle terminal in the key frame; and determining a target pose of the vehicle terminal according to the current pose of the key frame, the point cloud of the key frame, the historical point cloud of the key frame and a preset point cloud base map. According to the method provided by the invention, the technical problems of low positioning precision and poor robustness of the vehicle terminal are solved.

The invention particularly relates to an unmanned aerial vehicle autonomous tracking trajectory planning method. The method comprises the steps that firstly, an unmanned aerial vehicle obtains the speed and position of a target at the current moment according to a target recognition result; then motion primitive expansion is carried out according to the position and speed information to predict the position of the target at a certain moment in the future, and meanwhile Markov chain state transition is carried out to predict the probability that the target appears at a certain position. And generating viewpoints beneficial to observation of the target near all potential points according to the potential points generated by the prediction result. Judging whether the target is in the field of view of the unmanned aerial vehicle, and if so, determining that the path planning end point of the unmanned aerial vehicle is a globally optimal observation point; and if so, sequentially traversing the viewpoints with the highest cost to try to recapture the target. And finally, according to a decision result, constructing a multi-constraint combined B-spline trajectory optimization model, and solving a final optimal tracking trajectory by taking the yaw angle as an independent optimization variable. According to the scheme, the unmanned aerial vehicle can complete robust and efficient autonomous tracking in a complex and dense environment.

The invention discloses a comprehensive maneuvering motion planning method for sudden lane changing of an automatic driving vehicle, and relates to the technical field of automatic driving vehicles. The method comprises the following steps: receiving vehicle driving state information, wherein the driving state information comprises the positions, speeds, accelerations, course angles and yaw velocities of a vehicle and surrounding vehicles; constructing an MOBIL lane changing decision model, making a lane changing decision by using the model, judging whether a lane changing operation is carried out or not, and if so, carrying out calculation to obtain a lane changing movement track taking the minimum lane changing time and the minimum lane changing distance as optimization targets on the basis of a quintic polynomial form; and establishing a minimum safe collision-free circle based on a vehicle-vehicle geometrical relationship. According to the lane changing trajectory quintic polynomial optimization method based on the steady-state steering principle, the motion planning trajectory capable of achieving stable tracking of the vehicle under the emergency lane changing condition and avoiding the sideslip risk can be generated, and the method has good application prospects for autonomous collision avoidance and safe driving of the vehicle in the emergency scene of the dynamic environment.

The invention discloses a 6D pose detection and visual servo method of a bolt based on 3D template matching. The method comprises the following steps: S1, establishing a CAD model of the bolt; s2, acquiring a bolt image, and performing image preprocessing; s3, performing bolt feature information detection to obtain feature points, feature lines and feature surfaces; s4, bolt initial point matching is carried out, and matched feature information is stored; s5, performing tracking and 6D pose estimation on the bolt, and performing subtraction on the estimated 6D pose and a set expected pose as input of a visual servo algorithm; s6, acquiring the movement speed of the depth camera; s7, the movement speed of the mechanical arm is obtained and sent to the mechanical arm, and the mechanical arm is made to move; s8, the steps S5-S8 are repeated until the pose error in the step S5 is smaller than the pose error threshold value; and S9, a move L instruction is used for enabling an end effector installed at the tail end of the mechanical arm to conduct bolt alignment. According to the invention, the bolt can be stably tracked and the 6D pose of the bolt can be detected in real time.

The invention relates to a dynamic target tracking system and method based on image detection and a holder. The system comprises a data processing terminal, a holder, a camera, a single-chip microcomputer and a motion unit. When the system carries out target detection, model training is carried out by collecting and marking target images and corresponding negative sample data under diversified environmental conditions, and target detection is carried out through subgraph division processing, so that the robustness, speed and detection precision of target detection are improved. When the target is dynamically tracked, the deviation angle between the central point of the target marking frame and the central point of the image picture is converted into a PWM signal to carry out real-time accurate control on the holder steering engine, and the holder speed intelligent coefficient and the decision boundary are introduced, so that the movement of the holder is smoother and more stable, and the target is continuously kept at the center of the image picture. And the tracking distance between the holder and the target is monitored and maintained through the laser radar, so that the holder can stably track the moving target.

The present invention discloses a flight control method and system of unmanned aerial vehicle tracking technology, which relates to the field of unmanned aerial vehicle control technology. The method comprises the following steps: before the unmanned aerial vehicle takes off, the airborne sensor is pre-started, parameter initialization and calibration operations are performed, and magnetic markers are installed on the tracking target at the same time; the body position of the unmanned aerial vehicle during flight is obtained, and a multi-sensor data fusion algorithm is used to sense the target position of the tracking target in real time; obstacles existing in the flight path are detected in real time, integrated into a three-dimensional environmental model, and combined with a follow-up visual tracking algorithm to capture the motion state of the tracking target; target tracking data is saved and fed back in real time, and the battery status and energy consumption rate of the unmanned aerial vehicle are monitored. By combining magnetic guidance technology, multi-sensor data fusion and a follow-up visual tracking algorithm, the present invention can achieve high-precision target tracking in a dynamic and complex environment, while effectively avoiding obstacles and ensuring the flight stability and tracking accuracy of the unmanned aerial vehicle.

The invention relates to a quadruped robot single-leg sliding mode control method based on a disturbance observer, and belongs to the technical field of robot control. Firstly, a foot end track of a single leg of the quadruped robot in a swinging stage is established; then, establishing a kinematics model of a single leg of the quadruped robot to realize conversion between a foot end target position and a corresponding joint angle; on the basis, a nonsingular terminal sliding mode controller is designed to cope with dynamic control under the non-interference condition; finally, a final composite control strategy is designed in combination with a sliding mode controller and interference estimation, stable tracking of the joint angle of the robot is achieved, and therefore the anti-interference performance of the single-leg system is improved. According to the method, a composite controller is designed by combining a finite time disturbance observer and a nonsingular terminal sliding mode controller, so that the foot end trajectory tracking precision and the system robustness of the quadruped robot are improved, and the uncertainty caused by external disturbance and modeling errors of the robot is eliminated.

The invention discloses a low-altitude unmanned aerial vehicle countering monitoring system based on state feature decoding, relates to the technical field of unmanned aerial vehicle prevention and control, and is used for solving the problem of poor low-altitude target detection and countering. According to the invention, through closed-loop management and control of heterogeneous radar networking detection, signal level fusion processing, threat assessment and resource scheduling and intelligent countering decision, accurate identification and directional countering of the low-altitude unmanned aerial vehicle are realized; according to the method, the detection precision and the micro-Doppler feature extraction capability of a low-speed small target are improved by combining a virtual aperture synthesis technology, then unbalanced resource allocation is implemented based on a threat level, the system efficiency is optimized, and finally seamless migration of an interfered node task is realized by constructing a signal interference situation map. And continuous tracking and effect evaluation on the target are maintained in the active radio frequency countering process, so that the overall efficiency and reliability of low-altitude security and protection in a complex environment are effectively improved.

The invention discloses a pulse signal source tracking method based on a robust Kalman filter. The method comprises the following steps: reading a target state vector and error covariance matrix at a previous moment, and observing a noise covariance matrix; a variational Bayesian parameter before iteration, a target state vector and an error covariance matrix are initialized; starting iteration, and calculating the quadratic form sufficient statistics of the observation error and the forecast error; updating variational Bayesian parameters; updating the forecast error covariance matrix and the observation noise covariance matrix; calculating a target state vector and an error covariance matrix of the iteration; when the variation of the target state vector of the current iteration and the target state vector of the last iteration is smaller than the tolerance, ending the iteration, and outputting the target state vector at the current moment, the error covariance matrix and the observation noise covariance matrix; compared with a deterministic integral sampling method, the method provided by the invention adopts adaptive volume sampling to realize higher tracking precision under lower calculation cost.

The present application belongs to the field of power prosthesis control, and specifically discloses a method, device and equipment for adaptive collaborative control of a power thigh prosthesis, including: obtaining examples of motion trajectories of healthy users crossing obstacles of different heights and / or different widths; when the prosthesis needs to cross an obstacle, the obstacle crossing process of the prosthesis is divided into the initial stage of obstacle crossing, the middle stage of obstacle crossing and the final stage of obstacle crossing according to the hip joint angle and angular acceleration of the residual limb; in the early stage of obstacle crossing, a knee joint trajectory example is selected and combined with the hip joint angle to determine the knee joint control angle of the prosthesis; in the middle stage of obstacle crossing and the final stage of obstacle crossing, two knee joint trajectory examples are selected for interpolation, and the knee joint control angle of the prosthesis is determined in combination with the hip joint angle, and the dynamic motion primitive algorithm is used to smoothly connect the knee joint trajectories of the latter part of the middle stage of obstacle crossing and the front part of the final stage of obstacle crossing, and the knee joint control angle of the final stage of obstacle crossing is updated. Through this application, adaptive collaborative control of a power thigh prosthesis is achieved to reduce the risk of falling.

The invention discloses a visual tracking and positioning method and system for an ocean net cage handrail. The method comprises the following steps: preprocessing an image collected by a monocular RGB camera; identifying the ocean net cage handrail in the preprocessed image, preliminarily detecting the position of the ocean net cage handrail by adopting a target detection algorithm based on deep learning, and further confirming the position of the ocean net cage handrail in combination with a feature matching method; continuously tracking the identified ocean net cage handrail, predicting the motion state of the handrail by adopting Kalman filtering, and realizing stable locking of a handrail target in combination with mean shift target tracking, time sequence information fusion and motion compensation technologies; and estimating the distance between the unmanned ship and the ocean net cage by using monocular vision and scale information according to the identified and tracked ocean net cage handrail. Based on the economical and practical monocular camera, accurate identification, stable tracking and distance estimation of the ocean net cage handrail are realized.

The invention relates to the technical field of machine vision and intelligent monitoring, and discloses an automatic camera tracking method and device based on machine vision, and the method comprises the steps: obtaining a real-time video frame sequence, and carrying out the preprocessing of the real-time video frame sequence, and obtaining an initial image set; identifying features of the initial image set, judging an abnormal state, and fusing the data to generate behavior change trend data; a smooth trajectory is predicted based on the data, and a parameter adjustment data set is obtained by combining camera position calculation deviation; adjusting camera parameters according to the generation instruction, detecting shielding and fusing trajectory data to obtain real-time tracking parameters; and applying parameters, complementing missing frame verification, updating a process and reextracting features, and performing time sequence analysis in case of abnormality to obtain a final tracking logic sequence. According to the method, accurate target locking can be realized, and the stability and accuracy of abnormal emotion tracking in a crowded environment are improved.

The invention discloses a 3d target matching method based on a trajectory intersection-to-union ratio. The method comprises the following steps: step 1, point cloud data detection; step 2, global coordinate conversion; step 3, GIOU preliminary matching is carried out; and step 4, the T-IOU reallocates the trajectory. The method has the beneficial effects that according to the technical scheme, based on actual data and application, a 3d frame matching method combining a GIOU index and a TIOU index is designed according to the characteristics of linear movement and smooth track of objects such as people, vehicles and the like in a traffic scene, so that the track generated through matching is more accurate and more coherent; according to the method, firstly, a GIOU index is used for matching, and compared with a traditional iou index, the index considers a non-overlapping part between two target crabs, can reflect an alignment mode of the two target crabs, and is suitable for linearly moving objects in a traffic scene; and for a dispute detection frame capable of simultaneously matching a plurality of adjacent tracks in the GIOU matching process, the T-IOU is used for re-matching, so that the target object can be matched to a correct track.

The application discloses a stable domain constraint trajectory planning and control method for a modular distributed electric drive heavy load vehicle, comprising the following steps: constructing a nonlinear dynamics model of the modular distributed electric drive heavy load vehicle, fitting a tire lateral force model through a rational polynomial, and establishing a vehicle system state space expression based on the rational polynomial form; adopting a sum of squares planning method to estimate the stable domain of the vehicle under different vehicle speeds, road adhesion coefficients and front, middle and rear wheel turning angles, and analyzing the influence of each state quantity on the shape and boundary of the stable domain; embedding the stable domain constraint into a trajectory planning layer based on a quintic polynomial to generate an obstacle avoidance trajectory meeting the dynamics stability; designing a multi-axle proportional steering strategy based on a steady state assumption, and combining a nonlinear model predictive controller to realize trajectory tracking control, so that the stability and control feasibility of the vehicle in a high-speed dynamic obstacle avoidance process are ensured. Through construction of a high-speed dynamic obstacle avoidance simulation scene, the effectiveness, stability and control feasibility of the stable domain constraint trajectory planning and control framework are verified.

The present invention belongs to the field of aerospace technology, and discloses an embedded method for removing sailboards of non-cooperative infrared space targets, comprising the following steps: S1, merging a target body and its sailboard by utilizing the principle that the infrared radiation characteristics of the target body and its sailboard are similar; S2, removing the target sailboard by utilizing the coaxial characteristics of the body and the sailboard of the non-cooperative target; S3, recalculating the center of mass of the target body by utilizing the four vertex positions of the target body. The present invention calculates the center of mass position of the non-cooperative target body in real time by identifying the features of the infrared non-cooperative target, and can accurately obtain the center of mass of the target body, especially when the sailboard of a large non-cooperative tumbling target exceeds the imaging field of view, thereby solving the problem of rapid tracking stability of large targets and applying it.

The present application provides a kind of offshore area navigation safety control and optimization method, comprising: step 1, the multi-source observation data of offshore navigation target is collected, and pre-processing is carried out, to obtain the space-time observation correlation graph;Step 2, the dynamic credibility of each observation node is evaluated using multi-modal consistency graph neural network;According to the obtained dynamic credibility, an adaptive weighted factor graph model is constructed, and the system state is solved to obtain the optimal fusion track, the corrected environmental parameter and the posterior error covariance matrix;Step 3, based on the optimal fusion track, the corrected environmental parameter and the posterior error covariance matrix, a regional risk field is constructed;Step 4, based on the regional risk field, a multi-objective collaborative optimization model is constructed and solved to obtain the navigation control parameter;Step 5, execute navigation control parameter, and adopt multilevel closed loop feedback to optimize, complete offshore area navigation safety control and optimization.

The invention relates to an airborne end'low-slow-small 'target identification tracking method and system based on improved YoV5 and ECO, belongs to the technical field of unmanned aerial vehicle target identification and tracking, and solves the problems that'low-slow-small' target identification accuracy is low and tracking is easy to lose in the prior art. Adding a small-scale detection head P2 at a detection end to obtain the detection end of the low-slow-small target detection model; adding a feature fusion layer F2 corresponding to the detection head P2 to a neck network of the YoV5 model, and adding a CBAM model to each feature fusion layer of the neck network to obtain the neck network; training a low-slow-small target detection model based on the training set, performing low-slow-small target detection identification on a target image acquired in real time through the trained low-slow-small target detection model, and outputting a target identification result; and determining a tracking target based on the target identification result, and continuously obtaining continuous frame images of the tracking target based on a preset tracking algorithm.

A method for planning an autonomous landing safe trajectory suitable for UAV inspection is proposed. After the UAV takes off, it acquires the current pose of the UAV, the point cloud map information of the surrounding environment, and the spatial orientation of the target point. By establishing the spatial transformation relationship between the camera coordinate system and the body coordinate system, the orientation vector relative to the ground target is calculated, and the target point is generated within the radar-sensible area. Based on the generated target point, an optimization function that minimizes the fourth derivative is constructed based on multiple high-order polynomials to generate a smooth trajectory with continuous position, velocity, and acceleration. The initial trajectory is transformed into a B-spline curve, and local trajectory segments with collision risk are optimized. A segmented penalty mechanism is designed based on the boundary threshold to guide the trajectory away from the boundary area. The time span is adjusted by calculating the trajectory derivative over-limit ratio, and a trajectory similarity cost function is introduced to ensure that the final optimized trajectory maintains the original geometric structure, thereby achieving trajectory control optimization and safe landing of the UAV.

This invention provides a multi-channel image sensor data transmission system and optoelectronic system based on FPGA-PCIe, belonging to the technical field of video processing and acquisition systems. The multi-channel image sensor data transmission system includes an FPGA circuit board, external memory, and a back-end image processing platform. The FPGA circuit board includes an integrated image processing module, a bus control module, and a PCIe interface control module. Utilizing an FPGA to process and acquire multiple sensor video streams saves hardware costs and circuit board area. It also ports various operation operators to the FPGA, enabling real-time processing and acquisition of the currently transmitted video stream, improving the real-time performance of the operation. Simultaneously, it achieves control and transmission of multiple video streams, meeting the requirements for data transmission agility and high speed. This solves the problem in existing methods where inconsistent video resolutions and frame rates from different image sensors lead to insufficient data bandwidth to meet protocol requirements, resulting in the inability of PCIe devices at both ends of the FPGA to communicate normally.

The invention discloses a control method for accurately and quickly obtaining various elements based on a multi-network model, comprising the following steps: setting an element solving neural network model to obtain shooting elements in a windless state, wherein the shooting elements include a sight, a range and a ballistic height; setting a longitudinal wind neural network model to obtain the average longitudinal wind during the flight of a shell according to the sight and the ballistic height in the shooting elements in the windless state, as well as the actual wind speed and wind direction, as the average ballistic longitudinal wind; setting an element correction neural network model, taking the average ballistic longitudinal wind as input, outputting a sight correction amount, correcting the shooting elements in the windless state, and obtaining the final shooting elements; adjusting the launch platform according to the final shooting elements, and launching the shell. The control method for accurately and quickly obtaining various elements based on a multi-network model provided by the invention fully considers real-time meteorology, can improve the accuracy of element solving, has a small amount of calculation, is short in time consumption, does not require cumbersome firing table query, can be deployed in a fire control computer, and can effectively avoid missing the target.