32 results about "Point sequence" patented technology

The invention relates to a fitness action error correction method and device for bone key point detection, equipment and a medium. The method comprises the following steps: acquiring video data in a multi-person fitness scene, and performing skeleton key point detection on each frame of image in the video data to obtain a multi-person skeleton key point sequence; performing spatial clustering processing on the multi-person skeleton key point sequence to obtain a plurality of individual skeleton key point sequences; according to the skeleton key point sequence of each individual, generating an action track of each individual; based on a preset standard action sequence, identifying an action matching result corresponding to each action track; according to the action matching result, the action deviation value of each individual is calculated, and action error correction feedback information is generated based on the action deviation values; the error correction feedback information is used for indicating each individual to adjust the fitness action. The method can adapt to complex scenes, solves the problem that key points of scenes of multiple persons are confused, and provides effective support for fitness guidance and the like.

The invention relates to a human body posture estimation key point correction method based on skeleton proportion constraint, and belongs to the technical field of human body posture estimation key point detection. The method comprises the following steps: extracting key points of a human body in a human body motion video, forming a skeleton based on the key points, calculating a skeleton proportion value of each skeleton length and a thoracolumbar spine length in the skeleton, and defining a frame corresponding to the skeleton proportion value not within a preset threshold as an invalid frame; iterating the positions of the key points in the invalid frame until the bone proportion values formed by all key point sequences in the invalid frame are within a preset threshold value or reach a preset maximum number of iterations; and detecting the widths of the four limbs based on the iterated key points, adjusting the positions of the key points to the axis positions corresponding to the width centers of the four limbs to obtain the adjusted key points, and reconstructing a key point sequence to realize key point correction. The objective of the invention is to solve the technical problem of low key point detection accuracy caused by deviation in key point positioning in the prior art.

The invention provides a method and system for pre-training a sign language understanding framework based on semantic enhancement of skeletons, and relates to the technical field of sign language recognizing.The method comprises the steps that sign language video data and skeleton sequences and text data matched with the sign language video data are obtained; extracting a skeleton key point sequence from the sign language video data, modeling to form skeleton features, and performing word segmentation processing on a text; in a pre-training stage, skeleton features and word segmentation texts are input into a fusion network to generate bidirectional enhanced features, and global and local similarities are obtained through double-layer semantic alignment; calculating the comparison loss based on the similarity, and coordinating the weight through the balance parameter to obtain the hierarchical loss; executing the matching task and the language modeling task to obtain corresponding loss, weighting and combining the three types of loss into pre-training total loss, and adjusting parameters to complete training; and finally, part of parameters are optimized in combination with specific task types in a fine tuning stage, and enhanced understanding of sign language semantics is realized. The sign language understanding accuracy is improved.

The invention relates to the technical field of computers, and discloses an anthropomorphic sliding track generation method and system, and the method comprises the steps: obtaining video data containing human sliding operation, and carrying out the preprocessing, thereby obtaining a static image and scalar time; inputting the static image into an image encoder, inputting scalar time into a time encoder, respectively extracting space task and time constraint features, and fusing the space task and time constraint features into fusion condition features; the method comprises the following steps of: inputting a track point into a layered generator, outputting a current track point coordinate by a track generation head at each time step of the generator, and generating a track point sequence and a video frame sequence by a video generation head in combination with the coordinate, a fusion feature and a frame generated in a previous time step; and inputting the track and the video frame into a discriminator, and returning an authenticity result to adjust parameters of the generator after combined judgment by the discriminator until the output reaches the standard, thereby obtaining the anthropomorphic sliding track. According to the method, anthropomorphic similarity and sample diversity of track generation are improved, and a richer data set close to real human behaviors can be provided for downstream applications.

The invention discloses a computational efficient point cloud analysis method based on a grouping selective state space, and the method comprises the steps: firstly, a sequence extension module carries out the serialization of points along each axis, and enables a disordered point cloud to more stably adapt to the causal characteristics of Mamba without parameters; secondly, sorting prompt and position embedding are adopted to provide sorting and position information for the point sequence respectively, so that geometric semantics are better captured; thirdly, the chain type bidirectional Mama enables the forward process and the reverse process in the parallel bidirectional Mama to be connected in series, a global receptive field on a point sequence is provided, and meanwhile high-order geometric information is captured in the scanning process. And fourthly, the grouping selective state space model introduces parameter sharing among multiple dimensions into the selective state space model, so that overfitting caused by a calculation mode in the selective state space model is relieved. And 5, a sequence merging module fuses corresponding high-order interaction features obtained through causal reasoning on different sequences. And finally, packaging the flow into a basic block, and embedding the basic block into a standard codec architecture for hierarchical feature aggregation. According to the method, the accuracy of point cloud analysis based on the state space model is improved while the calculation efficiency is ensured.

The embodiment of the application provides a personnel fall judgment method, device, storage medium and system, the method comprises the following steps: acquiring the latest personnel image of the target personnel, acquiring the key point information corresponding to each key point of the target personnel in each personnel image; judging whether the target personnel falls based on the acquired key point information, and generating first judgment information; inputting all the acquired key point information into a personnel key point sequence classifier, judging whether the target personnel falls through the personnel key point sequence classifier, and generating second judgment information; acquiring the personnel image information of each personnel image, inputting all the acquired personnel image information into a personnel image sequence classifier, judging whether the target personnel falls through the personnel image sequence classifier, and generating third judgment information; judging whether the target personnel really falls based on the first judgment information, the second judgment information and the third judgment information, so that the accuracy of judging whether the personnel falls can be improved.

Method for providing a sequence of measurement points, in particular for carrying out a test procedure for measuring a physical unit, wherein each measurement point is defined by a combination of one input value from several input variables according to the sequence of input values, wherein the sequence of input values ​​of each input variable corresponds to a step function with steps having a step height (H), a jump height (S) between the steps, and a step length (L); comprising the following steps: - Providing (S3, S4, S5; S11, S21) a step height value sequence for each of the input variables, wherein the step height value sequence corresponds to a uniformly distributed value sequence and specifies a sequence of step heights (H) along the course of the step function of the input variable in question; - Providing (S2, S12, S22) a step length value sequence for each of the input variables, wherein the step length value sequence corresponds to a uniformly distributed value sequence and specifies a sequence of step lengths (L) along the course of the step function; - Generating (S3, S4, S5; S13, S14, S15, S16; S23, S24, S25, S26) sequences of input values ​​for each of the inputs depending on the sequence of step heights (H) and depending on the sequence of step lengths (L), wherein generating sequences of input values ​​for each of the inputs comprises generating the input values ​​of each of the inputs in a sequence resulting from concatenating the step heights according to their order in the step height value sequence assigned to the input in question, wherein each of the step heights is provided successively with a number resulting from the sequence of step lengths assigned to the input in question in the step length value sequence; - Generating (S6, S18, S28) the sequence of measurement points by combining the corresponding input values ​​of the sequences of input values ​​of the multiple input variables.

The present disclosure relates to a motion scoring method, device, equipment and storage medium, and relates to the technical field of fitness mirrors. The method comprises the following steps: first, acquiring a human body key point sequence corresponding to a to-be-scored motion on a target image, a human body key point template and a human body joint angle template; determining a key point Pearson matching degree, a joint angle Pearson matching degree and a joint angle difference value similarity; and determining a motion score of the to-be-scored motion on the target image according to the key point Pearson matching degree, the joint angle Pearson matching degree and the joint angle difference value similarity. It can be seen that, by means of the key point Pearson matching degree, the joint angle Pearson matching degree and the joint angle difference value similarity, the motion score of the to-be-scored motion on the target image is determined, and the accuracy of the motion score is improved.

This application discloses a method, system, device, and medium for handling violation scenarios in large-scale model logs, mainly relating to the field of violation handling technology. It aims to address the problems of rigid rules, limited coverage, knowledge silos, lack of correlation, reliance on experts, and high iteration costs in existing solutions. The method includes: tracking behavioral nodes corresponding to several consecutive raw logs to form a behavioral node sequence; comparing the behavioral node sequence with preset legal path sequences in a behavior tree model library to determine if a preset abnormal node sequence exists; when an abnormal node sequence exists, determining whether it conforms to an event sequence corresponding to any rule in the event analysis rule library; if it does, entering the preset alarm scheme corresponding to the event sequence; if it does not, entering the unknown scenario mining process, inputting the abnormal node sequence into a trained second large-scale language model to obtain an initial processing scheme, and inputting the initial processing scheme into a preset verification terminal to obtain the final processing scheme.

Embodiments of the present application disclose a data processing method and device, computer equipment and a storage medium, wherein the method comprises the following steps: obtaining a question answered by a target user and a target question not answered; generating a question sequence, a knowledge point sequence and an answer record sequence of the target user according to the answered question and the target question; performing feature extraction on the question sequence, the knowledge point sequence and the answer record sequence to generate a question vector corresponding to the question sequence, a knowledge point vector corresponding to the knowledge point sequence and an answer record vector corresponding to the answer record sequence; performing graph feature extraction on the question vector and the knowledge point vector to generate a corresponding graph vector; and generating a correct probability of the target user answering the target question based on the graph vector, the question vector, the knowledge point vector and the answer record vector through a question answering prediction model. The present application can accurately grasp the degree of user's mastery of knowledge points and improve the accuracy of predicting user's answers.

The application discloses a kind of two-dimensional space to three-dimensional space key point mapping conversion method and system, including sequence preprocessing module: the length of two-dimensional space key point sequence is obtained, carries out sparse preprocessing, obtains sparse two-dimensional space key point sequence;Mapping dimension-increasing module: sequence modeling is carried out to sparse two-dimensional space key point sequence, obtains three-dimensional space key point sequence after preliminary mapping;Sequence aggregation module: three-dimensional space key point of center target is obtained by compressing and aggregating three-dimensional space key point sequence after preliminary mapping;Training network module: end-to-end training is carried out to network using supervised learning, and mapping accuracy of three-dimensional space key point estimation network is improved using space-time constraint strategy;Visualization module is used to obtain visual result.The application has good robustness and universality, and can be widely applied in key point detection of a variety of objects.

The invention discloses a human body action video generation method based on angle conditions. Comprising the following steps: acquiring a reference figure image or video frame and a reference posture sequence of a target action, and performing human body key point detection; generating a key point latent variable sequence and a continuous angle feature sequence; constructing a conditional sequence, generating a latent variable-conditional sequence according to the key point latent variable sequence and the conditional sequence, and further obtaining a training data set; then training the key point generation model to obtain a trained key point generation model; and selecting a reference person video frame and a target action and generating a corresponding latent variable-conditional sequence, thereby obtaining a target key point sequence and inputting the target key point sequence into a video generation model, and generating a target human body action video. According to the invention, while the consistency of figure identities is maintained, the cross-body-type high-precision and editable human body video generation is realized, the action connection is natural, the robustness is high, and the method is suitable for scenes such as virtual human production, movie and television animation, human-computer interaction and the like.

This application provides a computer vision-based gesture recognition system, method, device, and medium. For each occluded keypoint, based on a pre-defined spatiotemporal graph model of the target object's hand keypoints, a graph convolutional network is used to extract spatial features of keypoints with high detection confidence in the spatial neighborhood of the occluded keypoint and temporal series features with high detection confidence in historical frames before occlusion. This yields a spatiotemporal collaborative attention map for guiding information completion. Based on the spatiotemporal collaborative attention map, feature propagation and aggregation are performed from the keypoints with high detection confidence to the occluded keypoints to obtain the completed feature representation of the occluded keypoints. The completed coordinates of the occluded keypoints are then output, generating an occlusion-resistant keypoint sequence for the target object's hand region. Based on this occlusion-resistant keypoint sequence, the gesture category of the target object is identified. Using the scheme of this application, robust gesture recognition against occlusion can be achieved.

It is determined whether each of the first motion point sequence and the second motion point sequence contains a first motion point of a first ordinal number and a second motion point of a second ordinal number, and the first motion point sequence and the second motion point sequence contain the first and second multiple motion points of one or more body parts of a person, ordered according to the respective time points at which the first and second multiple motion points were detected from the first and second series of video frames corresponding to the detection region. A method for identifying the start and end of a motion point sequence for a person to perform the task, based on the first motion point and the second motion point, from the first motion point sequence and / or the second motion point sequence.

The invention relates to the field of computer vision, and provides a dynamic gesture detection method and system and electronic equipment, and the method comprises the steps: obtaining to-be-detected target video sequence information; preprocessing the target video sequence information to obtain an appearance frame sequence and a key point sequence; performing data processing on the appearance frame sequence based on a space-time Transform encoder to obtain an appearance feature vector, and performing data processing on the key point sequence based on the space-time Transform encoder to obtain a key point feature vector; performing cross-modal attention fusion on the appearance feature vector and the key point feature vector to generate a fusion feature vector; and performing dynamic gesture detection based on the fused feature vector. The gesture detection method is used for overcoming the defects that in the related technology, when dynamic gesture detection is carried out, time sequence modeling capacity is poor, and performance in a complex scene is poor. The gesture detection method is higher in accuracy.

The invention provides a video compression method and system based on a LivePortrait-GAN video technology, and the method comprises the steps: extracting a coordinate set of key points of a human body of a teacher, carrying out the matching of the coordinate set with a preset teaching action semantic template library, carrying out the coding of a template number and a space offset if the matching succeeds, and otherwise, reserving a residual key point sequence; adjusting the residual key point sequence in combination with the synchronous voice rhythm characteristics, the structure mask and the space offset; fusing the semantic anchoring vector, the residual vector and a gating parameter generated based on the ratio of the residual amplitude to the template offset amplitude, and generating a driving vector with a smooth time sequence; and driving the clipped and fixed-point LivePortrait-GAN generator to reconstruct a portrait picture by using the identity characteristics of the static reference image of the teacher and the driving vector, and outputting a final video frame through frame-level fusion. The key model can be efficiently deployed on a domestic artificial intelligence acceleration chip (such as AX650N), end-to-end real-time coding and rendering are achieved, and the domestic controllable deployment requirement is met.

The embodiment of the present disclosure discloses a passing point prediction method and a passing point prediction model training method and device, the method comprising: obtaining a historical passing point sequence of a navigated object, the historical passing point sequence comprising data records indexed by historical passing points, each data record comprising a historical passing point and a corresponding historical scene feature sequence; obtaining a scene feature of a target navigation route of the navigated object as a current scene feature; inputting the current scene feature and the historical passing point sequence into a model, the model comprising a recommendation representation module, and obtaining a recommended representation of a historical passing point corresponding to a historical scene feature based on at least the current scene feature and the historical scene feature in the historical passing point sequence; the model comprising a first scoring module, and obtaining a score of each historical passing point based on the recommended representation of the historical passing point; and determining a passing point prediction result based on at least the score of each historical passing point. The embodiment of the present disclosure can improve the prediction accuracy.

The invention discloses a large-model log violation scene processing method, system and device and a medium, mainly relates to the technical field of violation processing, and is used for solving the problems of rule stiffness, limited coverage, knowledge islanding, lack of association, dependency on experts and high iteration cost in the existing scheme. Comprising the steps of tracking behavior nodes corresponding to a plurality of continuous original logs to form a behavior node sequence; comparing the behavior node sequence with a preset legal path sequence in a behavior tree model library, and determining whether a preset abnormal node sequence exists or not; when the abnormal node sequence exists, determining whether the abnormal node sequence accords with an event sequence corresponding to any rule in an event analysis rule base, and when the abnormal node sequence accords with the event sequence corresponding to any rule in the event analysis rule base, entering a preset alarm scheme corresponding to the event sequence; and if not, entering an unknown scene mining process, inputting the abnormal node sequence into a trained second large language model to obtain an initial processing scheme, and inputting the initial processing scheme into a preset verification terminal to obtain a final processing scheme.

This invention discloses a method and apparatus for real-time writing direction determination based on single-character stroke features. The method includes: acquiring a sequence of coordinate points of a stylus on a whiteboard in real time; calculating a feature set representing the writing features of a single character based on the coordinate point sequence; calculating a horizontal comprehensive score by weighting the feature set; calculating a vertical comprehensive score by weighting the reciprocals of each feature in the feature set; and determining the writing direction of the stylus based on the horizontal and vertical comprehensive scores to obtain a determination result. This invention proposes a method and apparatus for real-time writing direction determination based on single-character stroke features. It calculates a feature set and calculates a weighted score based on the single-character stroke features in real time to determine the writing direction, without relying on multi-character layouts, thus avoiding response delays at the source and enabling independent and effective recognition of single characters. Therefore, it can solve the problems of response delays and single-character recognition failures caused by existing technologies that rely on multi-character layouts to determine writing direction.

The disclosure provides an image and text based video generation method and device, and relates to the technical field of computer vision. The method comprises the following steps: obtaining fusion encoding, wherein the fusion encoding comprises image encoding of a reference image and text encoding of action text, and the reference image comprises a target object; inputting the fusion encoding into a decoder to obtain a target key point sequence; and obtaining a reconstructed video according to the reference image and the target key point sequence, wherein the video content of the reconstructed video is that the target object performs an action described in the action text; wherein the decoder is obtained by training based on a first key point sequence of an original video sample and a second key point sequence corresponding to a reconstructed video sample, and original video encoding samples and fusion encoding samples, and the fusion encoding samples comprise text encoding of an action text sample and image encoding of a reference image sample.

The application provides a kind of animation style virtual person animation real-time generation method and system, comprising: extracting multidimensional style features from animation reference map, wherein multidimensional style features include line drawing features, color features, texture features and character form features;From real-time captured portrait video, extract portrait key point sequence and interactive object sequence, and generate dynamic semantic identity anchor point stream according to interactive object sequence;With portrait key point sequence as structural constraint condition, with multidimensional style features as global guidance, dynamic semantic identity anchor point stream is embedded into latent space as hidden variable constraint, and controlled diffusion reasoning is carried out in latent space, to obtain animation latent feature frame sequence;Three-dimensional causal decoding and time domain smoothing filtering are carried out on animation feature frame sequence, to obtain smooth animation frame sequence;Real-time style feedback and style correction are carried out on smooth animation frame sequence, to obtain virtual person animation.

The invention discloses an interest point recommendation method based on completion enhancement and multi-angle deentanglement fusion learning, which comprises the following steps of: firstly, complementing an original sign-in sequence of a user by adopting breadth-first search and geographical distance constraint based on a global transfer frequency diagram; secondly, utilizing the complemented sign-in data to respectively construct a user-interest point collaborative interaction hypergraph, an interest point-interest point sequence transfer hypergraph and a spatial proximity graph; then, designing a three-stage hypergraph convolution and graph convolution network, and respectively learning point-of-interest representation under three types of views; then, generating multi-angle user representations based on the user-interest point interaction matrix, aligning the multi-angle representations of the user and the interest point by adopting comparative learning, and fusing the aligned multi-angle representations of the user and the interest point to generate global user and interest point representations; then, for each user, only selecting a subset corresponding to the historical access interest point of the user in the global interest point representation, organizing into a sign-in sequence according to a time sequence, inputting the sign-in sequence into a sequence modeling module, generating a local user representation, fusing the global user representation and the local user representation by using a learnable weight, and generating a final user representation; and finally, generating a recommendation probability of the next interest point through dot product operation of the final user representation and the candidate interest point representations. According to the method, deviation of original sign-in data of the user can be avoided, multi-aspect transfer motivation of the user is effectively modeled, global information and local information are fused at the same time, and the accuracy and interpretability of interest point recommendation are improved.

The application provides a method and system for enhancing a pre-training sign language understanding framework based on bone semantics, and relates to the technical field of sign language recognition, wherein the method comprises: obtaining sign language video data, paired bone sequences and text data thereof; extracting bone key point sequences from the sign language video data and modeling to form bone features, and performing word segmentation on the text; in a pre-training stage, inputting the bone features and the segmented text into a fusion network to generate bidirectional enhanced features, and obtaining global and local similarities through double-level semantic alignment; calculating a contrast loss based on the similarities, and obtaining a hierarchical loss by balancing parameters to coordinate weights; performing a matching task and a language modeling task to obtain corresponding losses, weighting and combining the three types of losses into a pre-training total loss, and adjusting parameters to complete training; and finally, in a fine-tuning stage, optimizing part of the parameters in combination with specific task types to realize enhanced understanding of sign language semantics. The application improves the accuracy of sign language understanding.

This invention proposes a video recording compression method and system based on LivePortrait-GAN video technology. The method includes: extracting a set of key point coordinates of the teacher's body and matching them with a preset teaching action semantic template library. If a match is successful, it is encoded as a template number and spatial offset; otherwise, it is retained as a residual key point sequence. The residual key point sequence is adjusted by combining synchronous speech rhythm features, structural masks, and spatial offsets. A temporally smooth driving vector is generated by fusing semantic anchor vectors, residual vectors, and gating parameters generated based on the ratio of residual amplitude to template offset amplitude. Using the identity features of the teacher's static reference image and the driving vector, the cropped and fixed-point LivePortrait-GAN generator is used to reconstruct the portrait image, and the final video frame is output through frame-level fusion. The key model in this invention can be efficiently deployed on domestically produced artificial intelligence acceleration chips (such as AX650N) to achieve end-to-end real-time encoding and rendering, meeting the requirements of domestically produced and controllable deployment.

The application discloses a transition video generation method and device, equipment and a storage medium, and relates to the technical field of video processing. The method comprises the following steps: acquiring multi-modal input data; constructing an emotion anchor point sequence according to voice data in the multi-modal input data; extracting emotion information and context information in the emotion anchor point sequence; generating a transition control parameter according to the emotion information and the context information; and generating a target transition video according to the transition control parameter. The application realizes accurate matching of a transition effect and emotion changes of previous and subsequent clips by extracting transition camera parameters and visual style constraints matched with emotion changes from multi-modal data, solves the technical problem that a video transition template is fixed and is inconsistent with a picture style, and improves emotion coherence and visual consistency of generated transition videos.

This invention provides an automatic correction and cropping method for core images based on YOLOv8-seg and perspective transformation, belonging to the field of image processing technology. The method includes: S1: collecting core box images; S2: polygon point sequence annotation; S3: obtaining the optimal segmentation model; S4: cropping. This invention addresses the problems of excessive manual intervention, low efficiency, and poor adaptability in existing core image processing methods by providing a fully automated end-to-end solution that requires no preprocessing. It enables rapid and stable correction and cropping of core images in complex scenarios, reducing the mechanical workload of geologists and allowing them to focus on core exploration work, thus meeting the practical application needs of large-scale geological exploration projects.

This invention discloses a method for simplifying the number of keyframes for discrete shaping effects in nonlinear editing, belonging to the field of nonlinear editing technology. The method includes: acquiring an original discrete two-dimensional point sequence; setting an aliasing deviation threshold; identifying a first set of ideal polyline endpoints through forward search, wherein the search is achieved by continuously extending candidate line segments and determining whether the vertical distance from the midpoint to the line segment exceeds the threshold; obtaining a second set of endpoints through reverse search and matching and averaging it with the first set to correct endpoint offset; employing multi-threaded parallel processing of large-scale sequences; and for multi-parameter cases, taking the union of the time positions of the endpoints of each parameter to generate unified keyframe data. This invention solves the storage and performance bottleneck problem caused by recording massive keyframes for shaping effects frame by frame.

The track generation method comprises the following steps: acquiring a track point sequence of a target object in a first time period; on the basis of the track point sequence, a track description text is generated, and the track description text is activity description of the target object in the first time period; and displaying the track description text. Therefore, the track description text capable of describing the activity condition of the object in a period of time is generated from the track point sequence, so that a user can intuitively know the activity condition of the related object in a period of time.

The invention discloses a counter behavior detection method and device, equipment and a medium. The method comprises the following steps: after a to-be-detected video and a to-be-detected audio are obtained, determining an action key point sequence, a dialogue text, movement information and decibel information of a target counter; determining the probability that various preset actions occur in the target counter; determining the probability of occurrence of each type of preset interaction state in the target counter; detecting whether the occurrence probability of each type of safety abnormal behavior in the target counter is greater than the safety threshold value of each type of safety abnormal behavior; and when it is detected that the probability that the target safety abnormal behavior occurs at the target counter is greater than a safety threshold, determining that the target safety abnormal behavior occurs at the target counter, and providing alarm information for a monitoring user. According to the embodiment of the invention, whether the safety abnormal behavior occurs in the counter or not can be automatically, comprehensively and accurately detected based on the multi-dimensional information related to the business personnel and the customers at the counter.

The invention discloses a production line personnel behavior identification method and system, and the method comprises the steps: obtaining the original video stream data of a production line, inputting the original video stream data into a pre-trained improved key point detection model, and obtaining the key point coordinate data of a human skeleton in each frame of image; organizing the key point coordinate data of continuous n frames into a time sequence key point sequence; and inputting the time sequence key point sequence into a pre-trained behavior recognition model to output a behavior category corresponding to the time sequence key point sequence. According to the production line personnel behavior identification method and system, the problem that the precision and the real-time performance are difficult to consider under the shielding and complex background in the prior art is effectively solved, and reliable technical support is provided for intelligent safety monitoring and quality tracing of a production line.