319 results about "Point tracking" patented technology

The invention relates to the technical field of six-degree-of-freedom mechanical arm control, in particular to a six-degree-of-freedom mechanical arm trajectory tracking control method and system. The method comprises the following steps that an operation planning track sequence of the six-degree-of-freedom mechanical arm is obtained; executing real-time trajectory point tracking according to the operation planning trajectory sequence, and performing tail end attitude resolver analysis to generate tail end attitude resolver coupling data; when an end effector of the six-degree-of-freedom mechanical arm executes high-speed rotation load operation, end gyroscopic effect data are calculated; analyzing and estimating an uncontrolled offset vector according to the tail end attitude resolver coupling data and the tail end gyroscopic effect data; and dynamic offset vector correction processing is conducted on the estimated uncontrolled offset vector, so that intelligent control over operation of the six-degree-of-freedom mechanical arm is executed. According to the method, accurate tracking control over the tail end pose of the mechanical arm under the high-speed rotating load working condition is achieved through aerodynamic disturbance torque processing and rigidity weakest shaft recognition, and track deviation caused by composite disturbance is effectively compensated.

The invention discloses a structural deformation measurement method based on geometric correction of a multi-view panoramic camera. A method for measuring bridge displacement by using a single multi-view panorama camera is provided, multi-view segmentation is performed on a panorama image according to a panorama camera equivalent model and a cube projection method, and independent calibration and correction are performed to obtain a distorted image capable of covering a 360-degree full-field range; therefore, a single panoramic camera is equivalent to a vertical four-camera system without overlapped view fields, so that the reference target in any direction can be used for displacement correction. The invention further provides a camera disturbance elimination method based on a multi-view circle center array target and pose transformation matrix purification optimization, and a coordinate system transformation matrix after purification optimization is obtained through a high-precision multi-feature point tracking algorithm from camera single-view moving target pose measurement. And obtaining a multi-view geometric correction theoretical model of any view of the measurement target and the reference target in a 360-degree range by utilizing a multi-view equivalent camera fixed connection relationship.

The invention provides a sensor dynamic zero point correction method based on deep Kalman filtering, and relates to the technical field of sensor zero point tracking. The method comprises the following steps: acquiring voltage signal zero point output time sequence data of a sensor; the obtained data serve as input of a deep learning network model based on Kalman filtering, the model processes the data, noise and zero drift interference are removed from the output data, dynamic zero correction is achieved, and a real measurement value of the sensor is obtained. The deep learning network model based on Kalman filtering comprises an RNN recurrent neural network module, a Pyro probability programming module and a variational inference module. According to the invention, the external interference is simulated by using the environmental vibration to carry out the experiment, and the result shows that the deep Kalman filter model provides a new idea for the dynamic zero correction of the force transducer.

The invention is applicable to the technical field of medical image processing and mixed reality, and provides a laparoscopic surgery mixed reality navigation method based on deep learning and dynamic point tracking, which comprises the following steps: dynamically registering a three-dimensional model containing kidney, tumor and vessel with an initial frame of a laparoscope through a mixed reality alignment technology; the method comprises the following steps: constructing an operating forceps motion sensing model based on a time sequence deep neural network, realizing real-time control and parameter locking of a three-dimensional model pose, dynamically updating a two-dimensional feature point set by adopting a multi-feature-point combined tracker, constructing a candidate feature combination through a cross-quadrant sampling strategy, and constructing an operating forceps motion sensing model; a candidate feature combination is generated through a four-quadrant division and cross-regional sampling strategy, an optimal camera pose parameter is generated in combination with a re-projection error and pose continuity constraint, and an operation video is dynamically covered with a semitransparent three-dimensional model. The method can significantly enhance the spatial perception capability of the kidney anatomical structure, reduce the registration error of the kidney in the three-dimensional integrated kidney structure model and the laparoscope video, and improve the navigation precision.

The invention discloses a cross-language application program vulnerability mining method based on static analysis, which adopts a nested cross-programming language pointer analysis method and a micro-service cross-programming language taint tracking method to effectively solve the limitation of processing nested cross-language control flow and micro-service cross-language data flow. A nested language semantic boundary is accurately positioned by constructing a nested byte code, and a data stream is completely tracked by utilizing an interface relay technology, so that the detection precision is improved. According to the method, part of multi-end data streams and complex control streams of cross-programming language applications can be uniformly processed, and the analysis process in a cross-programming language environment is simplified; by nesting cross-programming language control flow diagram construction and double-end / multi-end data flow diagram construction, a unified analysis framework is constructed, seamless cooperation of static analysis among different languages is achieved, the analysis cost is reduced, the analysis efficiency is improved, efficient and accurate vulnerability mining is achieved in a complex cross-programming language application program, and the method is suitable for application and popularization. And the reliability of cross-language vulnerability detection is improved.

The invention discloses an optical imaging zoom point selection correction method and system, which can correct the deviation between a theoretical model and actual lens characteristics in real time through a peak point tracking algorithm and offset vector calculation in a second stage, effectively solve the problem of individualized errors caused by lens group assembly tolerance, material refractive index difference and the like, and improve the correction accuracy. The stratified sampling strategy of the first stage is combined with the slope interpolation of the third stage, untested points in a theoretical curve are filled, a high-precision focusing curve is generated, the full range of zooming is covered, and local focusing failure caused by traditional uniform sampling is avoided; according to the method, curve jitter caused by test noise or mechanical vibration is eliminated, a smooth zoom focusing curve is output, the anti-interference capability in actual control is improved, frequent jitter of a focusing motor is avoided, a search strategy is dynamically adjusted in combination with definition data, an actual peak point is quickly locked, and the problem of overmodulation or slow convergence caused by a traditional fixed step length is solved.

The invention relates to the technical field of high-precision positioning, and discloses a linear module high-precision positioning control method and system, and the method comprises the steps: carrying out the feature point extraction and feature point tracking of a motion image sequence of a linear module, and obtaining displacement measurement data and vibration feature data; performing nonlinear compensation on a linear module according to the displacement measurement data and the vibration characteristic data, and constructing a standard linear state equation; performing sliding mode surface design and gain parameter calculation based on the standard linear state equation to obtain an initial sliding mode controller parameter; track interpolation and motion feedback are carried out on the linear module according to the initial sliding mode controller parameters, and real-time position deviation data are obtained; the initial sliding mode controller parameters are iteratively optimized based on the real-time position deviation data, and target sliding mode controller parameters are obtained.According to the method, impact in the acceleration and deceleration process is reduced, system vibration is effectively reduced, and the positioning precision is improved.

The invention discloses a frequency spectrum refining frequency point tracking type optical frequency domain reflection distributed sensing method, which comprises the following steps of: inputting probe light into a sensing optical fiber engraved with weak reflection points to obtain backscattered light; carrying out coherent frequency mixing on the backscattered light and the reference light, and then carrying out photoelectric conversion to obtain an electric signal containing weak reflection point information; aD sampling is carried out on the electric signal containing the weak reflection point information, and a first discrete signal sequence is obtained; performing frequency spectrum refinement on the first discrete signal sequence to obtain a refined beat frequency spectrum; performing peak searching operation on the refined beat frequency spectrum to obtain weak reflection beat frequency points at different positions in different measurement periods; extracting phase values of beat frequency points of weak reflection points at different positions in different measurement periods by utilizing Hilbert transform; and carrying out phase demodulation on the phase values of the beat frequency points of the weak reflection points at different positions in different measurement periods to obtain the change condition of parameters measured by the sensing optical fiber. Errors caused by the Doppler effect can be eliminated.

The invention belongs to the field of power distribution network monitoring, and provides a power distribution box damage behavior monitoring method and system based on human body posture estimation, and the method comprises the steps: obtaining a monitoring video of a power distribution box terminal for preprocessing, and obtaining a preprocessed video frame; based on the preprocessed video frame, performing posture recognition by using a pre-trained human body and distribution box posture recognition model to obtain a human body and distribution box posture recognition result; key point tracking and identity association are performed by using a human body and distribution box posture recognition result, a space-time skeleton diagram is constructed, and the space-time skeleton diagram is screened to generate a skeleton space-time sequence; on the basis of the skeleton space-time sequence, a pre-trained space-time diagram convolutional network model is utilized to identify a damage behavior, and a damage behavior identification result is obtained; when the confidence coefficient in the damage behavior recognition result exceeds an alarm threshold value, alarm information is generated, and an alarm process and safety linkage control are triggered. According to the invention, accurate monitoring and real-time early warning of the damage behavior of the distribution box are realized.

The invention discloses an extended target feature point tracking method based on motion characteristic compensation, and belongs to the field of target tracking of computer vision, and the method comprises the steps: firstly constructing a matching feature optimization method based on robust feature points, reducing the number of target feature point mismatching, and achieving the estimation of optimal transformation model parameters; secondly, a tracking prediction correction method based on Kalman filtering constraint is designed, and the uncertainty of the matching tracking model is better compensated; and finally, designing a long-time and short-time fusion feature template updating strategy, fusing historical information and current target state information, enhancing the robustness of template features, effectively avoiding noise superposition, and realizing long-time-sequence stable target tracking. According to the method, the image space information and the interested feature point information are interactively interpreted in the feature matching and prediction process, so that the model of the matched features is more robust, the prediction target position error is smaller, and the long-time tracking result is more stable and accurate.

The present invention relates to the field of measurement and testing technology, and specifically, to a differential pressure sensor zero-point tracking self-calibration method, system, device and storage medium. The differential pressure sensor zero-point tracking self-calibration method comprises the following steps: Environmental data acquisition: obtaining current environmental parameters through a detection module. The present invention saves manpower and time costs and improves the user experience of the product by setting an automatic zeroing and self-calibration mechanism without the need for manual intervention by the user. It also effectively maintains the long-term stability and accuracy of the sensor by real-time monitoring of the drift caused by temperature and humidity and performing compensation. At the same time, by using a filtering algorithm for calculation, it can completely eliminate the noise and drift generated by the sensor during long-term use, protect and extend the service life of the chip. In addition, the status of the device can be fed back in real time through the display module, so that the user can clearly understand the current working status of the system, thereby improving the confidence and satisfaction in the use of the device.

The invention discloses a virtual hand grabbing method and system for mixed reality interaction, which is based on a hinge proxy hand model, utilizes a hand tracking device and a physical engine, and fuses hand key point tracking data and a force closing algorithm, and is a grabbing method which is based on physical simulation, is real-time and efficient and does not need to predefine object information. High-precision and real-time hand-object interaction is realized, a virtual hand observed by a user in a mixed reality picture is effectively prevented from penetrating through a virtual object, and the interaction experience in VR / AR equipment is remarkably improved. Moreover, the method supports the real-time adjustment of the dynamic grabbing gesture based on the surface of the object, and is suitable for various VR / AR devices.

The invention provides a human body posture abnormity assessment method based on a body measurement all-in-one machine, and the method comprises the steps: obtaining the video data, collected by multiple cameras, of the bending and foot touching actions of a subject, carrying out the preliminary positioning of a spine, a lumbar vertebra and thoracic vertebra transition region, and key points of legs, and extracting the coordinates of the key points; leg joint angle change is calculated according to the leg key point coordinates, if the angle change amplitude is lower than a preset threshold value, it is judged that leg flexibility is insufficient, a key point tracking strategy is adjusted, and the tracking density of a leg area is increased; according to the posture abnormity score, generating a posture abnormity report of the bending and foot touching actions of the subject, and marking specific positions of spine curvature abnormity, stiffness increase of a transition area between the lumbar vertebra and the thoracic vertebra and insufficient leg flexibility; and dynamically updating a key point tracking strategy and an attitude anomaly judgment threshold according to the action normativity score, and optimizing the accuracy and real-time performance of subsequent action evaluation.

The invention relates to the technical field of robot tactile perception, aims to solve the technical problems that an existing visual tactile sensor is low in precision, poor in real-time performance, incapable of adapting to large deformation and the like during three-dimensional reconstruction, and particularly relates to a binocular visual tactile sensor based on a color block mark point array and a real-time high-precision three-dimensional reconstruction method. According to the method, high-precision three-dimensional reconstruction is completed based on a binocular visual tactile sensor, image preprocessing, color block mark point identification, center coordinate calculation and color discrimination are included, the position of a color block in a mark point array is determined based on coding information and mark point tracking, matching is completed, and finally point cloud is calculated to achieve three-dimensional reconstruction. According to the method, the time spent on part matching is less than 10ms, 400 pieces / cm < 2 > dense point cloud data can be obtained, the method can adapt to the loss of mark points under the condition of large deformation, and the method has an important value for improving the performance and safety of a robot in various complex tasks.

The invention relates to a monocular vision dynamic SLAM method based on semantics, and belongs to the technical field of computer vision. The method comprises the following steps: 1) carrying out semantic segmentation on a monocular vision image by adopting a deep learning model, and identifying an unknown motion state region and a pure static state region; 2) performing data enhancement on the original image; (3) extracting a SuperPoint feature point by using a SuperPoint network; 4) matching the SuperPoint feature points of the pure static region, and obtaining feature correspondence through confidence coefficient screening; 5) ensuring the accuracy and robustness of subsequent pose estimation through constant-speed motion model tracking, key frame reference tracking, and SuperGlue and SuperGlue-based 2D-3D tracking and repositioning; and 6) constructing a 3D sparse map of the environment. According to the method, an ORB-SLAM 2 system in a monocular camera mode is improved, and a feature point tracking algorithm is optimized.

The invention discloses a civil engineering foundation pit deformation monitoring system based on image analysis, and relates to the technical field of foundation pit deformation monitoring, and the system collects and enhances a multi-modal image, eliminates interference, corrects image distortion, tracks key points, constructs a panoramic image, generates a point cloud, extracts deformation features, and constructs a deformation map. The method comprises the following steps: identifying a construction disturbance event, constructing a deformation chain and performing causal reasoning, identifying a high-risk area, predicting a deformation trend and outputting an early warning, displaying a monitoring result, collecting user feedback and synchronizing system data. A deformation track is accurately extracted through image correction and key point tracking, a three-dimensional deformation map is constructed in combination with point cloud modeling, causal analysis is carried out based on construction disturbance and a deformation path, a high-risk area is predicted and identified by using a time sequence trend, an early warning is given out, user feedback collection and result synchronization are supported, and monitoring intelligence and response efficiency are improved.

The invention discloses a photovoltaic power generation system maximum power point tracking control method considering time delay, and belongs to the technical field of photovoltaic cell maximum power point tracking control. The method solves the problems that a traditional maximum power point tracking control method is low in tracking precision and low in tracking speed and cannot deal with complex working environments, battery array parameter change and time delay in a system. An averaging method based on time delay is used for averaging a system to obtain an equivalent system, a constant variation formula method based on an ordinary differential equation is used for carrying out quantitative stability analysis on the equivalent system, a quantitative relation between system stability and controller parameters is established, a final controller form is obtained, and the stability of the system is improved. The MPPT tracking control of the photovoltaic cell is realized in a real sense, the tracking precision and the tracking speed are improved, and the power loss of the photovoltaic cell is effectively reduced. The method can be applied to maximum power point tracking control of the photovoltaic power generation system with time delay.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention provides a video point trajectory constraint-based video description method and system. The method comprises the following steps of: sampling a key frame image and acquiring a space-time trajectory of continuous inter-frame pixel points by using a point tracking algorithm; performing average pooling operation on the visual features of the corresponding frames of the same track fragment; performing semantic alignment on the text features, the visual features and the track features and then performing multi-head attention feature fusion; semantic correlation score calculation is carried out on the visual areas corresponding to the track fragments, correlation scores are arranged in a descending order, the correlation scores are accumulated, and a threshold value is set; jointly optimizing a video point tracking model by using language generation loss and focusing loss; and decoding the multi-source features after focusing optimization to obtain a final video description result. The method introduces a video point trajectory aggregation strategy, explicitly models the dynamic characteristics of a target in a space-time dimension, retains the spatial appearance and time coherence of an object, and effectively solves the problems of semantic fracture and description fragmentation in a complex scene.

The invention discloses a method and a system for automatically calculating the expansion speed of an asphalt mixture test crack and estimating the repair volume. The method comprises the following steps of: preprocessing a test crack image by graying, smoothing and histogram equalization; a Canny edge detection algorithm, a BFMatcher feature point matching algorithm and a Lucas-Kanade feature point tracking algorithm are utilized to realize experimental crack tracking; the crack propagation speed is automatically calculated through time sequence analysis; acquiring three-dimensional point cloud data of the test crack and performing three-dimensional reconstruction; automatically calculating a crack orientation minimum bounding box; and estimating the repair volume of the crack. The method can effectively solve the problems of low detection efficiency and large subjectivity of the detection result caused by human eye observation or image recording in the crack propagation exploration process, improves the problem of inaccurate repair volume estimation after the test crack is generated, and improves the automation and refinement level of the test crack test.

The present invention belongs to the technical field of eye movement tracking, and discloses a near-eye fixation point tracking method, device and medium based on an event sensor, including: an infrared light source provides infrared illumination for the eye and the surrounding area of the eye, and the event sensor collects information on the eye and the surrounding area of the eye to obtain a series of event stream information generated by the change in light caused by movement; the time window clustering event set T is sent to the ROI and pupil calculation module to output pupil ellipse information; the event stream information is sent to the vector calculation module, and in combination with the pupil ellipse information, the movement vector of the pupil center is calculated; in combination with the movement vector, the pupil ellipse information is updated; in combination with the dynamic filtering method, the pupil ellipse information is smoothed; the filtered pupil ellipse information is sent to the line-of-sight regression and compensation model, and in combination with the deep learning method, the fixation point coordinates are calculated. The present invention introduces an event sensor and can more accurately identify moving targets in a dynamic scene.

The invention discloses a light-storage-hydrogen direct current micro-grid coordination control method based on maximum efficiency point tracking, and belongs to the field of photovoltaic hydrogen production system optimization control. The method comprises the following steps: 1, establishing a photovoltaic system power generation model, a storage battery voltage stabilization model and an AWE actual operation model; 2, based on the AWE efficiency characteristic curve, adopting a particle swarm optimization algorithm to track voltage and current values corresponding to the maximum efficiency point of the AWE at different temperatures; 3, designing a photovoltaic system control strategy, a storage battery control strategy and an AWE control strategy based on sliding mode variable structure control; and 4, designing a plurality of coordination control strategy scenes in combination with voltage and current values corresponding to the maximum efficiency point of the AWE at different temperatures, so that the light-storage-hydrogen direct current micro-grid operates under various working conditions. The high electric energy utilization rate of direct coupling and the hydrogen production continuity of indirect coupling are combined, the energy conversion efficiency of the system is improved, and the current ripple is improved.

The invention provides an unmanned aerial vehicle cluster fire point real-time detection and positioning method and system. The method comprises the following steps: acquiring a visible image and an infrared image synchronously shot by a single unmanned aerial vehicle, and recorded shooting time and unmanned aerial vehicle pose data; processing the visible image and the infrared image based on threshold segmentation and morphological filtering to obtain a moment image pair; inputting the moment image pair into the trained double-attention segmentation network, and outputting a fire semantic map; analyzing and processing the fire semantic map based on the connected domain, outputting candidate fire points, and obtaining initial geographic coordinates of the candidate fire points based on the pose data of the unmanned aerial vehicle; based on the shooting time, tracking the initial geographic coordinates of the candidate fire points in real time through Kalman filtering, and outputting a fire point tracking and positioning result of the single unmanned aerial vehicle at the to-be-positioned moment through dynamic compensation of elevation data; and fusing the fire point tracking and positioning results of all the unmanned aerial vehicles in the unmanned aerial vehicle cluster at the to-be-positioned moment to obtain a fire point fusion positioning result at the to-be-positioned moment.

The invention provides an outdoor cross-modal robust positioning and navigation method for extreme severe weather, and relates to the field of online positioning and navigation of mobile robots. The method comprises the following steps: acquiring and preprocessing data; generating a path point set based on a CMR network, perfecting the path point set through local trajectory fitting and error optimization, and constructing a path point relative pose measurement-topology hybrid map; motion prior estimation is carried out based on Doppler velocity, effective matching prior estimation is generated in combination with LiDAR odometer information, cross-modal pose estimation is carried out through a CMR network, and a pure tracking strategy and PD controller path point tracking are adopted; and geometric and intensity alignment is carried out on the forward 4D millimeter wave radar point cloud and the omnidirectional 3D laser radar point cloud through a CMR network, a rotation matrix and a translation vector are output, and accurate positioning is completed. According to the invention, the sensing and positioning defects in extreme weather in the prior art are overcome, and the positioning precision and robustness are improved.

The invention relates to the technical field of 3D visual inspection, in particular to a dynamic shielding area multi-view time sequence point cloud completion method and system in 3D visual inspection. The method comprises the following steps: collecting a multi-view time sequence image of a dynamic detection object for providing a data basis for feature point tracking; and carrying out Lucas-Kanade optical flow method tracking on the feature points, associating the feature points of different visual angles, describing a motion track of the feature points through an optical flow vector, carrying out cross-visual-angle and cross-time feature point matching, and establishing time sequence feature association to obtain time sequence feature points. According to the dynamic occlusion area multi-view time sequence point cloud completion method in 3D visual detection, 360-degree time sequence data of a dynamic detection object is acquired through multi-view time sequence image acquisition, and comprehensive basic data support is provided for subsequent feature tracking.

The invention discloses a non-contact intelligent health monitoring method and equipment based on a physiological signal, and relates to the technical field of non-contact health monitoring, and the non-contact intelligent health monitoring method based on the physiological signal mainly comprises the steps: carrying out the division according to a face video image, and obtaining a region of interest; obtaining an aligned region of interest by utilizing a facial feature point tracking and alignment technology, and fusing and inputting an improved rPPG technology into the region to obtain an actual heart rate value; according to the original audio signal, performing framing and discrete Fourier transform by using a Hamming window to obtain a spectrogram of each audio clip; mel feature extraction is carried out by using a librosa library to obtain MFCC features, and the three features are fused to obtain a predicted emotion type of the voice. By means of the non-contact intelligent health monitoring method and device based on the physiological signals, the robustness and precision of heart rate monitoring can be improved, the accuracy and robustness of emotion recognition are improved, and combined monitoring of the emotion and the heart rate is achieved.

The invention discloses a monocular vision inertial fusion positioning method based on an optical flow smoothness constraint, which comprises the following steps: designing a visual feature enhancement algorithm based on the optical flow smoothness constraint, improving feature point tracking and elimination in a visual inertial navigation system, and carrying out tight coupling optimization on a visual residual error part so as to improve the quality of observed quantity in positioning; comprising the following steps: generating a clustering result and a support factor for each feature point tracking vector of an acquired image through optical flow smoothness constraint; constructing a compound expectation maximization (EM) algorithm, fusing a support factor into algorithm solution to serve as prior information, and eliminating wrong matching point pairs to obtain a correct matching vector; adaptively adjusting the visual weight of the pose estimator, and performing region shielding; and monocular vision inertial fusion positioning based on optical flow smoothness constraint is realized.

The invention discloses a method for generating a high-precision dynamic digital human, which belongs to the field of computer vision and comprises the following steps of: extracting skeleton motion and frame-level potential codes from a multi-view video, driving a coarse model network to generate a three-dimensional grid, and realizing attitude alignment by combining double quaternion skin and embedded deformation; a vertex three-dimensional corresponding relation is obtained through 2D point tracking and a depth map, and grid alignment precision is optimized; on the basis, animatable Gaussian textures are trained, and texture detail modeling is achieved; a physical solver is further introduced, the physical response of cloth is solved according to the bone posture and the material type, and die penetration and distortion are eliminated; meanwhile, modeling is carried out on texture offset by adopting a space-time Transform, so that the dynamic consistency is improved; and finally, high-fidelity image rendering is completed through analytical sputtering, and a dynamic digital human which is rich in details, stable and controllable is generated.

The invention relates to the technical field of AI evaluation, in particular to a post competency AI evaluation system and a post competency AI evaluation method, the post competency AI evaluation system comprises an intelligent scanning layer, a diagnosis decision-making layer, a talent development prediction layer, an education optimization layer and a data closed-loop feedback module, and the intelligent scanning layer comprises a multi-modal biological characteristic acquisition module and a dynamic ability modeling module; the talent development prediction layer comprises a capability-post matching engine and a demission risk early warning unit; the education optimization layer comprises a course reform simulator and a virtual teaching and research room; compared with the defects that in the prior art, single questionnaire or static video analysis is relied on, the evaluation dimension is one-sided, and subjective interference is likely to happen, the scheme synchronously integrates three types of biological characteristics of facial micro-expression (52 key point tracking), writing force waveform (1000 Hz pressure sense) and VR situation response (1200 + scene library), and the evaluation accuracy is improved. Holographic dynamic capture of a candidate cognitive mode, anti-pressure ability and actual combat response is realized, and objectivity and ecological validity of competency evaluation are remarkably improved.

The invention discloses a cardiac ultrasound key point tracking implementation method based on a pseudo tag, and relates to an ultrasound image analysis technology. The method comprises the steps of extracting a key frame from an ultrasonic image set corresponding to a cardiac pulse period, extracting a key point on the key frame as a pseudo tag, and performing key point detection on each intermediate frame based on a preset tag propagation model according to the pseudo tag and in combination with a time sequence relationship between ultrasonic images in the ultrasonic image set to obtain a key point detection result; key points, corresponding to the key pseudo labels, on each middle frame are extracted to serve as middle pseudo labels; and performing optimization training on the propagation model according to the intermediate pseudo-tag to obtain an optimized tag propagation model. According to the method, the high-quality pseudo label can be generated online, the pseudo label of the middle frame is generated through the propagation model in combination with the high-quality pseudo label, and the potential value of unlabeled data is fully mined; and finally, a propagation model is further optimized and trained by using the generated intermediate frame pseudo tag, and the efficiency and the intelligent degree of cardiac ultrasound key point tracking are improved.