846 results about "Real time video" patented technology

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention discloses a video stream adaptive low-delay real-time transmission method and system based on edge calculation. The method comprises the following steps: receiving a real-time video stream from a network camera, creating a pipeline queue, adding timestamp information for each video frame, and setting a queue protection mechanism; the coded video frames are taken out from the input queue, and the frames in the video are processed through hardware acceleration decoding; the resource use condition of the system is monitored in real time; executing a self-adaptive frame skipping decision according to a performance monitoring result; timestamp generation: dynamically calculating a timestamp interval according to an actual processing frame rate; receiving the decoded original video frame and the corresponding timestamp information, accelerating decoding by using hardware, and executing a video coding operation; and packaging and transmitting the coded video data, and providing a standard protocol interface to be connected with a client for playing. According to the scheme, stable low delay and relatively low resource occupation can be kept, and meanwhile, the video quality is remarkably improved.

The invention relates to the technical field of photovoltaic power station operation and maintenance, in particular to a photovoltaic power station intelligent inspection system based on AI vision, which comprises a video acquisition module, a geometric reference construction module, a tremor offset resolving module, a coordinate inverse correction module and a defect fine calibration module, the video acquisition module is used for acquiring a real-time video stream of an unmanned aerial vehicle polling photovoltaic array, and performing time-space synchronization calibration on the video stream to generate an original image sequence. According to the invention, the computer vision technology is utilized to calculate a current frame blanking point in a video picture as a tiny offset of a reference object relative to a reference position in real time, the displacement is deducted from a GPS coordinate, and a coordinate inverse correction module is utilized to inversely calculate a visual axis offset into a ground projection error. The shake amount and the shake direction of the camera at each moment can be accurately calculated, and then the GPS coordinates are corrected in turn, so that the geographic accuracy of defect positioning is improved, and the operation and maintenance personnel can accurately find a fault component.

User authentication, spoofing and replay attack prevention, liveness detection, and user-and-document verification using a live video stream with spatial challenges. A camera of an electronic device captures and transmit a live selfie user-facing video, as part of a user registration process. The user is instructed to spatially move his body or face, such that his face would appear within a first particular on-screen shape; and to also, concurrently or simultaneously, spatially hold in his hand or move a particular an identification document such that it would appear within a second on-screen shape. Optionally, the on-screen shape moves on the screen, and the user is required to spatially move the relevant item to keep it within the boundaries of the moving on-screen shape. The system then analyzes the video via computerized vision, to determine whether the user complied with the spatial manipulation challenges.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

A system and method for latent geodesic traversal across multi-axis hyperspaces for real-time video reconstruction and augmentation. Spatiotemporal video data are compressed into navigable latent representations using hierarchical and Lorentzian autoencoders that preserve geometric and temporal structure. A geodesic traversal engine computes paths across spatial, temporal, spectral, and semantic axes, guided by symbolic anchors and spatiotemporal routing protocols. A correlation network restores fine detail, while an augmentation generator synthesizes additional or counterfactual content to enable infinite zoom, continuous multi-scale exploration, and temporally coherent augmentation. A strategy caching system preserves successful traversal patterns for reuse, supporting persistent learning and adaptive real-time performance.

The invention discloses an intelligent security and protection management system and method based on abnormal target tracking, and relates to the field of security and protection management, which analyzes the space-time accessibility of a target in an environment topology after the target is lost, models the dynamic evolution process of target characteristics, and compares the judgment reference of identity re-identification from the static global appearance, so that the identification accuracy of the target is improved. And transferring to a dynamic probabilistic spatio-temporal search model with perspectiveness. And furthermore, based on the model, actively predicting a time-space window where a target may appear and an attenuated appearance portrait, performing targeted search and matching in a specified real-time video stream, and realizing reliable re-association of the identity according to the confidence of a matching result. Therefore, two key variables, namely environment topology and time decay, are introduced into a tracking mechanism, so that active prediction of the space-time position and the appearance state of the missing target can be realized, and the continuity and the accuracy of identity tracking can be guaranteed under the complex condition that the target is separated from the visual field for a long time.

The invention provides a subway platform edge behavior abnormity real-time identification and early warning method based on deep learning, and relates to the technical field of deep learning, which comprises the steps of obtaining real-time video data, performing pedestrian detection and track extraction, calculating behavior characteristic parameters and a dynamic distance threshold value, performing danger grade division on pedestrians, and performing early warning and early warning. According to the method, time sequence feature extraction is carried out on potential dangerous targets, a behavior abnormity scoring mechanism is established, timely early warning of abnormal behaviors is realized, the accuracy and real-time performance of safety monitoring of the subway platform can be effectively improved, and the occurrence rate of safety accidents can be reduced.

The invention relates to the field of coal mine safety and mine system intellectualization, in particular to a multi-source information fusion supervision method for an underground coal mine drilling process, which comprises the following steps: S1, acquiring continuous synthesized monitoring pictures in the underground coal mine drilling process to form a supervision video stream; s2, performing region segmentation on each frame of synthesized monitoring picture in the supervision video stream to obtain a corresponding static UI region, a dynamic data region and a real-time video region; s3, compressing a static UI region, a dynamic data region and a real-time video region of each frame of synthesized monitoring picture in the supervision video stream by adopting corresponding compression strategies to obtain corresponding compressed data; s4, fusing the compressed data of the static UI area, the dynamic data area and the real-time video area to generate a regional compression supervision video stream; and S5, storing the regional compression supervision video stream to realize subsequent inspection and supervision. According to the invention, the storage efficiency of supervision videos and the efficiency and accuracy of coal mine underground drilling process supervision can be improved.

The invention provides a real-time video monitoring intelligent analysis system based on deep learning, and relates to the technical field of computers, and the system comprises a video collection unit which is used for obtaining a real-time monitoring video stream to be analyzed, and extracting a target video frame sequence from the real-time monitoring video stream; the scene analysis unit is used for processing the target video frame sequence by using a pre-trained scene analysis model to obtain a dynamic object mask and a scene semantic graph corresponding to the real-time monitoring video stream; the acquisition unit is used for acquiring thermal infrared characteristics and dynamic environment variable information of a monitoring area corresponding to the real-time monitoring video stream; and the semantic segmentation unit is used for inputting the dynamic object mask, the scene semantic graph, the thermal infrared features and the dynamic environment variable information into a pre-trained semantic segmentation model to obtain a semantic segmentation result output by the semantic segmentation model. By applying the method provided by the invention, the monitoring video can be accurately analyzed.

A system and method for real-time facial and sentiment detection using a computing system. The system includes a video input module that receives real-time video input from various sources such as webcams, security cameras, and smartphone cameras. The video frames are pre-processed by adjusting the resolution, converting color spaces, and isolating the foreground from the background. A facial detection module employs a convolutional neural network to identify and localize human facial regions within the video frames. Geometric and appearance features are extracted from the localized facial regions by a feature extraction module. A sentiment classification module classifies the extracted features to determine sentiments using a deep learning model. The system also includes a module for API integration, enabling third-party applications to utilize the sentiment recognition results.

The invention discloses a landscape object identification and explanation method and system, a medium and a product, and relates to the technical field of augmented reality. The method comprises the following steps: when the head motion state of a user is stable, triggering a camera to collect and recognize a single-frame image, matching a recognition result with a regional knowledge base, and generating a candidate object list and coarse-grained poses which are sorted according to spatial probability and historical popularity; performing feature point matching on a current frame and an ideal frame in the video stream, and calculating a fine-grained pose of a target landscape in the candidate object list relative to a camera coordinate system according to a matched target feature point; according to the fine-grained pose and the real-time eye movement data, determining a drop point part of the sight line of the user on the target landscape, forming a multi-dimensional query vector according to the drop point part, the intention of the user and the environment context parameters, and generating multimedia explanation content; and carrying out fusion rendering on the multimedia explanation content and the real-time video stream, and outputting and displaying. By implementing the technical scheme, the personalized multimedia explanation content can be generated.

According to the invention, an unmanned aerial vehicle platform is creatively combined with the improved YOLOv11, and the unmanned aerial vehicle construction site safety helmet intelligent inspection method based on the improved YOLOv11 is provided. The method comprises the following steps: firstly, carrying out digital planning on an inspection route, and generating an executable flight path file; the unmanned aerial vehicle carries high-definition camera equipment, and carries out omnibearing video acquisition on a construction area according to a preset route; and the ground control center receives the real-time video stream data and inputs the real-time video stream data into a specially-designed safety helmet identification model to complete intelligent analysis of the state of personnel safety equipment and record of violation events. The detection model is subjected to triple optimization on the basis of a YOLOv11 architecture: a C3k2SPD module is adopted to replace a C3k2 module in an original backbone network, an ASPP module is introduced to replace an original SPPF module, and meanwhile, an NWD loss function is fused at a detection head part, so that the small target recognition capability and the complex scene fitness are remarkably improved.

The invention provides a multi-target seamless tracking method and system based on active prediction and feature pre-fusion, and the method comprises the steps: obtaining real-time video streams of a plurality of intelligent cameras for registration, and obtaining a unified fusion video stream under a global geographic space coordinate system; a plurality of dynamic targets are recognized according to the fused video stream, a globally unique identity label is distributed to each dynamic target, a three-dimensional space prediction track and a virtual relay area of each dynamic target are determined by combining the fused video stream, and a relay intelligent camera is determined according to a shooting condition index; the relay intelligent camera is aligned with the corresponding virtual relay area, when the relay intelligent camera recognizes the dynamic target in the virtual relay area, the master control authority is switched to the relay intelligent camera for seamless tracking, and the virtual relay area where the dynamic target appears is actively predicted in a prospective mode for alignment of the relay intelligent camera; shooting view is ensured, and target losing is avoided.

The invention provides a personnel safety early warning and prediction method and system based on dynamic perception of an unmanned aerial vehicle. The method comprises the following steps: collecting real-time video data of a construction site through an inspection unmanned aerial vehicle; collecting high-precision pose data of the unmanned aerial vehicle; processing the video data, identifying personnel and construction equipment, and obtaining geographic coordinates of the personnel and the construction equipment; performing risk analysis in combination with the dynamic electronic fence of the construction equipment and the behavior intention prediction result of the personnel, and generating personnel-related safety early warning; the system comprises a data acquisition module, a data processing and identification module, a prediction analysis module, a risk research and judgment module and an early warning execution and feedback module. According to the invention, personnel and equipment high-precision positioning, accurate behavior intention prediction and multi-dimensional risk early warning personnel safety early warning and prediction can be realized.

The invention belongs to the technical field of underground safety intelligent monitoring and early warning, and particularly relates to an underground safety posture recognition and early warning method based on self-adaptive cascade skeleton compensation, which comprises the following steps: S1, acquiring real-time video stream data of a coal mine underground hydraulic support operation area; s2, extracting key point coordinates and confidence of the skeleton frame by frame by using a lightweight attitude estimation network deployed at an edge computing terminal; s3, for each frame, counting the number of continuous missing frames from each key point to the current frame, and calculating the global missing key point proportion; s4, performing branch compensation on the current frame based on the diagnosis result of S3; s5, calling a preset operation safety risk index model, outputting a real-time risk score of the current frame, and identifying whether a violation behavior violating the violation posture rule exists or not; and S6, triggering a grading early warning mechanism. According to the method, the real-time performance of the edge terminal can be guaranteed, and meanwhile high-robustness recognition and accurate safety evaluation of the posture of the miner in the operation area of the hydraulic support are achieved.

The invention discloses an intelligent alarm system based on a real-time video stream of a platform, and relates to the technical field of video processing early warning, and the intelligent alarm system comprises a data collection module which carries out the real-time monitoring based on a monitoring camera, reads the image data of a point position camera, carries out the standardization and normalization of the data, and carries out the unified input of the image data; and the target discovery module is used for inputting the preprocessed image frame and the monitored area, identifying and positioning pedestrians in the image frame by adopting an algorithm based on target detection, performing label processing on the identified pedestrians based on a detected tracking algorithm byterack, and outputting the residence time of the current frame target in the monitored area. According to the platform safety management system, various advantages are brought to platform safety management, the alarm threshold value can be flexibly adjusted, decision support is provided, management personnel are helped to analyze passenger flow, optimize deployment and troubleshoot hidden dangers by recording related data, a'early warning-processing-optimization 'closed loop is formed, the safety management and control level is improved, and safety accidents are effectively prevented.

The invention discloses a vehicle violation manned intelligent detection method and system based on a CLIP and D-Fine cascade framework, and the method comprises the steps: collecting a real-time video frame image of a preset traffic monitoring network, processing the real-time video frame image, inputting a monitoring image into a CLIP-ILP model, employing the CLIP-ILP model as a filter, and combining with a Top-K screening strategy, and screening out candidate images; and inputting the candidate image into a constructed context enhanced D-FINE detection model, carrying out positioning and multi-class detection on a preset target, outputting a multi-class detection result, and executing verification of a spatial co-occurrence rule, a license plate position heuristic rule, a size consistency rule and a context consistency rule. And marking the result which does not pass the verification as suspicious or rejecting the result, and outputting the result which passes the verification as an illegal manned detection result to the target terminal. According to the method, the problem of balance between the recall rate and the precision in manned detection can be solved.

The application provides a wireless broadcast service message enhanced transmission method, which comprises the following steps: S1, connecting a broadcast service server and at least one first broadcast service client to a switch network subsystem in wired communication; the switch network subsystem is in communication connection with a Wi-Fi subsystem; the Wi-Fi subsystem is in wireless communication connection with at least one second broadcast service client; S2, presetting the Wi-Fi subsystem; S3, when a broadcast service client is interested in a video, a user feeds back an IP address of the broadcast service client which needs to receive the video; S4, performing legality check on the minimum transmission rate of the at least one first broadcast service client and / or the at least one second broadcast service client; and S5, transmitting by the Wi-Fi subsystem according to the parameters preset in step S2. According to the requirements of different service clients, different processing procedures can be executed correspondingly, so that the requirements of real-time video transmission can be met.

The invention relates to an indoor target positioning method and system based on machine vision and deep learning, and belongs to the technical field of target positioning. The method comprises the following steps: acquiring positioning data transmitted by a plurality of long-distance radio beacons deployed indoors in real time, and anchoring the positioning data to an indoor three-dimensional coordinate grid model node to obtain a global space reference framework; a real-time video image is obtained and preprocessed, and an LW-DETR algorithm is adopted to detect initial position information and size information of a target on an image plane; calculating a distance and an azimuth angle between a target and each beacon by combining beacon signal characteristics to obtain spatial position associated data; performing spatial registration and fusion on the multi-source data in combination with a global spatial reference frame to obtain target position information; and tracking and predicting the motion trail and speed information of the target, and integrating to obtain the final predicted position of the target. According to the method, the hardware collaborative dependence can be reduced, the anti-interference capability in an extreme environment is enhanced, and the indoor target positioning stability and precision are remarkably improved.

The invention discloses a crowd density detection method fusing optical flow and texture features, and relates to the technical field of computer vision, and the method comprises the following steps: collecting a real-time video stream of a camera, and carrying out graying, Gaussian filtering and perspective correction preprocessing; performing motion compensation by using image registration and offset transformation; modeling based on a Gaussian mixture model and extracting a foreground to generate a binary mask; analyzing a foreground coverage rate, an optical flow and texture features; pre-defining a multi-ROI and a density early warning standard; inputting the fusion features into a regression model and outputting initial density; dynamically calibrating and correcting the deviation; and generating a thermodynamic diagram superposition video to realize visualization. According to the invention, the optical flow and texture features are fused to improve density estimation precision, illumination resistance and dynamic background interference resistance; the dynamic calibration maintains long-term accuracy, and the dynamic ROI adapts to scene change; the thermodynamic diagram can quickly identify risks, is adaptive to multiple scenes, and meets real-time monitoring requirements.

According to the method and device for the endoscope to automatically follow the instruments, the electronic equipment and the storage medium, the actual focusing center and the real-time video of the endoscope are firstly obtained, then the real-time video is processed, the current operation stage is recognized, the real-time position information and the weight of each instrument in the real-time video are obtained, and the real-time position information and the weight of each instrument in the real-time video are obtained; the weights of the instruments and the corresponding position information are subjected to weighted fusion, the semantic attention center of the endoscope is obtained, and finally the focus center of the endoscope is adjusted according to the actual focus center and the semantic attention center. According to the technical scheme, the weight and the semantic attention center of each instrument are calculated by fusing the multi-dimensional features of the basic data, and finally, the logic of the focusing center of the endoscope is dynamically adjusted, so that intelligent following in a multi-instrument collaborative scene is realized, the defects of single instrument tracking and lack of semantic adaptation in the prior art are overcome, and the tracking efficiency is improved. And the flexibility and the accuracy of the automatic following instrument of the endoscope are improved.

The invention discloses a real-time video processing method and system based on artificial intelligence, and belongs to the technical field of computers. Through deep fusion of multi-source information and a regional adaptive coding mechanism, the problem of collaboration between content generation and network resource allocation in a high-interactivity live broadcast scene is solved. In the feature extraction link, video, audio and text streams are synchronously processed, so that three-dimensional perception of a live broadcast scene is realized; in the multi-mode driving instruction set generation process, audio event features and text interaction features are subjected to semantic association, so that content generation and real-time interaction are highly synchronized, and response delay caused by information splitting in a traditional scheme is avoided; according to the regional differentiation coding processing, resource allocation is dynamically adjusted according to the regional importance of visual semantic feature recognition and the network state, and the coding quality of a digital human region is preferentially guaranteed, so that the stability of core visual elements is maintained under the network fluctuation condition, and the intelligent level of real-time video processing and the user experience are improved.

The invention relates to the technical field of computer graphics, in particular to a virtual-real fusion method and system based on Gaussian splash live-action reconstruction, and the method comprises the following steps: S1, carrying out the motion recovery structure processing of an obtained target scene image sequence, and generating a sparse point cloud of a target scene; s2, training the sparse point cloud through a Gaussian splashing technology, generating a live-action three-dimensional Gaussian model, carrying out lightweight processing on the live-action three-dimensional Gaussian model, and outputting a lightweight live-action model; and S3, acquiring an internal parameter matrix, an external parameter matrix and a real-time video stream of the physical camera. According to the method, the spatial mapping relation between the live-action reconstruction model and the artificial fine model is established, the user interaction instruction is responded, and the explicit and implicit states of different detail level models in a unified scene are dynamically controlled, so that smooth switching without context loss from macroscopic to microscopic is realized; the problems of visual angle jump and spatial cognition interruption caused by system switching in a traditional scheme are solved.

The invention provides a vehicle assembly process identification method and device, equipment and a medium. The method comprises the following steps: acquiring real-time video data and sensor data of an assembly process; inputting the real-time video data into a trained recognition model to recognize the position of an assembly tool and the position of an assembly part in the real-time video data; on the basis of the position of the assembling tool and the position of the assembling part at the current moment, whether the current specially-assembled part is carried out according to a preset assembling process or not is determined; and based on the position of the assembly tool at the current moment, the position of the assembly part and the sensor data, whether the assembly part which is being assembled is assembled or not is recognized. The operation quality and the overall assembly efficiency can be improved.

The invention relates to the technical field of video processing and artificial intelligence, and discloses an AI video integrated device and a processing method thereof, and the device comprises the following steps: a video collection module obtains a real-time video stream; the event pre-classification module performs analysis to generate an event probability vector; the dynamic strategy scheduling module distributes a control instruction according to the vector. The deep analysis module loads an artificial intelligence model after receiving the instruction and extracts structured metadata; the archive packaging and anchoring module integrates the video stream and metadata, constructs a digital archive package, and carries out encrypted hash calculation, trusted timestamp anchoring and encrypted packaging; and the storage and output module transmits the encrypted file package to an external file management system. An event-driven dynamic strategy scheduling mechanism is introduced, low-power-consumption operation is kept in a conventional state, when the lightweight event pre-classification module recognizes a key event, a high-performance processing mode is switched to, deep analysis resources are mobilized, and the resource utilization efficiency of the device is improved.

The invention relates to a motion posture tracking method and device, a storage medium and electronic equipment, and relates to the technical field of image data processing, and the method comprises the steps: carrying out the preprocessing of a real-time video stream, and obtaining a processed target video stream; selecting a target image from the to-be-analyzed images at intervals of a preset frame number; according to each target image, determining a motion track of a single person object in the target video stream through a preset byterack algorithm, and obtaining a target attitude point of the single person object in each target image; selecting at least one tracking object from the personnel objects according to the movement tracks, and performing dynamic smoothing processing on target attitude points of the same tracking object in the target images to obtain an initial attitude point queue; and performing interpolation on the target attitude points of the same tracking object in each residual image to obtain an attitude point supplement queue, and further determining a final attitude point queue of the corresponding tracking object. The method has the effect of improving the personnel motion posture tracking stability.

The invention belongs to the field of artificial intelligence, and particularly relates to an electric power operation safety violation behavior identification method based on deep learning. The objective of the invention is to solve the problems of low efficiency, insufficient coverage and poor illegal behavior recognition precision and robustness under complex working conditions of traditional safety management. The method comprises the following steps: acquiring real-time video data of an electric power operation site, and performing preprocessing (target detection and key area positioning); performing spatio-temporal feature extraction and behavior analysis by using a deep learning model; identifying a security violation behavior; and generating and sending violation behavior early warning information. According to the invention, the method can achieve the automatic and high-precision recognition of various power operation safety violation behaviors, improves the intelligent level and response speed of power operation safety management, remarkably reduces the accident risk, and provides a safer and more reliable working environment for operators.

The invention provides a vision-based intelligent fusion terminal security detection method and an intelligent fusion terminal. The method comprises the following steps: acquiring real-time video data around the intelligent fusion terminal; extracting an image from the real-time video data, and performing target identification on the image; after a preset target is recognized, determining a candidate area with motion change by adopting the real-time video data; detecting a target in the candidate region based on a trained target detection model, and determining a detection result; and determining whether an abnormal event occurs based on a detection result. According to the invention, by deploying a multi-event unified detection model in the low-power-consumption embedded terminal, combined discrimination and active early warning of multiple types of environmental security events such as stranger approaching, smoke and flame are realized, and the method is suitable for security protection scenes of power concentrators, industrial control equipment and other unattended intelligent fusion terminals.

The invention provides a construction equipment safety early warning and prediction method based on unmanned aerial vehicle dynamic perception. The method comprises the following steps that real-time video data of a construction site and high-precision pose data of an unmanned aerial vehicle are collected through an inspection unmanned aerial vehicle; processing the video data, identifying construction equipment, key components and constructors, and obtaining geographic coordinates of the construction equipment, the key components and the constructors; generating a dynamic electronic fence changing along with the operation state based on the key component; predicting a future movement track of the construction equipment and analyzing track abnormity in combination with historical data and task types of the equipment; calculating a collision risk level between the construction equipment, and optimizing risk judgment in combination with an out-of-range overlapping degree; according to different construction equipment types, dynamically planning an unmanned aerial vehicle inspection path through a differential inspection strategy; and risk analysis is performed in combination with the dynamic electronic fence and the prediction result, construction equipment related safety early warning is generated, and the safety management level of the construction site can be improved through the method.