12 results about "Safety behaviour" patented technology

The invention discloses a high plateau airport operator unsafe behavior monitoring and early warning method and device, and relates to the technical field of plateau operation monitoring. According to the method, algorithm such as YOLOv8 and feature fusion are combined to collect physical and behavior data from multiple sources and extract features, then a bimodal model is constructed by SVM and logistic regression, and a comprehensive fatigue level is generated by matching with an evidence theory, so that the limitation of single-modal data is avoided; meanwhile, a fuzzy Bayesian dynamic fusion model is introduced, and a fatigue score is corrected in combination with specific environmental data (low pressure, low temperature and the like) of the high plateau, so that risk judgment is more adaptive to physiological-environmental collaborative risk characteristics of the high plateau scene; and finally, the graded early warning is triggered based on the comprehensive risk grade of fatigue, environment and space dimensions, so that the accuracy of risk identification is realized, the timeliness of high-plateau operation safety management is improved through differentiated intervention, and the potential safety hazard of high-plateau operation is effectively reduced.

The utility model provides limited space networking equipment based on unsafe behaviors of a power plant. The limited space networking equipment comprises a mounting platform, a shell, a first sealing part and a second sealing part, the mounting platform is a platform with a certain thickness; the shell is a hemispherical transparent shell, and the shell and the end face of the installation platform are arranged in a separable mode. The first sealing part is arranged on the end surface of the mounting platform and is arranged along the side line of the end surface; the second sealing part is arranged at the edge of the shell; wherein the inner wall of the shell and the end surface of the mounting platform form an accommodating space, and the accommodating space is used for accommodating monitoring equipment; and the first sealing part is matched with the second sealing part, so that the accommodating space is in a sealed state isolated from the outside. According to the application, the shell is arranged on the mounting platform, the sealed accommodating space is formed between the shell and the mounting platform through the sealed matching of the shell and the mounting platform, particularly through the tight fitting of the first sealing part and the second sealing part, and the monitoring equipment is mounted in the accommodating space for moisture-proof and waterproof sealed protection.

This invention discloses a network security abnormal behavior early warning management method in the field of network security technology, including the following steps: S1: constructing a security behavior architecture diagram of the target network; S2: inputting security monitoring behaviors into the security behavior architecture diagram and analyzing the target security behaviors; S3: setting an early warning evaluation interval; S4: judging abnormal monitoring behaviors and analyzing the behavior early warning methods of the abnormal monitoring behaviors; S5: analyzing the architecture layer anomaly coefficient of the behavior architecture layer and analyzing the abnormal behavior architecture layer and its architecture layer early warning method; S6: analyzing the network anomaly coefficient of the target network and analyzing the network early warning method of the target network; and performing abnormal behavior early warning of the target network based on the behavior early warning method, architecture layer early warning method, and network early warning method. This invention improves the comprehensiveness and accuracy of target network abnormal behavior early warning management.

The application discloses a workshop safety production visual early warning method and system based on digital twinning, comprising: dividing workshop system entities into several workshop subsystems, determining production factors in each workshop subsystem, and constructing a workshop digital twinning model from the geometric dimension, behavior dimension and rule dimension; selecting a corresponding data acquisition form to obtain workshop safety production data, classifying and storing the workshop safety production data; and based on the real-time mapping relationship of the data, completing the binding between the workshop safety production data and the workshop digital twinning model; constructing a target detection model to monitor whether unsafe behavior exists in the operating personnel in real time, and issuing a warning information if unsafe behavior exists. Through the target detection technology, the safety early warning is carried out, the digital twinning technology is combined to accurately and timely monitor and manage the safety problems in the complex production process, a safer and more reliable production environment is constructed, the efficiency of safety production management is effectively improved, and the risk of accidents is reduced.

The invention relates to a power construction safety behavior intelligent monitoring and early warning method based on artificial intelligence. The method comprises the following steps: acquiring a scene data snapshot of a construction site; the scene data snapshot is mapped to a preset construction site three-dimensional model, and a dynamic digital twinning scene is obtained; traversing each independent risk source in the dynamic digital twinborn scene, and calculating an influence area of the independent risk source to obtain an independent risk field; performing risk superposition mapping based on the independent risk field to obtain a risk level thermodynamic diagram; performing risk detection based on the risk level thermodynamic diagram and the dynamic digital twinborn scene to obtain a risk behavior; and generating an early warning instruction set based on the risk behavior. By adopting the method, the multi-risk coupling effect can be identified, and when the risks of different working planes are superposed, accurate risk level assessment and early warning decision can be made.

The invention relates to the field of monitoring and early warning, in particular to an unsafe behavior monitoring and early warning method and device based on deep learning. The system comprises a wearable monitoring device, a fluid impedance tomography monitoring module and a fluid-structure interaction wave equation inverse solution model. The wearable monitoring device integrates a low-pressure pneumatic flexible network, a magneto-rheological fluid layer and a negative Poisson's ratio auxetic skeleton, collects a pressure pulsation frequency spectrum and electromagnetic parameters in real time, and inverts a normalized stiffness index of a human body contact part by using deep learning; the method is characterized in that magnetic field PWM and air pressure frequency are regulated and controlled in a linkage mode according to a rigidity judgment result, and air pressure pretightening force is used for inducing phase change of magnetorheological fluid and generating high-frequency vibration early warning; according to the invention, the geometric antagonism mechanism of the auxetic skeleton is utilized to ensure the tight fitting of the device and the high fidelity of signals, so that accurate tactile feedback monitoring is realized.

This invention provides an intelligent identification and early warning system for production line personnel safety, comprising: an environmental and personnel feature extraction module for constructing a digital environment model of the work site and extracting key personnel features; a multimodal fusion perception and diagnosis module for receiving the key personnel features and fusing multi-source sensor data to identify and diagnose unsafe behaviors or states of personnel; a situation prediction and assessment module for constructing a personnel safety status deterioration trend model and a comprehensive evaluation model; a data fusion and processing hub module for performing unified data standardization fusion and optimization processing; and an intelligent linkage and decision optimization module for generating and executing optimal safety early warning and linkage control strategies based on the fused and optimized information. This invention can form a complete closed loop from perception, diagnosis, prediction to linkage, significantly improving the intelligence level and response efficiency of production line personnel safety management.

The present application relates to a kind of unsafe behavior intelligent identification method based on YOLO, belong to image recognition field.The present application is for the input scene picture, using YOLOv7 to carry out target detection to picture, output the position information of personnel contained in scene, whether wear safety helmet, whether wear work clothes;Using high-low lens algorithm judges input scene picture belongs to high lens or low lens;According to the input scene picture, select the dangerous area discrimination decision tree model under the corresponding scene;Decision tree model discriminates according to the input 5 parameters whether the human body corresponding to this data is in dangerous area;The personnel position, whether wear work clothes, whether wear safety helmet and whether in dangerous area in the result of step S1, S4 are integrated, and the unsafe behavior grade of personnel in scene is output according to the requirement of label.The algorithm scheme proposed in the present application can quickly and accurately identify the unsafe behavior grade of personnel in video monitoring scene.

The application discloses a research method for the influence of a safe atmosphere on an unsafe behavior supervision decision-making process in a military-civil airport construction, and comprises the following steps: S1, model assumption: determining a game group, a strategy set and a safe and stable strategy; S2, establishing a behavior game value perception matrix; S3, using a replication dynamic equation to perform numerical solution on the model and performing evolution equilibrium point analysis to obtain conditions required to be met by the model stability; and S4, parameter simulation and analysis.The application uses the prospect theory and the psychological account theory to establish the value perception matrix, determines the non-rational and risk preference factors of the safety behavior decision-making of the supervision and construction personnel, simulates the safety behavior decision-making evolution process of the construction related persons through the establishment of an evolution game model conforming to the safety management characteristics of the airport non-stop construction, and according to the simulation experiment result, the unsafe behavior in the construction process can be effectively controlled through the single factor control of the safety supervision.

The invention provides an unsafe behavior identification method and device based on cross-modal alignment, and a medium. The method comprises the following steps: respectively obtaining inertial measurement unit data and human skeleton key point data of a scaffolding worker; performing cross-modal alignment training on an inertial measurement unit encoder and a human skeleton key point encoder by using the inertial measurement unit data and the human skeleton key point data, so that the inertial measurement unit encoder and the human skeleton key point encoder can align semantic information of heterogeneous modals in the same feature space; only either an inertial measurement unit encoder or a human skeleton key point encoder is used to analyze the input single modal data to identify the unsafe behavior of the worker. Through the innovative design of the cross-modal alignment training framework and the high-performance encoder, the robustness, flexibility and economy of the system in actual deployment are remarkably enhanced while the recognition precision of the unsafe behaviors of the scaffolding workers is improved.

The invention belongs to the technical field of unsafe behavior detection, and particularly discloses an unsafe behavior detection method applied to a complex assembly scene. The method comprises the following steps: determining unsafe factors in hoisting and firework scenes, and collecting a hoisting and firework data set in a complex assembly environment scene; optimizing the structure and the loss function of the YOLOv7 model to obtain an improved YOLOv7 model; training the improved YOLOv7 model by using the data set to obtain a detection model; and deploying the detection model to carry out detection and alarm prompting on unsafe behaviors. According to the scheme, the technical problem that traditional unsafe behavior management and control judgment is inaccurate and incomplete is solved.

This invention discloses a method and system for automatic identification and assessment of construction risks for workers operating on high-altitude scaffolding, belonging to the field of construction risk identification technology. The method includes the following steps: multi-category target detection, using a target detection model to detect and locate workers, personal protective equipment, and scaffolding structures; generating scaffolding segmentation masks using an image segmentation network; detecting key points of the human skeleton using a human pose estimation model; high-risk action classification, designing a dual-path action classification strategy, including deep learning-based skeleton image classification and pose parameter classification based on artificial features, combining key points of the human skeleton to determine the action category, and checking whether the key points of the human skeleton overlap with the scaffolding area to correct the action classification results; risk index calculation and comprehensive assessment. This invention achieves automatic detection, identification, and risk quantification assessment of unsafe behaviors of personnel in complex construction sites, providing intelligent auxiliary means for construction safety supervision.