254 results about "Human skeleton" patented technology

The invention provides a human body skeleton point positioning identification method and system based on OpenPose, and the method comprises the steps: carrying out the preprocessing of an input human body image, and obtaining the preprocessing image data; performing skeleton point detection on the preprocessed image data by adopting a double-domain multi-path self-supervised diffusion model in combination with a convolutional neural network to obtain two-dimensional human skeleton point coordinate data; performing signal-to-noise ratio evaluation and noise reduction processing on the two-dimensional skeleton point coordinate data to obtain two-dimensional skeleton point data after noise reduction; performing two-dimensional to three-dimensional conversion through a triangulation principle and a feature matching technology to obtain preliminary three-dimensional skeleton point coordinate data; performing skeleton point optimization through rate perception analysis and a three-dimensional Gaussian compression algorithm; and carrying out shielding prediction and completion processing on the optimized three-dimensional skeleton points to obtain a complete human skeleton point positioning identification result. According to the invention, the problems of accuracy and stability of skeleton point positioning in a complex dynamic scene are solved.

The invention discloses an exercise rehabilitation assessment method and system based on limb posture and emotion recognition, and the method comprises the steps: collecting a motion video, recording the age and gender information of a patient, constructing a self-made data set, defining a candidate region containing the rehabilitation motion of the patient, constructing a basic motion posture data set, and carrying out the recognition of the rehabilitation motion of the patient based on a posture estimation algorithm. Personalized limb skeleton key points are extracted, and emotion features are obtained through face key point detection and face action unit analysis; analyzing the motion trail of the knee joint based on the personalized limb skeleton key points, and extracting limb posture information; and carrying out feature fusion on the limb posture information and the emotional features to form a quantitative rehabilitation evaluation result. According to the invention, high-precision capture of the key points of the human skeleton is realized through computer vision and pattern recognition technologies. In addition, in combination with analysis of the emotional state of the patient, the evaluation accuracy is enhanced, and the rehabilitation training effect evaluation is more comprehensive and accurate.

The invention discloses a physical fitness evaluation method based on human skeleton trajectory tracking, which comprises the following steps: S1, acquiring whole-process video data of a physical fitness test through video acquisition equipment, and generating a video frame according to an acquisition frequency; s2, processing the video frame by using a skeleton dynamic analysis algorithm, and extracting human skeleton key points; s3, extracting identity features of the testee through a motion map identity recognition network, and tracking and confirming the identity features; s4, performing action recognition, fragment segmentation and compliance judgment on the skeleton key point time sequence track; s5, aiming at the abnormal skeleton key points, performing complementation and time sequence smoothing by adopting an inverse kinematics inference method; s6, inputting the complete skeleton key point time sequence data into the human body action evaluation model for processing, and quantizing and outputting a physical ability evaluation index; and S7, generating a physical ability evaluation report and giving action feedback and optimization suggestions. According to the invention, objectivity, accuracy and intelligent level of physical ability evaluation are effectively improved.

The invention relates to the technical field of millimeter-wave radars, in particular to a millimeter-wave radar action evaluation method, system and equipment based on a human skeleton model, and the method comprises the following steps: S1, millimeter-wave radar point cloud sampling: employing millimeter-wave radar equipment to collect three-dimensional point cloud data of a human body, and extracting target points related to human skeleton nodes; s2, skeleton node estimation: predicting three-dimensional coordinates of each joint of the human body by adjusting an output structure of a PointNet neural network model, so as to reconstruct and fit a skeleton structure of the human body; and S3, human body action evaluation: constructing the obtained three-dimensional coordinate data of the multiple frames of human body skeleton nodes into time sequence data, and analyzing a motion track by using an evaluation system based on an ST-GCN network. According to the invention, complex dynamic changes and behavior modes in human motion can be accurately captured, and high-precision real-time evaluation and feedback are provided for the fields of motion monitoring, rehabilitation training, health evaluation and the like.

The invention relates to a forklift examination anti-cheating method and system based on artificial intelligence and a quantification method. The anti-cheating method for the forklift examination comprises the following steps: S1, acquiring a limb action video V (t) of an examinee during the examination and pressure data P (t) of a forklift driving system; s2, extracting a human body skeleton graph Gt at the t-th moment from the V (t); extracting the Gt to obtain a spatial structure feature set St, and extracting a time sequence dynamic feature set Dt of the St; and fusing the St and Dt features to obtain a pose feature Zt. Visual sense and hydraulic sensing data are fused, association analysis is carried out on examinee actions and vehicle responses, and abnormities which are difficult to find through a single video or sensing means are detected. Through conjoint analysis of images and pressure data, hidden cheating means such as remote control and dangerous violation operation can be identified, and three-dimensional monitoring of the examination process is realized.

The invention relates to the technical field of computer vision, in particular to a human body behavior recognition method and system. According to the method, a multi-head space hypergraph convolution module is arranged before each time graph convolution of an ST-GCN model, and a human body behavior recognition model is constructed; the multi-head space hypergraph convolution module constructs a non-uniform hypergraph to represent the topological relation of the human skeleton through the maximum number, which can be contained by hyperedges, of each node, and generates the output of the module in combination with a physical adjacency matrix reflecting the relation of each joint; virtual connection is also added, virtual features which are the same as the input channel dimension and the time dimension of the multi-head space hypergraph convolution module are constructed, and the virtual features and modal data features are spliced along the joint dimension and then serve as module input again, so that global semantic information of real nodes is enriched, generalization information of human body behavior modes is supplemented, and the real-time performance of the multi-head space hypergraph convolution module is improved. And stage dense connection is introduced between different layers to smooth the change degree of the features, so that the human behavior recognition precision is improved.

The invention relates to the technical field of knee joint rehabilitation evaluation, in particular to a knee joint rehabilitation evaluation method based on a human skeleton by using a space-time diagram convolutional network, and the method comprises the following steps: obtaining a rehabilitation evaluation gait video set; extracting human body key point skeleton data, and slicing the human body key point skeleton data into gait samples according to the number of time frames; preprocessing the gait sample, and extracting joint, skeleton and joint angle features; a gait-link method is adopted to divide a space gait skeleton diagram, a space-time attention mechanism is added, and an improved CTR-GCN network model is constructed; training the improved CTR-GCN network model by using the gait sample to obtain a gait evaluation model; and processing a gait video to be evaluated, and inputting the processed gait video into the gait evaluation model to obtain a rehabilitation evaluation result of the patient. According to the method, the human body key point skeleton in the walking video of the patient is extracted and input into the space-time diagram convolutional network for training, the rehabilitation evaluation model is obtained, the rehabilitation condition of the patient is evaluated by using the model, and the requirement of rehabilitation evaluation is met.

The invention belongs to the technical field of human body tumble detection, and particularly relates to a human body tumble detection method and device based on multi-feature fusion. According to the method, firstly, human skeleton key point features are extracted through an RGB camera in combination with an OpenPose model, then human contour features are extracted through an infrared camera in combination with an OpenCV library, then human three-dimensional point cloud features are obtained through a millimeter wave radar in combination with a data preprocessing method, and finally, the three features are fused and input into a GNN neural network model, so that the human body three-dimensional point cloud features are obtained. And obtaining a human body falling detection result. Through multi-mode feature complementation, the human body tumble detection system can adapt to different illumination, shielding and environmental interference, and is efficient and reliable.

The invention discloses a swimming pool drowning detection method based on human skeleton key points, and the method comprises the steps: firstly carrying out the real-time analysis of a video stream through employing an advanced human body key point detection model, and positioning the human body key points; and then a plurality of swimmers are distinguished by means of a multi-target tracking model, continuous tracking is carried out, and the dynamic change of each swimmer in the time sequence is captured, so that the motion trail of the human body is obtained. And finally, carrying out deep analysis on the extracted human body key point features through an action recognition model to judge whether drowning signs exist or not. According to the drowning detection scheme, the drowning situation can be monitored in real time and accurately recognized, the detection efficiency and accuracy and the generalization performance of the model are remarkably improved, and more reliable technical support is provided for swimming pool safety monitoring.

The invention discloses a field personnel behavior identification method based on human skeleton key points. The method is suitable for behavior monitoring of operators in complex production environments such as hydraulic power plants. The method comprises the following steps: performing feature extraction and modeling on skeleton data acquired by camera equipment, and performing multi-scale space-time modeling by adopting a time sequence guide space topology modeling module and a space-time motion modeling network; according to the method, the capturing capability of the dynamic relation between human skeleton nodes is enhanced, the behavior recognition precision of long time span and complex postures is improved, and the robustness and generalization capability of the model in a complex environment are improved; on the whole, the risk behavior of the operating personnel can be effectively identified, the production safety is guaranteed, and the risk caused by human errors is reduced.

The invention provides a radar-based human skeleton estimation method, system and product, and the method comprises the steps: carrying out the target detection of collected radar echo data, and generating a target four-dimensional point cloud containing distance information, speed information and angle information based on a target detection result; dividing the target four-dimensional point cloud into a plurality of human body topology point cloud blocks based on the velocity direction similarity of each point in the target four-dimensional point cloud; and inputting the human body topology point cloud block into a human body skeleton estimation network for human body skeleton estimation to obtain a human body skeleton estimation result. According to the human skeleton estimation method provided by the invention, human topology priori can be constructed from sparse millimeter wave radar point clouds, point cloud block features sensed by a structure are extracted, the modeling capability of a model for the spatial relationship of key parts of a human body is enhanced, diversified and natural daily human behaviors in a non-inductive monitoring scene can be adapted, and the human skeleton estimation accuracy is improved. The human body skeleton estimation with higher generalization ability is realized, and the accuracy and robustness of human body posture prediction can be effectively improved.

The invention discloses an action error prevention method and system based on AI identification. The method comprises the following steps: acquiring a standard action video and a to-be-detected action video; inputting the standard action video into a dynamic serialization analysis model for training, and extracting movement track features of standard human skeleton points and standard key objects; inputting the to-be-detected action video into the trained dynamic serialization analysis model to extract motion track features of to-be-detected human skeleton points and to-be-detected key objects; and comparing the motion trail characteristics of the standard motion with the motion trail characteristics of the to-be-detected motion to obtain an error value, and comparing the error value with a preset threshold to judge whether the motion is wrong or not. The method can overcome the defects that a traditional detection method is low in efficiency, difficult to capture motion dynamic characteristics, poor in adaptability and the like, manual operation errors can be found in time, and the product quality and the production efficiency are effectively guaranteed.

The invention discloses a dance movement auxiliary generation method and system based on three-dimensional modeling. The method comprises the following steps: constructing a three-dimensional human skeleton model; generating a joint action set of each joint node in the dance action; constructing a dance movement auxiliary model based on fusion of a time sequence convolutional network and a graph convolutional neural network, obtaining three-dimensional joint movement trajectory data in continuous frames, and obtaining relevance among dance movements by analyzing time sequence features and collaborative movement rules in the dance movements; acquiring joint actions associated with the current dance action in the joint action set, and generating a candidate action set; and calculating to obtain a connection fluency score and a posture aesthetics score with the current dance movement, and weighting to obtain the candidate movement with the highest total score as the next dance movement. The method has the advantages that through precise skeleton modeling and kinematics constraint, the deep learning technology is combined, smooth and attractive dance actions are efficiently generated, and the method is suitable for dance creation and virtual performance.

The invention relates to the technical field of body posture recognition, and particularly discloses an automatic body posture recognition system based on video analysis, which comprises a video acquisition module, a preprocessing module, a key point detection module, a skeleton tracking module, an action recognition module and a cross-view fusion module. According to the scheme, a parallel detection and information fusion mode is adopted, and the detection precision of the key points of the human body is remarkably improved by utilizing the accurate position of the high-resolution feature and the rich semantics of the low-resolution feature; the target is tracked according to the topological structure and the key point position of the human skeleton, the method has extremely high robustness for illumination and dressing changes, and a more stable and continuous human skeleton movement track is generated; the time sequence convolution and the space-time diagram convolution are combined to carry out joint modeling on the space and time double-domain features, complete action representation is obtained, and the stability, the accuracy and the adaptability of body posture recognition are effectively improved.

The invention discloses a video-based digital human motion capture and motion generation method and device and a medium, and relates to the technical field of machine vision, and the method comprises the steps: obtaining a video containing human motion; extracting a body area image frame and a hand area image frame corresponding to the figure based on the video; performing human body reconstruction based on the body area image frame and the hand area image frame to obtain a 3D human body model; and acquiring human skeleton information based on the 3D human body model, and generating a skeleton action file based on the human skeleton information. According to the scheme disclosed by the invention, the digital human body motion capture cost is reduced, and the problem that animation needs to be made again due to skeleton adjustment is solved.

The invention provides a human skeleton behavior recognition method based on a multi-modal enhancement converter and a hypergraph structure. The method comprises the following steps: S1, acquiring original skeleton data representing human behavior recognition; preprocessing the skeleton data to obtain four skeleton modal data in total; s2, carrying out single-mode feature extraction on the four types of skeleton modal data, and then carrying out multi-mode feature fusion to form unified skeleton features; s3, performing feature refining and three-dimensional dynamic feature reinforcement on the skeleton features of the unified mode in time and space; and S4, the refined skeleton features are subjected to a global average pooling layer and a full connection layer to obtain a behavior identification prediction result, and the result is a discrete action classification label. According to the method, a lightweight multi-modal converter network and a double hypergraph topological structure are constructed to enhance the multi-modal skeleton data fusion performance and capture the cooperative motion relationship between joint groups, so that the recognition accuracy and recognition efficiency of double interaction behaviors are improved.

The invention discloses a human body behavior recognition method, system and device based on a space-time shift attention mechanism and a medium. The recognition method comprises the following steps: firstly, performing position coding on human skeleton data as input of the spatial feature extraction module; secondly, a spatial feature extraction module extracts spatial features through global cyclic shift operation, a physical topology matrix and a spatial self-attention mechanism; then, taking the output of the spatial feature extraction module as the input of a time feature extraction module, and extracting time features by the time feature extraction module through a multi-scale time convolution module and a time transformer module; then, the classification module performs classification according to the features extracted by the space and time module to obtain the output of the single-joint flow; similarly, obtaining the output of the single skeleton flow; and finally, carrying out weighted fusion on the single-joint flow output and the single-bone flow output to obtain final recognition precision. According to the method, richer local and global space-time feature information can be captured, the human skeleton topological relation is effectively supplemented, and the method has the advantage of effectively extracting the local and global space-time dependency relation between the nodes.

The application discloses a kind of spatio-temporal decoupling human behavior recognition method, device and equipment based on dynamic semantic guide mask, comprising: extracting human skeleton sequence data from original video and carrying out data enhancement;Based on dynamic semantic guide mask mechanism, the skeleton sequence after data enhancement is masked in operation in two dimensions of space and time;Skeleton sequence data after masking operation is sent into query encoder and momentum encoder respectively, spatial representation and time representation are obtained respectively, cross-domain contrast loss is constructed, contrast learning training is carried out to obtain human behavior recognition model;The video to be identified is input into human behavior recognition model, and the prediction result of human behavior in the video to be identified is obtained.The spatio-temporal decoupling human behavior recognition method, device and equipment based on dynamic semantic guide mask of the application significantly improve human behavior recognition precision and environmental adaptability without manual label.

This invention belongs to the field of computer vision technology, specifically to a method for extracting a human skeleton from point cloud data. To make the human skeleton extracted in complex environments more stable and robust, this method uses a trained deep learning network to extract features from the original point cloud. This method then aggregates these features using a feature pooling operation, deriving joint points from the aggregated features. Finally, these joint points are connected in a specific order to form a three-dimensional human skeleton, enabling applications such as behavior recognition and other human-computer interaction applications.

The invention discloses an attitude estimation method based on graph convolution and double-branch fusion, and the method comprises the steps: employing a double-branch structure, a Mama branch and a Transform branch to work in parallel, enabling the Mama branch to process long-distance time dependence information through employing an efficient state space model, capturing a long-time-span correlation mode, and enabling the Transform branch to carry out the parallel processing of the long-distance time dependence information; the Transform branch strengthens modeling of local and global attention through a self-attention mechanism and pays attention to interaction relations of different time steps, and after the two branches are output and fused, dynamic changes of human body joint points on a time sequence can be more accurately expressed, a complex time sequence mode can be flexibly and effectively processed, different human body actions can be better adapted, and posture estimation accuracy is improved. The GCN is used for extracting the spatial topological features of the human skeleton, generating the preliminary feature representation, subsequently performing spatial structure optimization on the fused time features by using the GCN, and generating the structured associated features, so that the mode of combining the spatial information and the time sequence information can more comprehensively understand the posture of the human body and consider the dynamic change of the time and the relative position and the connection relationship of the space.

The invention provides an intelligent motion generation method, an intelligent motion generation system, electronic equipment and a computer readable storage medium, in the intelligent motion generation method, motion feature data are mapped and aligned to a virtual human skeleton structure, a preliminary motion sequence is obtained, and in addition, the motion feature data are mapped and aligned to the virtual human skeleton structure; and according to the virtual human skeleton structure and the preliminary action sequence, optimizing the action of the virtual human skeleton structure, and outputting an optimized action sequence, so that after the preliminary action sequence is obtained according to a redirection strategy based on the frame sequence of the video of the human body motion, the motion of the virtual human skeleton structure is optimized. And an optimized action sequence obtained by optimizing the preliminary action sequence can be more matched with the body shape of the virtual human.

The invention provides a human body posture detection method and device, electronic equipment and a storage medium. The human body posture detection method comprises the following steps: firstly, acquiring an infrared depth image in a target scene; and inputting the infrared depth image into a pre-constructed human body detection model for human body detection to obtain a human body region image. Inputting the human body region image into a pre-constructed human body key point detection model for key point detection to obtain coordinates and confidence of human body skeleton key points in the human body region image; and determining the type of each human skeleton key point based on the confidence coefficient. And finally, connecting each human skeleton key point based on the type to obtain a human skeleton feature vector, and determining a human posture based on the human skeleton feature vector. By using the method provided by the invention, the key points of the human skeleton can be accurately identified based on the infrared depth image, so that the feature vector of the human skeleton is obtained, the human posture is determined, and the safety of automatic driving is improved.

The present invention belongs to the field of artificial intelligence recognition, and specifically relates to a wave-for-help recognition system and method, including: a feature extraction unit, a calculation module, and a wave-for-help detection unit; the feature extraction unit is used to extract acoustic features and obtain a preprocessed face image, and send them to the wave-for-help detection unit respectively. At the same time, obtain the information of the key points of the human skeleton and transmit it to the calculation module; the calculation module is used to detect the waving action, detect the waving amplitude, and detect the sitting, lying or standing posture of the person. At the same time, obtain the waving frequency, and send the detection results including the stretch index, the waving action state, the sitting, lying or standing posture of the person, and the waving frequency to the wave-for-help detection unit; the wave-for-help detection unit uses the comprehensive weight method to determine whether the person is performing a wave-for-help action. The wave-for-help fusion strategy of the present invention: propose a polymorphic fusion strategy, making the final result of the wave-for-help determination more robust.

The invention discloses a human behavior prediction method based on a double-flow space-time diagram convolutional network, and belongs to the technical field of computer vision and deep learning, and the method comprises the following steps: S1, obtaining a human skeleton data set; s2, preprocessing skeleton point data; s3, processing the preprocessed skeleton point data through a double-flow space-time diagram convolutional network; s4, constructing a multi-scale joint point spatial dependency relationship; s5, processing the output features of the first stream through frequency weighting and a channel feature importance attention mechanism, dynamically adjusting the feature weights of high-frequency and low-frequency channels, and generating enhanced spatial-temporal features; and S6, fusing the output features of the double-flow network, generating a final behavior prediction result, and realizing human body behavior prediction. According to the method, through a multi-space category modeling graph convolution module and a frequency weighting and channel feature importance attention mechanism, a multi-space category modeling double-flow space-time graph convolution network is further provided, and the accuracy of behavior prediction is remarkably improved.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

The invention provides a skeleton behavior recognition method based on a multi-scale sampling aggregation graph convolutional network, which can improve the behavior recognition accuracy of specific actions only involving local joint changes on the basis of keeping the recognition accuracy equivalent to that of an existing model. In a multi-scale joint sampling image convolution module, joint sampling is carried out on human skeleton features, the importance of each joint part is calculated, and meanwhile, joint reconstruction is carried out according to an original structure of a human body to obtain local joint enhancement features; and then combining the local joint enhancement features and the global joint features, extracting and obtaining features playing an important role in specific actions through a joint guide attention module, fusing the important features with the global joint features and the local joint enhancement features, extracting a spatial information dependency relationship through multi-scale map convolution MS-GC, and obtaining a spatial information dependency relationship. And learning of local joint and global joint feature dependence is realized.

The embodiment of the invention discloses a human body part recognition method, a mattress and a storage medium, and the method comprises the steps: obtaining the three-dimensional contour data of a human body on the mattress through a laser radar sensor, and extracting the two-dimensional projection coordinates of a human skeleton according to the three-dimensional contour data; the whole pressure distribution data of the mattress is obtained through a piezoelectric film sensor, multiple target areas are divided according to the whole pressure distribution data and the two-dimensional projection coordinates of the human skeleton, and the multiple target areas comprise a head area, a trunk area and a limb area; obtaining local pressure distribution data of a local area of each target area through an air pressure sensor, and obtaining a central point of each target area according to the local pressure distribution data; and according to the two-dimensional projection coordinates of the human skeleton and the central point of each target area, reconstructing each body part of the human body, and identifying the position of each body part of the human body on the mattress. By implementing the embodiment of the invention, the position of each part of the human body on the mattress can be accurately identified.

The invention discloses a small sample action recognition method based on multiple tasks and local parallel attention, which comprises the following steps: S1, initializing an SSL-LPA network model, modeling a human skeleton sequence into a space-time diagram, dividing the space-time diagram into a training set and a test set, and constructing a support set and a query set; s2, inputting a training sample, and executing a feature extraction task to extract a support sample and a query sample; s3, performing data enhancement on the support sample, and then executing a self-supervision auxiliary task; s4, executing a supervised classification task, expanding a sample space, and enhancing action uniqueness between a prototype vector and a query sample; s5, calculating the similarity distance between the prototype vector and the query sample to determine the category of the query sample; s6, the supervised classification task and the self-supervised auxiliary task have a synergistic effect, and the SSL-LPA network performance is optimized through mutual feedback; the robustness and generalization ability of the model are improved, and the ability of accurately identifying the action can be learned under the condition of limited label samples.

The embodiment of the invention provides an intelligent alarm method and device for tumble monitoring, and the method achieves the effective protection of the privacy of a user through the innovative design of a low-position visual angle collection mechanism, the setting of the installation height of a camera not exceeding the knee joint, and the starting of collection only during the abnormal motion. A detection mechanism based on a human skeleton model is constructed, and an accurate fall judgment strategy is established by combining multi-part vertical distance analysis and motion state parameters. An intelligent alarm mechanism is introduced, and quick response of an emergency contact person is realized through key video packaging and emergency channel establishment. According to the method, while the privacy of the user is protected, the defects of the traditional technology in the aspects of data acquisition, tumble detection, alarm response and the like are effectively overcome, and the practicability and reliability of tumble monitoring are remarkably improved.

The invention discloses a method and a system for detecting the falling risk of a critical edge hole of a major project, and belongs to the field of falling safety monitoring, and the method comprises the steps: collecting the critical edge danger information of a project site, building a data set, converting the video monitoring data of the project site into a picture frame, and carrying out the marking processing. Constructing a yolov8 improved model, and detecting data set information of the security equipment; the protection device data set is sent to a Fast R-CNN for training, and a protection device detection model is obtained; and an improved ST-GCN model is constructed, human skeleton joint point information is sent into the improved model for training, hyper-parameters are optimized, the model performance is improved, and accurate identification of dangerous behaviors is realized. And inputting the results of the multi-class edge risk information into a constructed multi-class multi-source information fusion early warning rule method, performing calculation processing, and then triggering an early warning mechanism to perform visual alarm, so that the risk detection effect and the anti-interference performance of the system are improved, and the method is suitable for major engineering edge hole falling risk management.