294 results about "Industrial security" patented technology

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

An industrial security system leverages generative artificial intelligence (AI) to automate the process of identifying software or hardware insecurities on industrial assets, generate recommendations for mitigating these vulnerabilities, and, where appropriate, deploy countermeasures to the vulnerable assets. By leveraging automated asset discovery, real-time asset and network monitoring, and generative AI-assisted vulnerability detection and remediation, the system can reduce the amount of time spent by security administrators in identifying and closing security vulnerabilities within their plant environments, and can alert administrators of potential security issues before those issues become critical.

The invention provides an industrial safety risk monitoring and early warning system and method for an autonomous planning intelligent agent, relates to the technical field of industrial safety production, and solves various limitation problems still existing in an existing safety risk monitoring scheme. In the system, an environment sensing layer is used for collecting multi-source sensing data in an industrial production environment in real time; the edge calculation layer is used for carrying out primary processing on the multi-source sensing data and executing data compression and key information extraction so as to generate key analysis data; the cloud analysis layer is used for performing deep intelligent analysis on the key analysis data, executing security risk assessment and risk diffusion prediction, and dynamically generating and adjusting an early warning decision through an autonomous planning agent; and the action execution layer is used for executing a corresponding safety action instruction according to the early warning decision and feeding back an execution result to the cloud analysis layer to realize system optimization. According to the invention, the risk early warning accuracy and the emergency disposal timeliness can be improved finally.

The invention relates to the field of data processing, in particular to a security agent construction system and method based on a large model. Constructing a low-temperature liquid leakage knowledge base; collecting normal operation condition data, and training the leakage sensing model in an unsupervised mode; configuring a large model with a tool calling interface as a security agent; multi-modal data are synchronously collected in real time, a preliminary abnormal alarm is generated through a perception model, and associated information is retrieved; fusing the multi-modal information by the intelligent agent to carry out leakage prediction analysis and comprehensive research and judgment; and for the confirmed leakage event, a professional tool is automatically called to carry out quantitative consequence simulation, and a scenario disposal scheme is generated. According to the invention, the dependence on scarce leakage samples is effectively overcome, and the detection reliability is improved; through knowledge enhancement and multi-modal fusion, the research and judgment accuracy is greatly improved; full-process automation from early perception, intelligent analysis to consequence severity division is realized, and the intelligent level and emergency response efficiency of industrial safety monitoring are remarkably improved.

The invention provides a multi-modal fusion causal analysis method and platform for industrial safety production, and the method comprises the steps: carrying out the preprocessing of multi-modal key data from an industrial site, and generating a standardized time series data matrix; based on the standardized time sequence data matrix, combining industrial physical rules and operation process semantics to construct an industrial safety causal atlas; based on a self-supervised learning mechanism, optimizing the structure and edge weight of the industrial safety causal atlas by using the standardized time sequence data matrix to obtain an optimized causal reasoning model; and performing path-level risk activation judgment and accident causal traceability by using a real-time standardized time sequence data matrix constructed by the causal reasoning model and real-time inflow multi-modal key data to generate a risk alarm and traceability report. According to the method, the perspectiveness, the accuracy and the interpretability of industrial safety monitoring are remarkably improved.

The invention relates to the field of industrial safety monitoring, provides a real-time risk identification and active safety control method and system for industrial operating personnel in order to improve the accuracy and efficiency of a safety monitoring technology, and realizes real-time acquisition and edge preprocessing of human-machine-environment data by integrating a physiological / environmental / equipment multi-mode sensor. An FPN network is utilized to fuse spatial-temporal characteristics, Temporal-PC causal inference and a GBDT integration algorithm are combined, an interpretable risk propagation model is constructed, risk indexes are calculated, three-level early warning is divided, and hierarchical intervention is executed for different risk levels, including local early warning, environment regulation and control, emergency evacuation and multi-department emergency linkage. Through LSTM trend prediction and reinforcement learning dynamic parameter adjustment, closed-loop optimization of an intervention strategy is realized, the problems of information splitting, response lag and the like of a traditional monitoring system are solved, the risk identification precision and emergency disposal efficiency of a chemical high-risk scene are remarkably improved, and the method is suitable for operation safety control in high-temperature, high-pressure, toxic and harmful environments.

The invention discloses a vision-based industrial safety production full-cycle monitoring method and system, and belongs to the technical field of industrial safety, and the method comprises the steps: collecting the visual state data and interaction information of a production scene entity in real time, and generating an initial risk conduction link in combination with a pre-constructed working condition rhythm safety risk topological map; based on the initial risk conduction link, adopting a hidden Markov algorithm, a forward diffusion model and a Bayesian model to respectively obtain forward and reverse risk path sequences; inputting the forward and reverse risk path sequences into a risk monitoring map, and outputting risk probability, type and path characteristics through analogue simulation and risk threshold judgment; positioning a core risk node according to the path characteristics, and calling a matched initial prevention and control strategy; generating a secondary prevention and control strategy through an analogue simulation strategy effect, and carrying out iterative optimization until the risk probability is reduced to a safety threshold value; according to the invention, dynamic perception and accurate traceability of industrial production risks are realized, and the real-time performance and reliability of safety production monitoring are improved.

The invention discloses a fuel gas safety linkage control method and system based on a multistage alarm threshold value, and relates to the technical field of industrial safety control, and the method comprises the following steps: obtaining the operation data of a to-be-detected target, forming an operation feature vector, and carrying out the dynamic correction of a standard explosion lower limit concentration threshold value based on the operation feature vector; comparing the real-time gas concentration with the corrected concentration threshold value, and generating an initial grading judgment result in combination with the concentration change rate and the duration parameter; trend prediction is carried out according to time sequence data in the operation feature vector, and an initial grading judgment result is corrected; performing spatial fusion on the corrected grading judgment result, performing cross validation in combination with the running state and the credibility of the monitoring unit, and generating a final risk grade; according to the final risk level, a progressive linkage control action is triggered; according to the invention, through multi-level threshold value dynamic correction and trend prediction, the problems of missed report, false report and response lag in gas safety monitoring are solved.

The invention relates to the technical field of alarms, and discloses an infrastructure catastrophe sound-light alarm system with a fault self-checking capability, and the system comprises a data collection module which is configured to synchronously collect a sound signal, an optical signal, an electrical parameter and an environmental parameter of an alarm; the data preprocessing module is configured to perform noise suppression, normalization and abnormal value elimination on the acquired data; the feature extraction module is configured to extract a sound time-frequency domain feature, an optical signal fluctuation feature, a circuit stability feature and an environment correlation feature; the fault diagnosis module is configured to perform fault type judgment by adopting a multi-modal fusion model and a predefined rule base; and the grading processing module is configured to execute emergency response, maintain early warning or self-adaptive adjustment strategies according to the fault grades. According to the audible and visual alarm fault self-checking system, multi-modal data fusion, self-calibration, mobile detection and graded response are integrated, and the reliability of industrial safety early warning is improved through multi-dimensional data collaborative analysis and dynamic adjustment.

The invention provides an encryption access control method and platform for industrial security data, and relates to the technical field of data encryption access, and the method comprises the steps: carrying out the attribute classification and code identification of an industrial system data set; performing security level division on the industrial system data set according to the data security quantitative index system; performing matching analysis on the multi-level industrial security data set based on the data encryption algorithm list; encrypting and storing the industrial system data set based on the industrial data identification code set by adopting a multi-stage data encryption algorithm to generate an industrial encrypted data warehouse; and establishing a fine-grained access mechanism and a decryption interaction mechanism according to the user role library, and performing access authority distribution and encryption access control on the industrial encrypted data warehouse based on the fine-grained access mechanism and the decryption interaction mechanism. According to the invention, the technical problem that the comprehensive requirements of high safety, high precision and high flexibility in a modern industrial system are difficult to meet in the prior art can be solved.

The invention discloses a classification and grading alarm system based on multi-source data, relates to the technical field of industrial safety and workplace monitoring, and can solve the typical problems that in the prior art, alarm logic is rigid and single, data sources are static and isolated, an intelligent alarm verification mechanism is lacked, and intelligent analysis and management processes are separated. Through multi-source sensing equipment, an edge-cloud cooperative computing architecture, a multi-source event triggering engine, an innovative classification and grading alarm engine and an AI large model rechecking mechanism, event automatic handling measure execution and alarm information distribution are realized through an automatic linkage and notification subsystem; and finally, event closed-loop is completed by the event handling and closed-loop platform, so that efficient, accurate and automatic monitoring, alarming and closed-loop management of industrial and various working place security events are realized.

The invention discloses an industrial internet risk analysis system and method based on a knowledge graph, and the method comprises the steps: collecting data, and defining a multi-source original data flow and the knowledge graph; performing data preprocessing, unifying sample data, and performing mapping splicing on each mode in the multi-source original data stream; constructing, storing and updating a knowledge graph; carrying out risk modeling based on attention, and introducing an attention mechanism and a causal relationship model; carrying out risk scoring and inference; and performing risk early warning and visualization, outputting an early warning list, a propagation path diagram and key hub nodes, and performing butt joint with an existing industrial safety platform for display through an analysis report interface. The invention provides an industrial internet risk analysis scheme which not only can fuse the structural modeling capability of the knowledge graph, but also can utilize a self-attention mechanism to improve the key risk feature recognition precision.

The invention discloses an agent-based security risk assessment and operation management integrated method and system, and relates to the technical field of industrial security production and artificial intelligence, and the method comprises the steps: obtaining enterprise security production operation data, carrying out the preprocessing of the data, obtaining a standardized data set, and carrying out the deep transfer learning through an agent, and extracting scene risk features; calculating a propagation entropy value of the risk mapping data and constructing a propagation path, and performing dynamic time sequence fusion to generate a risk assessment result; generating a scene control instruction based on group game operation and performing optimization to obtain a baseline check instruction; operating data are collected, the conduction probability is calculated, a safety response rule is generated, linkage optimization is carried out, and a cooperative defense strategy is formed. According to the invention, risk feature extraction, propagation path analysis and cooperative prevention and control are realized through the intelligent agent, and the intelligent management level of enterprise safety production is improved.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port key operation parameter online monitoring and explosion safety protection system which comprises a parameter real-time monitoring module, a threshold activation judgment module, a digital twin modeling module, a risk dynamic prediction module, a protection decision analysis module and an intervention scheme execution module. By constructing a multi-source signal cooperative verification mechanism, when short-circuit parameters are monitored in real time, a self-adaptive digital filtering algorithm is adopted to dynamically match magnetic field intensity and temperature disturbance characteristics, noise stripping and baseline calibration are carried out on original voltage and current signals, the monitoring accuracy of core parameters such as short-circuit resistance is ensured, false alarm triggering is avoided, and real-time monitoring of the short-circuit parameters is realized. And meanwhile, by establishing a parameter credibility scoring model, when signal distortion exceeds a tolerance threshold, a standby sensing channel is automatically switched, so that the risk of production interruption caused by noise pollution is further reduced, and the anti-interference capability and the operation stability of the system are improved.

The invention relates to the technical field of industrial safety monitoring, in particular to a fallen object accurate identification FOD calculation method based on a fixed tower and application of the fallen object accurate identification FOD calculation method. Comprising the following steps: synchronously acquiring multi-source heterogeneous sensing data; carrying out multi-modal data preprocessing and feature extraction; performing cross-modal feature fusion and representation construction; modeling and reasoning a time-space diagram neural network; and performing multi-dimensional threat quantification and decision output. According to the design of the invention, multi-modal sensor groups are deployed at the tower bottom, the tower middle and the tower top of a fixed tower in a layered manner, and a high-definition multispectral camera, a high-density laser radar and a distributed acoustic sensor are integrated, so that 7 * 24-hour full-automatic data acquisition is realized; according to the design, the time and environment limitation of manual inspection is eliminated, and the complex weather detection capability is improved by 80%; meanwhile, the sensor synchronously triggers the mechanism and the grading detection process, the single-frame data processing delay is less than or equal to 50ms, and the real-time monitoring requirement of an aircraft take-off and landing high-frequency scene is met.

The invention discloses an industrial safety detection method combining time alignment and motion consistency, and belongs to the technical field of industrial safety monitoring. According to the method, through seven steps of multi-camera data acquisition and preprocessing, target feature extraction, coordinate projection, instantaneous speed estimation, multi-dimensional similarity fusion, soft matching optimization and model training and target trajectory generation, the problem of space-time inconsistency during multi-camera cooperative detection in an industrial scene is solved. A camera clock offset parameter is innovatively introduced to correct a cross-camera time difference, appearance, geometry and time prior multi-dimensional similarity are fused, a multi-loss function optimization model is constructed, and cross-camera target accurate matching and long-time trajectory tracking are realized. High-precision technical support is provided for personnel safety monitoring and equipment abnormal behavior detection in an industrial scene, and the industrial production safety protection capability is improved.

The invention belongs to the technical field of industrial safety monitoring and emergency response, and discloses a dynamic response system and method for risk real-time assessment and early warning in a dangerous environment, and the system collects gas, personnel and environment data in real time through a multi-source sensor, builds a "instant-accumulation-prediction" three-dimensional risk model, and carries out the real-time assessment and early warning of the risk. And the safety threshold is dynamically adjusted according to the environmental parameters. Based on a multi-dimensional risk assessment result, the system automatically determines an early warning level, and synchronously triggers personnel terminal early warning information pushing and linkage control of ventilation, isolation doors and other devices. A reinforcement learning mechanism is innovatively introduced, the system can continuously optimize key parameters based on historical early warning event data, and a complete closed loop from risk perception, intelligent decision and linkage execution to self-optimization is realized. The system effectively solves the problems of single risk assessment, fixed threshold, response lag, lack of adaptive ability and the like in a traditional method, and significantly improves the accuracy, real-time performance and overall safety of dangerous environment risk management.

The invention relates to an industrial safety monitoring system for dynamic counting of a window sash production line, which belongs to the technical field of industrial safety monitoring systems and machine vision and comprises a data acquisition module for acquiring a continuous image frame sequence from an image sensor on the production line and providing an original data source for subsequent feature extraction; the feature extraction module is composed of a preset convolutional neural network and used for extracting feature vectors describing the state of the window sash from the image frame sequence so as to achieve digital representation of the physical state; the state evaluation module is used for receiving the feature vector sequence and calculating a counting accuracy index based on an exponentially weighted moving average method; quantifying the stability of the model output confidence to generate a response deterministic indicator; and the data distribution difference is measured by calculating the KL divergence sum of feature component edge distribution, so that a model trust entropy index is established, and a quantifiable judgment basis about the input data quality is provided for a system.

The invention relates to a gas cylinder filling method, an early warning method, an assessment method, a system and a medium in the technical field of industrial safety production. The gas cylinder filling safety assessment method based on voice analysis comprises the following steps: acquiring acoustic data of an operator; and analyzing the acoustic data and calculating to obtain a knowledge proficiency score Spro. And acoustic data are extracted, and a psychological quality score Semo is obtained through calculation. Collecting motion posture data of the gas cylinder and calculating to obtain an operation stability score Ssta; and carrying out weighted fusion on the Stro, the Semo and the Ssta to obtain an assessment score Stotal. And setting a score line based on the assessment requirement, and if the Stotal is greater than or equal to the score line, determining that the operator passes the assessment. According to the invention, through innovative multi-mode assessment, the ability of the operator is changed from feeling-based ability to data-based ability, the final result can be known, and the specific weak link can be known according to the specific condition of each link, so that the psychological quality and emergency response ability of the operator can be effectively assessed.

The invention particularly relates to a password security management method applied to industrial security, which relates to the technical field of industrial control system security, and comprises the following steps of: deploying password strategies in batches in an arranged controlled simulation environment, triggering test case execution according to priorities, and monitoring system response data to obtain a test result; wherein in the test case execution process, whether the obtained execution success rate is correct or not needs to be judged through the verification error coefficient obtained through analysis. According to the invention, an isolation simulation test platform highly consistent with a real production environment is established, an HMI / operator station, a PLC / DCS controller and an industrial network structure are accurately duplicated in combination with a digital twinning technology, and a test process and the production environment are completely isolated; serious consequences, such as automatic process interruption and production line stop, possibly caused by password strategy change testing are avoided from the source.

The invention provides a wide dynamic range methane detection system based on synchronous accumulation and system reconstruction. The device is characterized by comprising a modulated laser (1), a gas absorption cell (2), a photoelectric detector (APD) (3), a variable gain trans-impedance amplifier (TIA) (4) based on a digital potentiometer, a dual-threshold voltage comparator circuit (5) and a microcontroller processing unit (6). The device can be used for large-dynamic-range real-time monitoring of the methane gas concentration, and the problem that a traditional TDLAS system is prone to saturation or limited in detection limit when the concentration span is large is effectively solved. The method can be widely applied to the fields of gas concentration detection, industrial safety control and the like.

The invention relates to the technical field of industrial safety management and control, and discloses an integrated digital twinning boiler limited space safety operation management and control system and method, and the system comprises a twinning environment building and sensing module which is used for building a three-dimensional digital twinning model, and collecting environment, personnel position and physiological signals in real time; the core calculation and analysis module is used for calculating an individual risk correction coefficient based on the physiological signal and calculating a dynamic risk potential field in a twinborn model based on the environmental data; the risk assessment and decision module is used for coupling the risk potential energy value at the personnel position and the individual risk correction coefficient to generate a comprehensive risk index; and the collaborative intervention and presentation module is used for generating and executing an active intervention instruction based on the comprehensive risk index. According to the invention, by constructing a human-loop coupled dynamic risk potential field, quantitative evaluation and active intervention of individual and system level risks are realized, and operation safety management and control from passive alarm to active prevention and from static management to dynamic closed loop are realized.

The invention relates to the technical field of industrial safety control, in particular to a dual-system redundancy safety circuit and a self-diagnosis method, the dual-system redundancy safety circuit is composed of a first safety system, a second safety system, a test load and a switching unit, and the first safety system and the second safety system realize information interaction through an optical coupling isolation communication interface. According to the self-diagnosis method, an encrypted diagnosis trigger pulse sequence is generated through the second safety system, a test contactor of the first safety system is driven to be connected to a test load, and a main safety loop generates simulated real safety action current; the second safety system performs high-speed sampling, filtering, correlation analysis and response delay extraction on the current waveform to obtain a diagnosis result of the contact performance and the load capacity; and when an abnormity is detected, the second safety system controls the switching unit to execute safety switching through the solid-state switching output point, so that control right taking-over is realized, and the reliability and the online diagnosis capability of the safety loop are improved.

The invention discloses a multi-person collaborative human factor risk monitoring method and device based on multi-modal perception, and relates to the technical field of human factor engineering, intelligent perception and group decision behavior modeling. The cognitive state, emotion change and physiological load of participants are sensed in real time by fusing EEG, motion state, positioning track, heart rate, blood pressure and other multi-source information, and the collaborative decision behavior and cognitive consistency of a group in a high-pressure task are evaluated through interactive modeling. The method is suitable for multi-personnel, multi-role and task-intensive cooperation scenes such as emergency management, military drilling, industrial safety, complex traffic guidance and the like.

A cloud-based industrial safety system executes safety applications and interfaces with industrial assets on the plant floor using software encoded processing (SEP). The use of SEP ensures safety reliable execution of safety applications and data communication software by redundantly executing native code, thereby implementing a level of software-based fault detection that is independent of the hardware on which the safety application operates. This allows the cloud-based industrial safety applications to achieve at least SIL3 safety ratings for safety services even though the safety applications are executed using standard COTS hardware that is commonplace in the server market. The use of SEP to reliably execute safety software on the cloud can make possible a wide variety of safety applications that would be difficult to implement using purely localized industrial safety systems.

The invention relates to the technical field of information technology and industrial safety management, and discloses a hazardous waste whole-process risk prevention and control method and system. The method comprises the following steps: acquiring a real-time monitoring data set of each link of the hazardous waste management chain; calculating the fluctuation amplitude of the environment change indexes and analyzing the distribution characteristics of the operation deviation marks; when the two meet a preset condition, judging the initial signal intensity of risk upgrading by utilizing a classification model; screening high-intensity signals and inputting the high-intensity signals into the regression model to obtain a risk transformation trend; matching the risk conversion trend with a historical risk mode to generate a dynamic monitoring adjustment parameter; if the parameter indicates that the monitoring sensitivity is insufficient, fusing additional sensor data to generate an enhanced monitoring data set; and tracking a risk propagation path based on the enhanced data set, and predicting an occurrence position and a time point of a risk peak value. According to the method, the real-time performance, the accuracy and the response speed of hazardous waste management are improved, and the effect of risk prediction and management is remarkably enhanced.

The invention discloses an industrial safety cross-domain collaborative analysis system based on time series data and visual data, and relates to the technical field of cross-domain data collaborative processing. Comprising a collaborative data acquisition and processing module used for acquiring multi-source monitoring data in real time, extracting a hot area characteristic parameter set and carrying out data preprocessing; the infrared thermal image time sequence response extraction module is used for quantifying a thermal response change rate and carrying out signal smoothing; the cross-domain time difference compensation alignment module is used for carrying out trend term elimination and offset scanning on the multi-source monitoring data, carrying out correlation quantification on the multi-source monitoring data and a thermal response change rate, obtaining the most accurate offset and realizing multi-modal signal time alignment; the cross-domain collaborative mutation analysis module is used for quantifying the mutation synchronization intensity of the multi-modal signal and identifying potential anomalies; and the leakage judgment grading feedback module is used for carrying out grading judgment and implementing response measures. The problem that the leakage early-stage characteristics are difficult to identify synchronously due to the time difference of the multi-mode response in the hazardous chemical substance pipeline and the storage tank area is solved.

The invention discloses an infrared thermal imaging data transmission and remote monitoring system based on wireless communication. The system comprises an unmanned aerial vehicle autonomous cruise module for realizing preset path planning and dynamic obstacle avoidance; the infrared thermal imaging detection module is used for detecting VOCs leakage; the wireless communication and data transmission module supports real-time return of high-definition infrared images and gas data based on multi-mode communication; the remote monitoring and intelligent analysis platform deploys an AI gas recognition algorithm at the cloud, automatically marks the leakage position and estimates the diffusion range. According to the infrared thermal imaging data transmission and remote monitoring system based on wireless communication, through autonomous cruise of the unmanned aerial vehicle, the high-sensitivity infrared detector, real-time wireless transmission and cloud intelligent analysis, full-automatic, large-range and high-precision VOCs leakage monitoring is achieved, and industrial safety and environmental protection supervision efficiency are remarkably improved.

The invention discloses an industrial safety monitoring method and device based on target recognition, equipment and a storage medium, relates to the technical field of safety monitoring, and discloses an industrial safety monitoring method based on target recognition, which comprises the following steps: extracting a frame-by-frame image based on a real-time monitoring video stream, and preprocessing the frame-by-frame image to obtain a preprocessed image; performing target identification on the preprocessed image to obtain coordinate information and confidence information of an initial detection frame set; performing dual-threshold non-maximum suppression processing on the initial detection frame set based on the coordinate information and the confidence information to obtain an optimized detection frame set; and obtaining a labeled video frame and an alarm signal based on the optimized detection frame set so as to monitor industrial safety. And the target identification precision in the security monitoring process in a complex industrial scene is improved.

The invention discloses an intelligent industrial safety production supervision platform based on intention recognition and a large field model. The intelligent industrial safety production supervision platform comprises a multi-source multi-modal data fusion module, a multi-modal video recognition module, an intelligent algorithm arrangement module, an intention recognition module, a large field model integration module and an emergency response module. The intention recognition technology is combined with an industrial field large model, comprehensive multi-modal data fusion and intelligent decision support are achieved in industrial safety production for the first time, and the method is not only suitable for high-risk industries such as electric power and petrochemical engineering, but also can be applied to safety production management in the fields such as machine manufacturing and food processing; besides, the system platform adopts a modular design and a low-code development environment, a user can flexibly configure a monitoring algorithm and an emergency scheme according to own requirements, and the system is suitable for various industrial environments and production scenes.