2123 results about "Safety Equipment" patented technology

The invention discloses a video analysis-based electric power operation personnel behavior identification and early warning system and method, which realize accurate identification and real-time early warning of electric power operation personnel behaviors by combining a video analysis technology with multi-modal data fusion, and effectively improve the safety management level of an operation site. Compared with a traditional safety supervision mode, the method employs a mode of combining deep learning target detection and time sequence behavior analysis, improves the recognition accuracy of operators and safety equipment, fuses the data of equipment worn by the operators with video data, improves the detection precision, and improves the safety supervision accuracy. And misjudgment caused by illumination change, shielding or complex environment is reduced. Besides, high-risk violation behaviors such as no safety helmet wearing, no safety belt fastening, violation climbing, high-altitude object throwing and the like are accurately recognized through the violation detection module, and different levels of alarm measures are adopted according to the severity of the violation behaviors in combination with an early warning feedback mechanism, so that the pertinence and response efficiency of early warning are improved.

An artificial intelligence based Offensive Cybersecurity Appliance or OCA for launching a cyber-offensive countermeasure aimed at effectively mitigating sophisticated cyber threats such as real-time Ransomware as a Service (RaaS) directives and emergent killware instigated by malicious-threat actors or cyber attackers. The system covertly uses a vast array of penetrative counter cyber attacks, based on an Advanced Persistent Threat or APT model, to circumvent encountered information security controls instituted by security-based components of a cyber attacker's Local Area Network or LAN. Intrinsically, the penetrative counter cyber attacks are constituted of multiple blended artificial intelligence based cyber attacks. The OCA subsequently uses these blended artificial intelligence based cyber attacks to render the hardware (including the cyber attacker's computer system used to instigate the cyber attack, discovered peripheral smart devices, and network-edge device such as a modem-router) interconnected to the cyber attacker's LAN inoperable by destroying the electronic components associated with that hardware.

The invention discloses a network / security equipment security policy generation and evaluation method and system based on a large language model, and the method comprises the steps: carrying out the fine adjustment of a base model through employing a threat research and judgment data set and a policy generation data set, and obtaining a threat research and judgment LLM and a policy generation LLM; a threat research and judgment LLM performs threat research and judgment on the received abnormal log output by the log acquisition module, and outputs a threat information tetrad; the policy generation LLM generates an equipment security policy by calling a knowledge base Agent according to a threat information quadruple output by the threat research and judgment LLM; and the policy generation LLM call evaluation Agent evaluates the equipment security policy from three aspects of policy correctness evaluation, policy validity evaluation and policy suitability evaluation. According to the invention, the effectiveness and reliability of the equipment security policy are improved.

A road safety system with a virtual animal crossing alert system, a portable road safety device, and a virtual road sign display system. The animal crossing alert system uses an AI module on a server to analyze real-time data on wildlife, weather, and traffic to predict animal presence and send alerts to a safety device on a vehicle when the likelihood is high. The portable device may connect to smartphones, physical buttons for user input, a camera interface for capturing road events, and vibration alerts and navigation apps. The portable device enables reporting of road hazards. The virtual road sign system processes data to determine optimal display times and rewards accurate data reporting with fungible tokens issued on a distributed ledger. The system integrates data from various sources, including user reports and distributed ledger NFTs, to provide timely, relevant alerts and information to drivers, improving overall road safety.

A system comprising security devices and a computing device. The security devices may each be configured to detect an event in response to video frames and audio and communicate the video frames, the audio and a notification of the event. The computing device may be configured to execute one or more steps, the steps comprising performing a classification of the notifications of the events into a plurality of event levels and displaying a list of the notifications of the events according to the classification. Each of the plurality of event levels determined to be high priority events are added to a top of the list as the high priority events are detected. Each of the plurality of event levels determined to be a lower priority than the high priority events are added to the list below the high priority events as the events with the lower priority are detected.

The invention relates to a training method and device of a safety equipment detection model, equipment and a storage medium. The method comprises the following steps: acquiring a sample image, inputting the sample image into a to-be-trained safety equipment detection model, performing preliminary feature extraction by a trunk module in the model to obtain initial image features, inputting the initial image features into a plurality of depth separable convolution modules with preset self-attention mechanisms in the model, and obtaining a plurality of depth separable convolution modules with preset self-attention mechanisms; a feature map is obtained through a depth separable convolution module, a corresponding attention weight is obtained through a self-attention mechanism, a weighted feature map is obtained through the information, then the weighted feature map is input into a feature fusion module of a model for self-adaptive fusion, a fused feature map is obtained, and a prediction detection result is obtained according to the fused feature map. And finally, according to the difference between the results, performing model training on the to-be-trained safety equipment detection model to obtain a trained safety equipment detection model. By adopting the method, the detection precision and the detection performance of the model can be improved.

The invention relates to the field of network security, and discloses a problem line determination method based on IP address classification and identification, which comprises the following steps of: collecting public network IP behavior data in a network flow log, extracting multi-dimensional characteristics such as a time regularity entropy value, a protocol diversity proportion, target port dispersity and a request rate change rate, and determining a problem line according to the extracted multi-dimensional characteristics; constructing an IP association graph fusing feature similarity, time synchronism and physical position constraints; an abnormal IP group is identified by adopting a community division algorithm, and the detection sensitivity is adaptively optimized according to a real-time network load and a historical false alarm rate in combination with a dynamic threshold adjustment mechanism based on a PID feedback control model; the attack type is judged through protocol-port mapping and Mahalanobis distance statistics, alarm information containing an abnormal IP list, a time window and a classification result is generated, and the alarm information is linked with safety equipment to execute a defense strategy. According to the method, the defects of a traditional method in the aspects of multi-dimensional attack recognition, dynamic environment adaptation and cooperative attack detection are overcome.

An emulated hardware security device is configured for a compute instance. A state descriptor of the compute instance comprising software identification metadata prepared using the emulated hardware security device is provided to a resource verifier. The metadata identifies a program to be executed at the compute instance. In response to a response received from the resource verifier, a decision is made as to whether to execute the software program at the compute instance.

Systems and methods for security-based device control are disclosed. For example, a presence-based trigger event is detected and one or more suggestion models are utilized to determine whether a security-based device control suggestion should be sent. Multiple suggestions are arbitrated, and a user device is selected for receiving the suggestion based on multiple inputs. The format of the suggestion is also determined and the suggestion is either automatically output on the selected device or a notification of the suggestion is provided.

The invention provides a multi-scale feature learning small target detection method based on an improved RTDETR, and the method comprises the steps: carrying out the improvement of a backbone network Backbone of the RTDETR, and carrying out the preliminary feature extraction through a Conv layer, a MaxPool layer and a PRep-Block module; and the PRep-Block module is used for adding the PConv into the Basic Block, and the RepConv is used for replacing the final convolutional layer of the PConv, so that the PRep-Block is obtained. In the CCFM module, in the feature fusion step, a TFE module is adopted to improve a multi-scale feature fusion mode, and a VoVGSCSP module is added after each feature is fused to optimize channel feature representation. And in the final feature fusion step, performing multi-scale feature fusion by using an SSFF module. Compared with the prior art, the model of the invention further improves the expression ability and robustness of the feature map by enhancing the extraction and fusion ability of the multi-scale features, realizes the lightweight of the model, is suitable for the operation of low-end equipment, and improves the recognition precision of the safety equipment in the chemical industry area, especially the detection precision of a small target.

A hard hat attachment system is described with mounts to support various accessories. The hard hat includes various attachment points configured to receive adapters to couple to various accessories. The user customizes the hard hat for the particular task with the preferred accessories used to complete the job. For example, headlamps, face shields, earmuffs, sun shades, etc. are interchangeably releasably coupled to mounting and / or auxiliary ridges on the hard hat. In this way, the hard hat is customized and / or modified to support a wide variety of accessories that are securely and releasably attached to the hard hat without destructive alterations to the hard hat. Brackets couple to mounts and / or adapters couple to ports to support and / or secure various accessories in place during operation and prevent accidental knocks or jarring the accessory loose.

The invention discloses a model training method and an intelligent construction site safety equipment detection method, and belongs to the technical field of target detection. The method comprises the following steps: firstly, acquiring a sample security equipment image, a sample security equipment annotation image and a YOLOv8 improved model, wherein the YOLOv8 improved model comprises a high-level screening feature fusion pyramid structure, a dynamic sampling module, a RepGhostCSPELAN module and an SEAM attention module; and then, based on the sample security equipment image and the sample security equipment annotation image, training a YOLOv8 improved model to obtain a target security equipment detection model. According to the method, the parameter quantity and the calculation quantity can be reduced, the detection effect of the safety equipment in the intelligent construction site is improved, and the detection real-time performance and high efficiency are ensured.

The invention discloses a network security attack and defense confrontation scene generation system for a novel power system. The scene editor module receives user interaction and constructs or modifies an attack and defense scene; the network simulation module generates real network environment data based on a network space surveying and mapping technology, constructs a simulation network environment and manages simulation data through a simulation database; the AI automatic control module integrates an AI algorithm to simulate an attack behavior, intelligently generates and adjusts a defense strategy, and manages an attack and defense rule through a rule engine; the security isolation module deploys security equipment to realize sensitive data isolation; the data flow direction is controlled through strategy configuration, and information leakage is prevented; the evaluation and feedback module is used for collecting data in the drilling process, carrying out multi-evaluation on the drilling effect and generating reports and suggestions; and the business module adopts a plug-in architecture, dynamically loads or unloads each functional module, constructs a multi-layer defense system, and realizes generation, evaluation and feedback of an intelligent confrontation strategy. According to the invention, the authenticity and effectiveness of attack and defense drilling are improved.

The invention relates to the technical field of digital factories, in particular to a smart factory real-time safety monitoring method and system based on edge computing. Firstly, an edge computing device, a camera and a sensor group are deployed in each production area; then, acquiring image data through a camera; acquiring sensor data through the sensor group; and finally, constructing a safety monitoring model, and operating the safety monitoring model on the edge computing device. Inputting image data and sensor data into the safety monitoring model, and obtaining a safety monitoring result through a data preprocessing layer, a feature analysis layer, a feature fusion layer and a monitoring result prediction layer in sequence; the image data and the sensor data are finely analyzed through the safety equipment wearing feature analysis network and the dangerous behavior feature analysis network, the finally obtained safety monitoring result can accurately reflect the real-time safety state of the smart factory, and efficient and dynamic safety management is ensured.

The invention discloses a method for enhancing a network boundary security policy of a power monitoring system, and belongs to the field of network security. Comprising the following steps: collecting security equipment strategies of different brands in the power monitoring system in combination with network security equipment characteristics of the power monitoring system, and performing intelligent analysis in combination with asset routing conditions; on the basis of asset detection, routing strategy acquisition and protection strategy acquisition, network security strategy calculation is carried out, and unified strategy management of security boundaries is realized; under the condition that the information security requirement is met, automatic issuing of protection and blocking strategies is realized; through centralized management of security policies of heterogeneous equipment, policy configuration data of the network security equipment is captured in an online acquisition mode, and a unified security policy library is formed in a normalization mode, so that unified management, carding reinforcement, carding optimization, topology modeling, path management and policy opening and plugging functions of the security policies of the heterogeneous network security equipment are realized. The method can be widely applied to the field of safety management and operation maintenance of a power monitoring system network.

The embodiment of the invention provides a Transform-based network security alarm noise reduction method, and the method comprises the steps: obtaining alarm data which is generated by a security device in real time; the alarm data are input into a target real alarm recognition model, whether the alarm data belong to real alarms or not is determined through the target real alarm recognition model, and the target real alarm recognition model comprises an input layer, a feature quantification layer, a target model processing layer and an output layer; the feature quantization layer is at least configured to adopt a Transform encoder to encode the dependency relationship and the interaction information obtained from the HTTP request and the response content. According to the characterization processing provided by the embodiment of the invention, the understanding and expression capability of the model on the alarm data is greatly improved, so that the model can more accurately distinguish the real alarm from the false alarm when classifying the alarm, and the false alarm rate is reduced.

The invention discloses traffic safety equipment centralized control middleware and an application method. The method comprises the following steps: determining a road section and / or a congested intersection with vehicle congestion characteristics based on the traffic safety equipment centralized control middleware; identifying a blocked point, and determining the congestion type of the blocked point; obtaining road use conditions of communities around the related road sections and / or roads inside the social public places; the method comprises the following steps: acquiring traffic operation conditions of road sections associated with congested road sections and / or congested intersections, and formulating related dispersion strategies according to arrangement conditions of traffic safety equipment of each road section and / or each intersection and road use conditions of internal roads of communities and / or social public places around the related road sections; and executing the related grooming strategy. According to the invention, intelligence and automation of the traffic system are realized, and the traffic efficiency is improved.

The invention discloses a computer network security data processing method and system based on artificial intelligence, and the method comprises the steps: building a multi-channel deep learning fusion model, extracting spatial local features in a traffic sequence through employing a 1D-CNN one-dimensional convolutional neural network, capturing a long-range time sequence dependence relation between log events, and carrying out the recognition of the long-range time sequence dependence relation between log events; modeling the user operation behavior sequence based on an LSTM (Long Short-Term Memory) network, and fusing the feature weight by using an attention mechanism to obtain a fused feature vector; inputting the fusion feature vector into a classifier established based on an OS-ELM online sequence extreme learning machine to perform real-time threat assessment, and outputting a probability index of network attacks occurring in a short time in the future; and generating a cooperative defense decision according to the network attack probability index, and sending the cooperative defense decision to security equipment for execution. Excessive defense or insufficient protection is avoided, and the cooperation efficiency of safety equipment is remarkably improved.

There is disclosed herein, by way of example, a personal safety device having a main body having disposed therein a non-lethal defensive weapon (such as pepper spray, taser, or baton); a hand enclosure; a light; and a bottom attachment; wherein the main body is constructed with a size and shape to comfortably fit an average human female hand.

There is provided a safety apparatus including a push-up member in which an ejected object is hardly displaced even during transportation, and an aerial vehicle including the safety apparatus. A safety apparatus 100 includes a cylinder 14 that accommodates a piston member 10 and is provided with a bore 13 through which the piston member 10 protrudes to the outside (upward in FIG. 1) during operation, a push-up member 15 that is pushed up in one direction by the piston member 10, an ejected object 16 that is pushed up while being supported by the push-up member 15, a gas generator 17 as a power source that moves the piston member 10 in the cylinder 14, a container 18 that has a bottomed cylindrical shape and accommodates the piston member 10, the cylinder 14, the push-up member 15, the ejected object 16, and the gas generator 17, and a lid 21 that closes an opening end of the container 18. A movement preventing member 27 that prevents the ejected object 16 from moving in a circumferential direction of a bottomed cylindrical portion 19 is provided on an upper surface of a support 20 of the push-up member 15.

A furniture drive includes an adjustment device having a rotatably-supported adjustment element, a transmission mechanism for transmitting a force of the adjustment element, an overload safety device configured to couple the adjustment element with the transmission mechanism upon a rotation of the adjustment element with a torque below a predetermined limit torque. The overload safety device is further configured to decouple the adjustment element from the transmission mechanism upon a rotation of the adjustment element with a torque above the limit torque, preferably such that no torque transmission between the adjustment element and the transmission mechanism takes place. The overload safety device includes a first component having a longitudinal direction and a second component configured to be moved in a direction extending transversely to the longitudinal direction of the first component when the torque is above the limit torque.

A method may include receiving, from a safety system coupled to an infusion system, repair data associated with a repair event, during which a user accesses the infusion system. The method may also include detecting, based on the repair data, the repair event is unauthorized. The method may also include causing, based on the detecting, at least one safety adjustment to operation of the infusion system. Related methods and articles of manufacture, including apparatuses and computer program products, are also disclosed.

The invention discloses a linkage disposal method and system for multi-source heterogeneous security equipment. The method comprises the following steps: acquiring security data of the heterogeneous security equipment, and preprocessing the security data to obtain first security data; processing the first security data through an association rule algorithm to obtain a potential threat event; judging the potential threat event, and if the potential threat event is judged to be a trigger strategy rule, retrieving a corresponding disposal strategy from a strategy knowledge base; and issuing a corresponding instruction based on the processing strategy, and linking corresponding equipment for query. According to the method, security data such as alarm logs of the multi-source heterogeneous security equipment of different equipment can be combined, the association rule is mined by using an association rule algorithm, potential threat events are obtained according to the mined association rule, a linkage disposal strategy is effectively, accurately and automatically generated in real time, finally linkage disposal of the multi-source heterogeneous security equipment is realized, and the safety of the multi-source heterogeneous security equipment is improved. And waste of manpower and material resources is reduced.

The invention discloses a method and a device for determining an operation result of an operation object and a storage medium. The method comprises the following steps: acquiring an initial acquisition image acquired by acquisition equipment; determining a target position of the operation object based on the initial acquisition image; based on the target position, a motion image of the work object is collected, and the motion image is used for indicating motion behaviors executed by the work object in the process of wearing the safety equipment; based on the motion image, an operation result of the operation object is determined, and the operation result is used for indicating that the motion behavior of the operation object is successfully matched with a standard motion behavior corresponding to the safety equipment or is failed in matching; and outputting an operation result. The technical problem that whether the operation of wearing the safety equipment by the operation object is standard or not cannot be determined is solved.

The invention relates to the technical field of network security, and discloses a network security situation awareness and active defense system, which comprises a micro-feature fine granularity extraction algorithm module, a dynamic abnormal behavior pattern library module, a real-time matching and dynamic evaluation engine module and an intelligent defense strategy generation and execution system module. According to the invention, a convolutional neural network and an attention mechanism are integrated through a micro-feature fine granularity extraction algorithm module, and micro-features of network flow data are extracted; the method comprises the following steps: extracting fine features from a dynamic abnormal behavior pattern library, matching the extracted fine features with abnormal behavior pattern features in the dynamic abnormal behavior pattern library, determining whether the fine features are abnormal features, if the fine features are abnormal features, evaluating the threat level of the abnormal features, and triggering a corresponding defense strategy based on the threat level. According to the network security situation awareness and active defense system, through fine analysis, intelligent identification and active defense strategies, micro flow attacks which are difficult to perceive by traditional security equipment are effectively handled, and the overall security protection capability of the network is improved.

The invention discloses a situation awareness and linkage disposal system based on dynamic threat modeling driving, and the system comprises a security data collection and standardization module which is used for collecting security data of a network, a host and an application layer, and carrying out the standardization processing; the dynamic threat modeling module is used for constructing a dynamic threat model; the threat agent modeling and simulation module is used for modeling each node in the threat attack graph into a threat agent; the parameter optimization module is used for globally optimizing parameters and strategy weights of the Monte Carlo tree search network by using a seagull algorithm; the linkage processing decision module is used for configuring a Monte Carlo tree search network based on the optimal parameter group; the safety equipment scheduling module is used for analyzing the optimal linkage disposal decision scheme; and the disposal effect evaluation and feedback module is used for performing multi-dimensional analysis on the disposal feedback data set. According to the invention, accurate situation awareness and linkage processing are provided for the user.

The invention relates to the technical field of safety equipment, in particular to a bird repelling method and a bird repelling device based on directional sound waves and biological warning sound, and the bird repelling method comprises the following steps: recording alarm sound of specific birds, and performing sound analysis on the recorded alarm sound; a sound synthesis technology is utilized to generate a sound signal which is similar to an original alarm sound but is adjusted, the generated sound signal is subjected to amplitude and frequency simulation to obtain a sound with a higher emergency degree, and the adjusted sound signal is sent to a target bird in a directional sound wave mode through a directional wave sounder. Therefore, the purpose of expelling or warning is achieved. The bird repelling device mainly comprises a power management module, a directional wave sounder, an infrared sensor, a wireless communication module, a main control board and the like, and the main control board controls a steering engine to rotate to drive an infrared radar detector to detect surrounding birds. When birds are detected, the main controller can control the left-right steering engine and the pitching steering engine to emit directional sound waves in the direction where the birds are detected to expel the birds.

The invention discloses a power monitoring system network security equipment security boundary strategy unified management system, and belongs to the field of security. Comprising the following steps of: acquiring information of general safety equipment including a firewall, a switch and a VPN (Virtual Private Network); analyzing traffic, strategies, logs and events by using a machine learning method; when the analysis result is displayed, a blocking suggestion is given and can be automatically issued, and a disposal report is generated; unified and centralized management of heterogeneous brands and heterogeneous models of network security equipment including firewalls, routing exchange, load balancing and VPN in the whole network of an enterprise is realized; through visualization of safety management by a user, safety compliance visualization analysis from a safety perspective is realized. Safety equipment strategies of different brands in a power monitoring system are collected, intelligent analysis is carried out in combination with asset routing conditions, and automatic issuing of protection and blocking strategies is achieved. On the basis of asset detection, routing policy acquisition and protection policy acquisition, policy unified management of the security boundary is realized.

A method includes receiving, by a first device, a request from a second device to participate in a session, the request being a message compliant with a WebRTC framework and including an identifier of a process hosted by the second device; verifying, by the first device, a type of the process hosted by the second device based on the identifier; initiating, by the first device, one or more actions on the first device in response to verification of the type of the process, the actions being other than those to communicate data between the first device and second device; and establishing, by the first device, the session with the second device after initialization of the actions on the first device.

The invention provides a network security alarm method and device, electronic equipment and a storage medium, and belongs to the technical field of network security, and the method comprises the steps: obtaining security logs, configuration information and asset importance levels of all security equipment in a network; performing association analysis on the configuration information, and determining attack surface data of each IP point in the network; performing noise reduction and correlation analysis on the security log to obtain processed log information, matching the processed log information with a preset feature library, and filtering alarms of normal service features and alarms of misreported attack features to obtain filtered alarm data; inputting the attack surface data, the asset importance level and the alarm data into a large language model to generate an alarm priority sequence; and executing an automatic response based on the alarm data, the alarm priority sequence and a configured response script. According to the invention, the problem of high false alarm rate of network security alarm in the prior art can be solved.