292 results about "Construction site safety" patented technology

The invention discloses a construction site safety risk intelligent assessment method and system, and belongs to the technical field of engineering supervision. The method comprises the following steps: S100, collecting multi-source data including an equipment state, an image video, a personnel state, an equipment distance, an environment parameter and an engineering text in real time; s200, performing space-time alignment, preprocessing and cross-modal feature fusion on the multi-source data; s300, outputting a risk score and a risk level label based on the dynamic risk knowledge graph and a multi-model fusion algorithm; s400, generating a hierarchical disposal strategy according to the risk scoring hierarchy, and realizing risk closed-loop management and control through rectification verification and cooperation of multiple parties; and S500, outputting a three-dimensional visual risk distribution map and a compliance report. Through technologies of multi-source data fusion, dynamic risk mapping knowledge, intelligent closed-loop management and the like, comprehensive perception, accurate evaluation, efficient management and control and compliance landing of engineering supervision safety risks are realized, the occurrence rate of safety accidents is remarkably reduced, and reliable technical support is provided for intelligent construction site construction.

The invention discloses a construction site safety risk intelligent early warning system and method based on BIM and big data analysis, relates to the technical field of building engineering construction safety, and solves the problem that it is difficult to transmit construction site multi-source data which is collected and preprocessed in real time in real time and carry out space mapping with a BIM model. A rule engine is difficult to carry out initial early warning; a machine learning model is difficult to analyze time series data, predict collapse risks and identify dangerous behaviors; a risk prediction model is difficult to construct and is difficult to integrate into a BIM model; and pushing and closed-loop management are difficult to carry out on the risk early warning information. According to the method, the multi-source data is collected at the construction site, the digital twinborn scene is constructed by mapping the multi-source data to the BIM model by means of space-time alignment, the multi-source data is analyzed and processed by applying technologies such as a rule engine and a machine learning algorithm, and the result is integrated to the BIM model, so that visual risk monitoring and early warning are realized.

The invention discloses a railway intelligent construction site safety penetration type management messenger platform which comprises a multi-modal data fusion processing module, an edge computing node cluster module, a three-dimensional visual penetration type management interface module, an intelligent early warning and emergency response module, a self-adaptive network transmission module and the like. Real-time cleaning, alignment and correlation analysis are realized by using a dynamic data calibration algorithm, and a data island is broken; the edge computing node cluster carries out localization preprocessing and the like on data in a key area, so that the load of a central server is reduced; the three-dimensional visual interface is based on a digital twinborn construction model, supports drilling type viewing and realizes three-dimensional monitoring; the intelligent early warning system adopts a reinforcement learning model to automatically trigger multi-channel early warning; and the adaptive network transmission module dynamically switches communication modes to ensure low-delay transmission of key data. The platform realizes real-time acquisition and integration of construction site data and reduces manual intervention.

The invention discloses a construction site unsafe behavior detection method and system based on image recognition, and relates to the technical field of intelligent visual safety monitoring, and the method comprises the steps: collecting a construction image containing a constructor, carrying out the rectangular frame labeling of unsafe behaviors in the construction image, and generating a structured labeling data set; inputting the structured annotation data set into a YOLOv5 target detection model, performing training through Mosaic data enhancement and an adaptive anchor box generation algorithm, and outputting the trained YOLOv5 target detection model; based on the trained YOLOv5 target detection model, performing 128-dimensional feature vector extraction and track association matching on a detected target through a DeepSORT multi-target tracking algorithm, and outputting a track data table; and the recognition stability and behavior analysis depth of the system in a complex environment are enhanced, so that automation and intelligentization of safety management of a construction site are effectively supported.

The invention provides an intelligent management and control system for real-time monitoring and early warning of construction site safety risks. The intelligent management and control system comprises a data acquisition module, a data preprocessing module, a risk identification module, a risk assessment module and an early warning and management and control module. According to the invention, through deep fusion of multi-module cooperation and multi-algorithm, full-process, high-precision and intelligent management and control of the safety risk of the construction site are realized, and the construction safety management efficiency and the risk early warning capability are effectively improved.

The invention discloses an intelligent safety inspection system for a construction site, and relates to the technical field of construction inspection, multiple data sources of the construction site are obtained through a data acquisition module, and a data fusion and processing module dynamically optimizes heterogeneous data through time synchronization, format standardization and noise filtering in combination with a self-adaptive weighted fusion algorithm, so that the construction site safety is improved. The intelligent analysis and early warning module evaluates the data difference degree based on machine learning, adopts clustering analysis to mine an abnormal mode for high-difference data, carries out safety risk identification on low-difference data, and dynamically adjusts an early warning strategy. The terminal interaction module pushes the inspection result and the early warning information to the mobile terminal APP, the monitoring center display screen and the voice broadcast device, so that a manager can remotely check the construction condition and quickly make a decision, the intelligent level of construction site safety inspection can be remarkably improved, efficient and accurate potential safety hazard recognition and dynamic early warning are achieved, and the safety inspection efficiency is improved. And the safety accident risk is effectively reduced.

The invention discloses a construction site safety monitoring method and system based on a large model and RAG, and the method comprises the steps: carrying out the intelligent analysis of real-time video data of a construction region based on a visual large model, and recognizing the behavior safety, violation operation, dangerous facilities and environment risks of construction personnel; adding a time label to the analyzed security event text to form a construction site security event log; reasoning and analyzing the security event log, determining whether security early warning is generated or not, and pushing early warning information; carrying out on-site intelligent voice broadcasting according to the early warning information; through a multi-RAG architecture, a construction site safety log database, a public construction safety management database and an enterprise internal safety management database are constructed, and construction safety information is stored by using a vector database and a graph database. By adopting the technical scheme of the invention, the problems of insufficient real-time monitoring capability, limited intelligent analysis capability and lack of effective knowledge base support in the existing construction site safety supervision are solved.

The invention relates to the technical field of construction site safety early warning, in particular to a multi-modal construction site safety early warning method and system for dynamic weight allocation, and the method comprises the following steps: deploying a multi-modal data collection module, and collecting and transmitting construction site data to a central processing unit in real time; constructing a correlation model according to the historical accident database; carrying out preprocessing and anomaly detection on real-time data, and dynamically adjusting a multi-modal weight according to a result; inputting the adjusted weight into the model, calculating a comprehensive early warning score and performing graded early warning; and correcting the model according to the early warning and accident matching degree. According to the method, the multi-modal data acquisition module is deployed to acquire sensor, visual and text data, and a dynamic weight distribution mechanism is combined, so that the weight of each modal can be adjusted in real time according to an anomaly detection result, the accuracy and comprehensiveness of potential safety hazard identification are remarkably improved, and the problems of one-sidedness and missing report of single-modal monitoring are effectively avoided.

The invention discloses an intelligent construction site safety evaluation method and system based on data elements, and relates to the technical field of computer platform load balancing, and the method comprises the steps: collecting construction site multi-source heterogeneous data, and carrying out the preprocessing of the construction site multi-source heterogeneous data; constructing a safety evaluation model of time sequence modeling and graph structure fusion through combination of a long short-term memory network and a graph neural network, performing feature selection and dimension compression operation on the processed multi-source heterogeneous data of the construction site to obtain an input feature tensor, and inputting the input feature tensor into the safety evaluation model; and calculating a comprehensive score according to a model output result, and generating a classification result and data feedback through an edge node. According to the intelligent construction site safety evaluation method based on the data elements provided by the invention, combined modeling of time sequence dependence and space association is realized through a deep modeling structure fusing LSTM and GNN, and the risk identification precision under multi-source heterogeneous data is effectively improved.

The invention relates to the technical field of construction safety assessment, and discloses a construction safety assessment method based on multi-model fusion. The method comprises the following steps: acquiring a multi-source safety monitoring data set of a construction site, wherein the multi-source safety monitoring data set covers image sequence data, sensor time sequence data and operation log text data; performing parallel feature extraction processing on the multi-source data to generate an image feature vector, a sensor feature vector and a text feature vector; calling a multi-model fusion framework to carry out cross-modal fusion processing on the three feature vectors to obtain a fusion security feature set; carrying out security risk assessment analysis based on the fused security feature set, and generating a security risk prediction value and a high-risk region identifier; and finally, generating a security policy adjustment instruction according to the risk prediction value and the high-risk area identifier, and feeding back the security policy adjustment instruction to the field control system to trigger real-time intervention operation. According to the method, a high-risk area can be recognized in time, intervention is triggered, and intelligent support is provided for safety management of a construction site.

The invention relates to the technical field of intelligent construction site safety management and Internet of Things, and discloses a construction personnel safety protection system and method for intelligent construction site safety management, and the system comprises a data collection module, a four-dimensional space-time tensor construction module, an incremental tensor decomposition module, a dynamic risk field modeling module, and a federated learning parameter updating module. A multistage safety response module; the method comprises the steps of collecting personnel data through intelligent equipment and constructing a four-dimensional tensor, extracting core features through dynamic decomposition to establish a risk propagation model, optimizing parameters in combination with federal learning, and finally triggering three-level response according to a risk field gradient to realize full-process closed-loop management and control of construction safety. According to the method, multi-modal data are fused to construct a four-dimensional tensor, incremental decomposition is carried out to extract dynamic features, a physical field model is coupled to predict risks, federated learning is used for collaborative privacy optimization, hierarchical response is carried out for closed-loop linkage control, and construction safety and precise protection and efficient emergency are realized.

The invention provides a construction site safety supervision method and system based on multi-mode intelligent perception, and relates to the technical field of highway construction supervision. A construction area is scanned through a 5G camera, multi-mode intelligent sensing data such as videos, positioning and environment sensors are fused, a three-dimensional model of a construction site is constructed, and the positions of equipment and personnel are accurately determined. Beidou positioning and safe area setting are combined, real-time safety detection is carried out, illegal intrusion of external vehicles and personnel is effectively prevented, and construction of equipment and personnel outside a safe area is avoided. According to the method, illegal behaviors of not wearing safety helmets and the like are intelligently identified by using AI, post-accident tracing is realized through the Internet of Things, accurate traffic guidance information is published in combination with Atdes and Baidu maps, the collision risk and potential safety hazards are reduced, and the safety of a highway construction site is comprehensively improved.

The invention relates to a construction site hidden danger identification method and system based on a visual language large model. The method comprises the following steps: acquiring real-time acquired image data of high-place operation on a construction site; preprocessing the image data to obtain a to-be-detected image; inputting the to-be-detected image into the construction safety visual language model to obtain a hidden danger recognition result; the hidden danger recognition result comprises a high-altitude falling safety hidden danger type and standard natural language image subtitles. By adopting the method, intelligent technical support can be provided for safety management of the construction site, and the occurrence rate of high-altitude falling accidents is effectively reduced by identifying high-altitude falling hidden dangers of the construction site and generating standardized image subtitles.

The invention relates to the technical field of artificial intelligence, and provides a construction site safety detection method, device and equipment and a medium, which can utilize a mobile wearable portable camera to collect real-time images of a target construction site so as to provide comprehensive site pictures; the real-time image and the building information model are aligned according to the external parameters of the camera, it is ensured that follow-up detection is conducted on the same view angle and pixel, and the detection accuracy is improved; an improved Transform network architecture is used for carrying out difference detection on a real-time picture frame and a building information model frame, and the problems that manual inspection is not high in accuracy and low in efficiency are solved; and mapping the difference mask to a three-dimensional engineering coordinate system of the building information model according to the external parameters of the camera and the depth map of the real-time image to obtain a three-dimensional coordinate point, and identifying a difference component and generating a three-dimensional thermodynamic diagram according to the three-dimensional coordinate point so as to realize comprehensive, accurate and efficient safety detection of the target construction site.

The invention discloses a construction hidden danger automatic detection method and system based on point cloud and image multi-mode. The method comprises the following steps: step 1, acquiring point cloud data and RGB image data and respectively preprocessing the point cloud data and the RGB image data; 2, safety equipment detection is carried out by establishing an improved YOLOv5 model; establishing a 3D-CNN model to qualitatively judge the abnormal behavior of the personnel; 3, combining a semantic segmentation model and point cloud data processing to carry out quantitative judgment of height calculation and coordinate conversion of the fence; and 4, realizing three-dimensional live-action model reconstruction based on point cloud feature matching, Poisson surface reconstruction and GPU rendering, and detecting potential safety hazards of a construction site. The accuracy of qualitative detection is further improved, the precision error of quantitative detection is reduced, the recognition efficiency and precision of potential safety hazards are greatly improved, intelligent and all-around guarantee is provided for safety management of a construction site, remote decision is assisted, the management cost is reduced, and the method is suitable for various construction scenes.

The invention discloses a building construction safety monitoring system based on the Internet of Things technology, and the system comprises the following steps: collecting various types of data of construction personnel, equipment and environment, constructing a unified data sequence, extracting feature information through employing an incremental principal component analysis method, and further employing an elliptical envelope line algorithm by the system, the method comprises the following steps: establishing an anomaly discrimination model in a plurality of local feature spaces, realizing the recognition of abnormal behaviors, integrating noise suppression processing and a dynamic parameter updating mechanism by a system, improving the monitoring accuracy and adaptive ability, combining the states of personnel, equipment and environment on a construction site, generating a corresponding risk level and an early warning control instruction by the system, and improving the early warning efficiency. Real-time risk identification and response linkage is realized, and the safety guarantee level of a construction site is improved. The method has the key characteristics of multi-source data fusion, boundary adaptive updating, risk response linkage and the like, and intelligent monitoring and dynamic intervention of construction site risks are realized.

The application relates to the technical field of artificial intelligence, and provides a construction site safety detection method, device, equipment and medium, which can collect real-time images of a target construction site by using a mobile wearable portable camera to provide comprehensive site pictures; the real-time images and a building information model are aligned according to camera external parameters, so that subsequent detection is carried out on the same visual angle and pixels, and the accuracy of detection is improved; the improved Transformer network architecture is used for difference detection of real-time picture frames and building information model frames, so that the problem of low accuracy and low efficiency of artificial inspection is solved; the difference mask is mapped to a three-dimensional engineering coordinate system of the building information model according to the camera external parameters and a depth map of the real-time images to obtain three-dimensional coordinate points, and the difference components are identified according to the three-dimensional coordinate points and a three-dimensional heat map is generated, so that comprehensive, accurate and efficient safety detection of the target construction site is realized.

The invention belongs to the technical field of environment state monitoring, and particularly relates to a construction site environment state monitoring method based on satellite images and unmanned aerial vehicle data, which comprises the following steps: acquiring satellite image monitoring information and unmanned aerial vehicle monitoring information of a construction site environment state; performing construction site environment surface coverage change analysis, and outputting construction site environment change characteristics; on the basis of a portable PM2.5 monitor arranged on a construction site, PM2.5 concentration characteristics are obtained; performing construction site equipment YOLO target detection, outputting an equipment distribution thermodynamic diagram, and determining construction progress characteristics of a construction site; constructing a construction site safety risk framework, and determining a construction site safety risk level; and determining a construction site environment state monitoring grade. According to the invention, through fusion of satellite images and unmanned aerial vehicle data, comprehensive and dynamic monitoring of the construction site environment state is realized, the monitoring precision and efficiency are improved, and powerful support is provided for scientific management of the construction site.

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 relates to the technical field of risk prevention and control, in particular to an electric power engineering construction site safety management method and system, and the method comprises the following steps: analyzing the spatial relation between constructors and high-voltage cable laying points based on the positioning and operation paths of the constructors, screening and adjusting path nodes, and judging the distribution of abnormal resident personnel; and dynamically evaluating the working condition risk level by combining the regional personnel density and the meteorological change, and adjusting the risk level of the operation region. According to the invention, through fusion of space positioning, path behaviors, risk monitoring and meteorological data, dynamic capture of operation behaviors and environment changes, combination of path space optimization and resident behavior screening, active perception of a space interaction relationship between personnel and a risk area, linkage of personnel distribution and extreme meteorology, continuous identification and linkage judgment of risk characteristics are realized, and the risk characteristics can be identified more accurately. And the partition risk levels are updated in real time according to operation behaviors and environmental conditions, and the field risk data linkage breadth and depth are enhanced.

The invention provides an intelligent construction site safety monitoring and early warning method and system, and the method comprises the steps: fusing the multi-source data, such as personnel positioning, equipment state and environment parameters, and building a structured time sequence feature set with spatial semantic annotation; constructing a symbolized rule base in combination with a field safety specification; applying a dynamic graph attention network, dynamically modeling personnel, equipment, environment nodes and an interaction relationship thereof, and deducing potential causal association strength between spatial entities; through path semantic distillation and anti-fact intervention, a compliant and accurate multi-stage causal reasoning path is formed, and key risk nodes and propagation channels are visualized in a three-dimensional BIM scene; and finally, dynamic early warning event generation and natural language interpretation are realized, model closed-loop optimization based on field feedback is supported, and the timeliness, interpretation and adaptive capability of construction site safety risk discovery are improved.

The invention belongs to the field of intelligent construction site construction safety management, and provides a construction abnormity allocation method and system based on the Internet of Things and artificial intelligence, and the method comprises the steps: collecting construction environment data, equipment states and historical data in real time, and carrying out the fusion to generate a dynamic risk score; executing dual-threshold early warning according to the dynamic risk score, and performing personnel matching according to an early warning result; and after personnel matching, generating an optimal scheduling path according to the physical distance between the matched personnel and the abnormal point. According to the scheme, multi-source sensor fusion and intelligent dynamic risk assessment are utilized, the system automatically identifies high-risk events and triggers the emergency process in real time, and compared with a traditional manual scheduling mode, the response time is greatly shortened, and it is ensured that key abnormal events are rapidly handled.

The invention discloses a high-performance construction site safety helmet detection method based on YOLO11 improvement, and belongs to the technical field of artificial intelligence and computer vision, and the method comprises the steps: carrying out the structure-level lightweight reconstruction of a Neck part in YOLO11, comprehensively optimizing the calculation efficiency and structure adaptability of Neck, replacing a conventional Conv module with a DepthwiseConv module formed by combining deep convolution and point convolution, upgrading a Bottleneck structure into a Bottleneck module, and carrying out the construction site safety helmet detection. The C3k and the C3k2 are respectively upgraded into a C3k module and a C3k2 module, so that the overall light weight and the function enhancement of the Neck structure are realized. Through module-level optimization, on the premise that the detection precision is not reduced basically, the calculation complexity and resource consumption of the Neck part are remarkably reduced, and the reasoning speed, deployment flexibility and energy consumption control capability of the model are improved.

The invention provides a construction site safety pre-warning system based on AI model identification. The construction site safety pre-warning system comprises a multi-source sensing layer, an edge calculation layer, a pre-warning decision module, a data middle platform and a report center, the invention discloses a construction site safety early warning method based on AI model identification. The method comprises the following steps that S1, a multi-source sensing layer collects field state data in real time; s2, the edge calculation layer receives and processes the field state data, and sends the field state data to an early warning system core and a command large screen for real-time feedback; s3, the face recognition unit dynamically monitors personnel access, updates on-site personnel information, and counts individual working hours according to the on-site personnel information for management personnel to check; s4, an early warning decision module performs intelligent analysis by using a configured early warning model according to the field state data; and S5, if the early warning rule is triggered, the system immediately displays early warning details on the commanding and dispatching large screen and gives out an alarm sound. According to the invention, full-process digital management of second-level intelligent sensing and risk early warning and disposal of potential safety hazards on a construction site is realized.

The invention discloses a water conservancy construction site safety early warning system which comprises the components of S1, a multi-source heterogeneous sensor module which is used for outputting an original data stream with a timestamp; s2, an edge processing module which is used for generating a time sequence feature vector sequence; s3, a graph modeling module which is used for mapping each node in the weighted graph into a low-dimensional fusion feature graph; s4, a risk identification module which is used for realizing risk level inference through side attention weight adjustment; s5, a self-adaptive early warning module which is used for outputting a multi-level early warning signal according to a risk level inference result; s6, a response module which is used for controlling an audible and visual alarm, a field terminal and a remote platform to execute response operation according to the multi-stage early warning signal; and S7, a cooperative control module which is used for realizing data acquisition interval adjustment through sliding mean deviation control. The method has the advantages of being comprehensive in sensing dimension, low in response delay and high in risk adaptability.

The invention provides a construction safety early warning method for a construction site, a terminal and a storage medium. The method comprises the following steps: constructing a physical model of an obstacle in a construction site based on a physical engine simulation algorithm; based on the physical model of the construction site and the state information of all obstacles in the construction site at the current moment, performing mixed collision detection, determining a target obstacle, screening out obstacles which do not collide, and reducing the calculation amount; physical motion simulation is carried out on the target obstacles to obtain state information of each target obstacle in a future time period; further collision prediction is carried out on the target obstacle based on the state information of the target obstacle in the future period, and a collision detection score is determined; and performing safety early warning on the construction site based on the collision detection scores of any two target obstacles in the future time period. According to the method, early warning can be carried out on possible accidents in advance, so that the occurrence of safety accidents is reduced, and the safety warning effect of a construction site is improved.

The invention discloses a building construction site safety management informatization system and method, and relates to the technical field of safety management. The method comprises the following steps: reading and recording hazard index readings of different construction points on a construction site on line in real time; obtaining data logs of reading change of different hazard index items of different construction points of the construction site according to the hazard index reading which is read and recorded in real time; unsafe construction sections in the construction site are obtained according to the data logs of the reading changes of the different hazard index items of the different construction points of the construction site; obtaining the unsafe coefficient of each unsafe construction section according to the data logs of the reading changes of different hazard index items of different construction points in the unsafe construction section; and sequentially arranging the unsafe construction sections from high to low according to the unsafe coefficients of the unsafe construction sections to obtain a field detection sequence of the unsafe construction sections. According to the invention, timely and effective safety management of the building construction site is realized.

The invention provides a construction site safety protection intelligent monitoring system based on AI image recognition, and relates to the technical field of safety protection. Comprising the following steps: collecting video image data and historical video image data of a construction site in real time; preprocessing the video image data to obtain preprocessed image data; obtaining illegal behaviors and potential safety hazards according to the safety identification and the target track; constructing a risk prediction model based on an improved AHP-TOPSIS algorithm, taking illegal behaviors as core evaluation indexes, dynamically correcting index weights through an entropy weight method, and outputting early warning information and risk diffusion prediction; and according to early warning information and risk diffusion prediction, triggering a layered response mechanism, and optimizing equipment control by adopting deep reinforcement learning. According to the method, the occurrence of accidents is effectively reduced through measures such as constructing a safety protection identification model by using the improved OfficientDet, the safety of personnel and equipment is guaranteed, and the overall construction safety management level is improved.

The present invention discloses a construction site safety monitoring system based on drone vision. The present invention relates to the technical field of construction site safety monitoring and solves the problem of being unable to uniformly identify whether relevant construction workers are wearing safety helmets and whether their safety helmets are worn in compliance with standards. The present invention uses a drone to monitor the construction area of ​​the construction site in real time, and determines the monitoring screen in real time, and then selects a face image from the monitoring screen based on relevant extracted features, and then performs a preliminary analysis on the face image. By locking the center point and the point to be processed, it is identified whether the mean angle of the connecting line within the contour is consistent, and a preliminary judgment can be made as to whether the corresponding construction worker's helmet is worn abnormally, and a relevant judgment signal is generated for timely display. The monitoring method is more comprehensive and can achieve better construction site safety supervision effects.

The invention provides a construction site safety training management and control method and system based on behavior recognition, and belongs to the technical field of construction safety training management and control. According to the invention, training legality is ensured based on dual authentication of keynote speaker biological feature verification and trainee historical record determination; synchronously acquiring voice transcription content with a time label and a head lowering and eye closing illegal behavior monitored in real time through a time sequence binding technology; according to the training text, dynamically generating examination question push examination containing counter example interference; generating an authority decision by adopting a triple verification mechanism of answer correct rate benchmark screening, answer question corresponding time period behavior concentration degree verification and trained state mark comparison; and finally, triggering an access control passing green light lightening instruction or intercepting a red light lightening alarm instruction in a second level, synchronously generating a targeted complementary training task binding an original teaching fragment and a real-time behavior record for forced learning, solving the problems of training assessment failure, cheating loopholes and control lag, and realizing real-time conversion of safety training effectiveness to a high-risk area physical authority to form a closed loop.