50results about How to "Improve safety management efficiency" patented technology

The invention discloses a dual-prevention intelligent interaction system based on a large language model, and belongs to the technical field of artificial intelligence. The system comprises a big language base model, a knowledge base management module, an agent module, a multi-modal big model and an evaluation module, and the big language base model understands user intentions and processes natural language tasks and provides token processing capacity for other modules; the knowledge base management module is used for classified storage and management of professional knowledge, establishing indexes according to semantics and finally performing localized storage; the intelligent agent module is used for analyzing input of a user, and different workflows are packaged in different intelligent agents so as to provide personalized services for the user; the multi-modal large model module receives voice input by a user, converts the voice into characters, transmits description of information to the Agent, generates answers by analyzing semantics, converts the characters into the voice and sends the voice to the user; the evaluation module establishes an evaluation index system from the four aspects of accuracy, answer correlation, context correlation and context recall rate.

The invention discloses a park security all-weather monitoring system and monitoring method based on a digital twinborn model, and relates to the technical field of security monitoring, the system obtains and stores video streams and environmental data in real time through a data acquisition module, and provides a basis for subsequent analysis. The central processing module carries out preprocessing and feature extraction on the data to generate a video image feature set and an environment feature set, and the digital twin monitoring module visualizes environment features through a three-dimensional model, so that visual display of dynamic changes of the chemical workshop is realized. And the video quality analysis module comprehensively calculates a comprehensive monitoring performance index Ezh by using the video quality index Q (t) and the monitoring regulation and control index C (t) so as to ensure the definition and the stability of video monitoring. And the comprehensive evaluation and early warning module combines the abnormal detection value A (t) with the comprehensive monitoring performance index Ezh, calculates a comprehensive safety risk index O (t) and generates early warning information to improve the safety management level of the park.

The invention relates to the technical field of silt dam monitoring, and discloses a silt dam monitoring information sensing integration method and system, and the method comprises the steps: collecting the monitoring data of a silt dam, carrying out the short-time risk prediction at an edge node according to the monitoring data, triggering local early warning based on the short-time risk prediction, and starting a response mechanism; a self-adaptive cooperation mechanism is established between edge nodes, and sensor abnormity and local disaster chains are identified through exchange and comparison of monitoring data between the nodes; and the cloud integrates the monitoring data uploaded by the edge nodes, generates a global risk prediction model through dynamic data fitting, and performs hierarchical emergency response according to a risk prediction result. According to the method, the safety management efficiency of the silt dam is improved, and a scientific support and an intelligent decision foundation are provided for flood prevention and fighting.

The invention relates to the technical field of identity verification, in particular to an information security data defense method and system based on a block chain, and the method comprises the following steps: deploying a decentralized identity storage library through a block chain technology, and storing user identity information and a verification key in the library; according to the method, the security of identity verification and the integrity of data are improved by combining the block chain technology and the decentralized identity storage library. The user identity information and the verification key stored on the block chain benefit from tampering resistance and transparency of the block chain, and the risk that the data is illegally modified or leaked is reduced. In addition, by generating an independent verification state identifier for each user request, the security of the verification process is enhanced, and the response speed to security threats is improved. In addition, dynamic permission adjustment and threat analysis based on real-time monitoring are also realized, and the change of a security environment can be quickly adapted and responded, so that the overall security management efficiency is improved.

The invention, which relates to the technical field of building construction monitoring, discloses a tall template construction safety monitoring system comprising a template data acquisition module, a dynamic load analysis module, an interpolation module, a time window analysis module, an adjacent region comprehensive analysis module and an early warning module. The template data acquisition module collects stress data of a basic area in real time through a sensor network, the dynamic load analysis module dynamically adjusts division of the stress area according to the stress data, the interpolation module performs smoothing processing on the adjusted data, the time window analysis module calculates area stress fluctuation, and the time window analysis module analyzes the area stress fluctuation. The adjacent region comprehensive analysis module evaluates the stress influence of adjacent regions, the early warning module generates risk levels through fuzzy logic, and the construction report generation module is used for recording and generating a safety report; according to the invention, a local dangerous area can be better controlled and accurately positioned, and large-range blind troubleshooting is avoided, so that the safety management efficiency of a construction site is improved.

The invention relates to the technical field of information technology and artificial intelligence application, and discloses a campus anti-bullying system based on voice recognition and big data analysis. Comprising a front-end device, a central processing unit, a voice communication real-time talkback module, a prevention reminding module, a disposal reminding module and a user report interface, wherein the front-end device further comprises an AI voice recognition terminal device, a microphone and a sensor module and is used for monitoring environment sound in a monitoring blind area in a campus in real time. By integrating AI voice recognition, a deep learning algorithm and a voiceprint recognition technology, voice features related to campus bullying events can be accurately captured and recognized in a monitoring blind area, and by combining an Ai artificial intelligence large model analysis platform, the system can effectively analyze the grade of the bullying events, intelligently trigger event prevention or event disposal notification pushing, and improve the safety of the bullying events. And the historical event behaviors are subjected to data archiving, so that the accuracy of bullying event identification is greatly improved.

The invention relates to the technical field of line external damage prevention, in particular to a dynamic external damage prevention monitoring and early warning system and method for power equipment. The system comprises an unmanned aerial vehicle which carries a camera and is used for monitoring the power equipment and the surrounding environment in real time, and the unmanned aerial vehicle sends monitored data to an image processing module; the data processing module is used for receiving monitoring data of the unmanned aerial vehicle and preprocessing the monitoring data; the identification and tracking module is used for carrying out multi-target identification and real-time tracking on the preprocessed data; the alarm module is used for giving an alarm if the recognition and tracking module detects that the power equipment is threatened by the external damage target; and the control module is used for adjusting the flight height of the unmanned aerial vehicle and the shooting angle of the camera. The external damage target can be monitored, recognized and early warned in real time, so that countermeasures are taken in time, and accidents are prevented from occurring or expanding.

The present invention relates to a mine safety risk investigation method and system based on intelligent Internet of Things devices. The method comprises the following steps: first, obtaining the working event data of the intelligent Internet of Things device, identifying and extracting the key features related to the safety symptoms; then, establishing a mapping model between the working events and the safety symptoms, performing statistical analysis on the real-time monitoring data, and identifying potential safety risk points; comparing the risk points with the mine safety management regulations, generating a safety risk assessment report, and issuing early warning information according to the assessment report. The system comprises a data acquisition module, a mapping model module, a data analysis module, a comparison and assessment module, and an early warning module, and can realize automated mine safety monitoring and risk investigation. The present invention significantly improves the real-time and accuracy of mine safety management through intelligent data analysis and early warning mechanisms, can effectively prevent and reduce the occurrence of mine safety accidents, and provides strong support for mine safety management.

The invention discloses an information security management method and system. The method comprises the following steps: acquiring code audit data, Web application firewall data and vulnerability management data; performing feature extraction on the same batch of code audit data, Web application firewall data and vulnerability management data to obtain structured feature vectors, and fusing the feature vectors into a feature matrix; constructing and training an anomaly detection model, and inputting a feature matrix into the anomaly detection model to obtain an anomaly score; and according to the abnormal score and a set threshold value, generating a corresponding security risk level, and sending the security risk level to a security operation module for display or alarm. According to the invention, the data of different sources are fused through the anomaly monitoring model, so that the deviation caused by a single data set is reduced, the detected abnormal point is more representative, the risk of false alarm is reduced, the accuracy of anomaly monitoring is improved, and the efficiency of safety management is improved.

The invention discloses an urban non-fortification building reinforcement identification method and device based on historical streetscape pictures, and relates to the technical field of civil engineering technology and image processing, and the method comprises the steps: constructing a streetscape sequence data set containing time sequence features according to the historical streetscape pictures and streetscape metadata, the method comprises the following steps: firstly, constructing a streetscape feature extraction model, marking streetscape pictures of non-fortified buildings in different reinforcement states to obtain a streetscape training set, and training the streetscape spatial-temporal feature extraction model which is an improved FCOS network; inputting a streetscape sequence image to be recognized into the trained streetscape spatial-temporal feature extraction model, and determining a reinforcement state and a change time point of a non-fortified building in the streetscape sequence image; and in combination with geographic information data, geocoding is performed on the identified building, the spatial distribution and change trend of the reinforced building are obtained, and a building reinforcement thermodynamic diagram over the years in the region of interest is obtained. According to the invention, the efficiency of building safety management can be greatly improved, and the investment of human resources is reduced.

The invention discloses a rural highway potential safety hazard checking system and method, particularly relates to the technical field of highway potential safety hazard checking, and is used for solving the problem of low safety management efficiency in a rural highway potential safety hazard checking process. By deploying the stress sensor, the displacement sensor and the acceleration sensor, different areas of a rural highway are monitored in real time, data acquisition frequency optimization and abnormal value detection are combined, data precision is ensured, and data are transmitted through a wireless network. And carrying out feature extraction on the collected data, constructing roadbed settlement and traffic load features, inputting the features into a nonlinear autoregression model for multi-step prediction, accurately predicting a future roadbed settlement trend, identifying a settlement risk in advance and providing early warning. And in combination with simulation verification information, the prediction precision and robustness of the model are further improved. By analyzing different generated signals and dynamically adjusting strategies, the system is ensured to flexibly cope with different settlement risks, and the safety management efficiency of rural roads is remarkably improved.

The invention provides an engineering safety management method and system based on a BIM model, and relates to the technical field, and the method comprises the steps: obtaining engineering information, constructing an engineering BIM model, carrying out the monitoring region division of the engineering BIM model, obtaining an engineering model monitoring region, and obtaining the region group association information; calculating an area scheme influence coefficient to obtain a risk area, and calculating a type influence coefficient of each engineering monitoring type to obtain a risk type; calculating an engineering cumulative risk influence coefficient, comparing the engineering cumulative risk influence coefficient with a preset cumulative influence threshold value, obtaining cumulative risk judgment of the engineering BIM model, and further obtaining risk influence early warning information; according to the risk early warning information, the abnormal risk monitoring type and the risk area information, reversely obtaining the wind direction source information, and adjusting the risk source information until the risk early warning information does not exist, the method can comprehensively and accurately identify and evaluate the potential risk in the project, and realizes real-time monitoring and effective early warning.

The invention relates to the technical field of tailing pond safety, in particular to an alarm method, system and device applied to tailing pond safety monitoring, and the method comprises the steps: obtaining environment parameter information in a preset range, and detecting the environment parameter information, whether at least one kind of parameter information which does not meet the environment parameter standard exists in the environment parameter information or not is judged, if yes, abnormal parameter information in the environment parameter information is determined, the abnormal parameter information is matched with a preset alarm standard, and abnormal alarm information corresponding to the abnormal parameter information is obtained; and determining an abnormal alarm device and an abnormal alarm signal according to the abnormal alarm information, and sending the abnormal alarm signal to the abnormal alarm device for alarm prompt. The safety management efficiency of the tailings pond is improved.

The invention discloses a safety helmet wearing detection method and device in a construction scene and a medium, an image is divided into grids, a sliding window is constructed to extract feature information in the grid image, the calculation amount is reduced, the problem of imbalance between positive and negative samples is relieved, the amplitude of an anchor frame in each direction is obtained through descriptors, and the safety helmet wearing detection accuracy is improved. According to the method, the gradient magnitude in the neighborhood in the interpolation direction is obtained, feature point positioning is performed based on the gradient magnitude, and the anchor frame is screened, so that a target object can be accurately positioned, the detection accuracy is improved, more accurate identification data is obtained by eliminating splicing gaps, and the image is denoised to improve the image quality, so that the image identification precision is improved. According to the invention, the method optimizes the training process of the model, improves the generalization capability and processing speed of the model, can achieve the quick and automatic recognition of whether a worker in a construction site wears a safety helmet or not, improves the safety management efficiency, and reduces the safety accidents caused by not wearing the safety helmet.

The invention provides a converter station personnel and vehicle positioning early warning method based on multi-platform data interaction, and relates to the technical field of positioning early warning, carrier phase information of a target positioning label is obtained through a Beidou satellite, the carrier phase information is analyzed through an RTK positioning model to obtain a three-dimensional space coordinate and a corresponding timestamp, and when a label association object is a vehicle part, the vehicle part is subjected to positioning early warning. Vehicle operation state matching is carried out according to the coordinates and the timestamp to obtain a vehicle operation state model, operation risk assessment is carried out according to the model to obtain a danger index, and when the danger index is larger than or equal to a danger index threshold value, a first alarm signal is generated according to the coordinates, the timestamp and the model, and visual display alarm is carried out on a display terminal. The technical problems that the positioning system is low in positioning precision and reliability, insufficient in real-time data processing and risk assessment early warning and limited in flexibility and expansibility in a complex scene are solved, and the effects of improving the positioning precision and the flexible processing capacity in a complex environment and achieving dynamic risk assessment and intelligent early warning are achieved.

The invention relates to the technical field of batteries, in particular to a self-adaptive battery heat preservation system in a low-temperature environment. The system comprises a battery pack module, a data acquisition module, a battery heating module, a data processing module, a heat preservation control module and a temperature control execution module. According to the invention, the vibration interference of the battery pack module is monitored and analyzed, the temperature data is obtained under the condition of no vibration interference, the battery pack module is periodically monitored, and the temperature data and the vibration data of the battery pack module are analyzed in a combined manner under the condition of vibration interference. The situation that the working performance of a battery is affected due to the fact that cracking of a battery pack shell is not detected in time when a power battery system is seriously affected due to vibration generated by the terrain is avoided, and therefore the vibration working condition and the temperature condition of the power automobile are monitored in real time by arranging a data acquisition module; and the thermal insulation control module timely responds to low-temperature protection control on the battery, so that the safety management efficiency of the battery system is improved.

The invention discloses a coupling real-time risk assessment method for diffusion of fuel gas to a power pipeline, and belongs to the field of risk assessment. The method comprises the following steps that S1, a gas pipeline and a power pipeline are subjected to pipe section division; s2, gas leakage types are judged, different diffusion boundary dangerousness is obtained, and soil permeability and road surface correction factors are calculated; s3, acquiring coupling risk influence factors of the gas pipeline and the power pipeline; s4, obtaining the coupling risk of the gas pipeline and the power pipeline; s5, different correction coefficients are set according to different power pipeline laying modes, the cross section area of fuel gas gathering in the different power pipeline laying modes is calculated, the fuel gas diffusion speed is calculated, the plugging correction factor of the power pipeline is obtained, and the maximum distance from the fuel gas diffusion to the dangerous concentration is calculated; and S6, obtaining the risk of the power pipeline. According to the invention, a basis can be provided for troubleshooting, early warning and processing of a gas-to-power pipeline diffusion coupling accident, occurrence of a fire explosion accident can be prevented, and city safety is guaranteed.

The invention discloses an enterprise data security management method and system suitable for big data analysis, particularly relates to the technical field of data processing related to computing systems or devices, and comprises a block chain technology, hash operation, authority control, an adaptive segmentation algorithm and the like. The source, the flow direction and the transmission time of each data transmission are recorded through the private block chain, a non-tampering record is generated, even if the data frequently flows among multiple departments, the transmission path of the data can be tracked, and the security and the integrity of the data are ensured; in addition, the number and granularity of data segments are dynamically adjusted according to the data transmission condition and the evaluation result, the safety management efficiency in a complex data flow environment is further improved, and the complexity of enterprise data management is reduced.

The invention relates to an industrial area risk management and control technology, in particular to an industrial plant area environment risk management and control system, which comprises the following steps: firstly, carrying out statistical analysis on accident points of each area in a plant area, then evaluating the risk level of each accident point, and then analyzing the overall area risk index of the area according to each accident point; a corresponding accident monitoring terminal is arranged at each accident point to carry out real-time risk factor monitoring on each accident point, real-time monitoring of risks of each area in a factory area is realized, abnormity is found in time and early warning is carried out, accident information is displayed through an alarm display module, and a corresponding maintenance route is planned. And the safety management efficiency of the factory area is improved while the fault occurrence rate of the factory area is reduced.

The invention discloses an implementation mode of on-track operation management and control of railway personnel, and particularly relates to an implementation mode of on-track operation management and control of railway personnel through an operation management and control system based on a railway yard total-factor proportion graph and a personnel operation terminal with high-precision positioning. Through the operation management and control system based on the railway yard total-factor proportion graph and the operation terminal with high-precision positioning personnel, the functions of accurate recording of the time for the operators to go up and down the railway, operation track recording, operation border crossing alarm and the like are realized, and the safety protection level of the first-line maintenance personnel of the railway is effectively improved.

The invention relates to the technical field of information security, in particular to an information security risk quantification method based on risk harmfulness and asset structure decomposition, and the method comprises the steps: generating a probability value and an influence value of each information security risk on each underlying asset based on each vulnerability faced by a business informatization asset system; calculating a hazard quantized value of each kind of information security risk generated by each kind of vulnerability on each underlying asset; combining at least two asset types and the underlying assets corresponding to the two asset types, generating a weighted hypermatrix of the combined underlying assets by adopting a network analysis method, and solving the weight of each underlying asset based on the weighted hypermatrix; and calculating an overall information security risk value of the business informatization asset system based on the harmfulness quantized value and the weight. An asset system structure and a vulnerability risk distribution structure are integrated, asset association importance analysis is carried out by flexibly utilizing a network analysis method, and comprehensive information security risk assessment and analysis of the system are carried out.

The present invention discloses a smart construction site safety monitoring management system and method, which relates to the field of safety monitoring technology, and is used to solve the problem that the existing construction site environment is relatively complex and changeable, and the working conditions and hidden safety hazards are insufficiently responded to, resulting in limited prediction and identification capabilities, and lagging information transmission and resource allocation, resulting in a lack of flexibility in emergency plans, and the inability to quickly adapt to different emergency situations; by using TOPSIS to comprehensively analyze the characteristic variables used to predict the probability of accident hazards, the probability of accident hazards in each area is calculated; by deploying GPS or RFID systems, the position and density changes of personnel are tracked in real time. Infrared sensors, cameras and other technologies are used to monitor the number and position changes of personnel in real time to improve the accuracy of monitoring, and the position coordinates of personnel and the number of personnel in the area are recorded every unit time, and the position change rate and density change rate are calculated.

The wireless blasting vibration meter comprises a shell, the shell comprises a bottom plate and an outer shell, and the bottom plate and the outer shell are fixed together in a sealed mode; a fixing frame is arranged in the bottom plate, and three accommodating cavities for accommodating detectors in the X direction, the Y direction and the Z direction are arranged in the fixing frame; a circuit board is further fixed on the inner side of the shell, and the detector is electrically connected with the circuit board; a 4G antenna, an external SIM card terminal and an on-off key are arranged on the side face of the shell and all electrically connected with the circuit board. According to the utility model, the X-axis detector, the Y-axis detector, the Z-axis detector and the data acquisition part are integrated in the same sealed housing, so that compared with a traditional split structure, installation steps can be simplified; and meanwhile, the shell adopts a closed design that the positioning raised lines are matched with the sealing rings, so that the waterproof, moistureproof and anti-interference capabilities of the equipment in a severe environment can be remarkably enhanced, and the stability and continuity of monitoring operation are ensured.

The application provides a kind of intelligent construction site safety monitoring and abnormal behavior detection method, it is related to behavior identification technical field, including: based on video frame data, through target detection algorithm, personnel and cigarette in the construction site multi-modal data are positioned, to output first detection result;Based on the first detection result, video frame data and construction site multi-modal data in the preset time period, utilize 3D CNN to combine attention mechanism, to output second detection result;Based on the second detection result and construction area security level information, by isolated forest and self-encoder algorithm, get comprehensive abnormal score.

The invention discloses a road traffic potential safety hazard troubleshooting method based on a railway crossing scene. The method comprises the following steps: S1, constructing a railway crossing road traffic potential safety hazard troubleshooting knowledge system; s2, establishing a standardized data acquisition process; s3, the cleaning process is completed based on the checked road sections; s4, completing a stake number calibration process and completing a troubleshooting road section calibration process; s5, establishing an intelligent railway crossing image recognition method; s6, establishing a railway crossing road traffic potential safety hazard standardized troubleshooting method and summarization; by constructing the intelligent railway crossing hidden danger checking system, multi-angle data acquisition and processing are realized. The system integrates equipment such as a vehicle-mounted high-definition camera, a GPS (Global Positioning System) and a gyroscope, ensures synchronization and accuracy of acquired data, and accurately positions the geographic position of each image in combination with a stake mark calibration technology. The hidden danger types of traffic signs, marking lines and facilities are systematically analyzed for railway crossings with different guard modes.

The present invention relates to the field of battery technology, and in particular to an adaptive battery insulation system in a low temperature environment. The system includes a battery pack module, a data acquisition module, a battery heating module, a data processing module, a heat preservation control module, and a temperature control execution module. The present invention monitors and analyzes the vibration interference of the battery pack module, not only to obtain temperature data in the absence of vibration interference, but also to periodically monitor the battery pack module, and in the case of vibration interference, by combining and analyzing the temperature data and vibration data of the battery pack module, to avoid the failure to detect the cracking of the battery pack shell in time when the power battery system is seriously affected by the vibration generated by the terrain, thereby affecting the working performance of the battery. Therefore, by setting a data acquisition module to monitor the vibration condition and temperature condition of the power vehicle in real time, the heat preservation control module can respond to the low temperature protection control of the battery in time, thereby improving the safety management efficiency of the battery system.

The invention relates to the field of airport road foreign matter detection, and provides a machine learning-based FOD detection algorithm, which comprises an input module used for receiving basic image data of an airport runway; the processing module is used for carrying out preprocessing and feature extraction on the input data; the storage module is used for storing the original image data and the detection result; the acquisition module is used for acquiring image and sound data of an airport runway in real time; and the recognition module is used for recognizing moving objects and static objects in the collected data based on a machine learning model. And high-precision real-time detection is realized through cooperation of multiple modules. The acquisition module adopts a high-definition camera to ensure that image data is clear; the processing module utilizes a convolutional neural network (CNN) to extract features, and can accurately recognize moving and static objects in combination with a YOLOv5 algorithm of the recognition module. The comparison module compares the real-time image with the original image, abnormity is judged if the real-time image is lower than the threshold value, the runway foreign matter can be found in time, and the system can rapidly position the FOD in the complex environment.

The invention provides a coal mine safety early warning method and device, electronic equipment and a medium, and relates to the field of coal mine production. The coal mine safety early warning method comprises the steps that target sensors are used for collecting safety data of a coal mine, the target sensors comprise a low-power-consumption laser methane sensor, a self-diagnosis sensor and a multi-parameter sensor, the target sensors are deployed on a working face of the coal mine, and the distance from the target sensors to a central station does not exceed three layers in a network topology structure; according to the safety data, different early warning levels are determined, an alarm is given, and the safety data comprise the gas concentration of the monitoring working face, the overrun duration of the gas concentration and the overrun range; and responding to the alarm, and performing graded response according to the early warning grade. Through the technical scheme provided by the invention, the problems of insufficient risk early warning and assessment reliability and low safety management efficiency in coal mine production in related technologies are solved, and the risk early warning and assessment reliability and the safety management efficiency are improved.

The invention relates to the field of image processing, in particular to a high-altitude falling object detection method and system based on machine vision. The high-altitude falling object detection method based on machine vision comprises the following steps that a visual area is determined, a visual picture of the visual area is obtained, and the visual picture is captured by at least one imaging device; identifying a first falling object in the visual picture, and evaluating the risk degree of the first falling object; according to the risk degree of the first falling object, a second falling object in the visual picture is recognized, and the risk degree of the second falling object exceeds a risk degree threshold value; and predicting a falling point range of the second falling object, and triggering early warning of the high-altitude falling object according to the number of personnel in the falling point range. According to the invention, all-around monitoring and early warning can be carried out on high-altitude falling objects in complex environments such as a building construction site, so that the personnel and property safety guarantee is effectively improved, the occurrence of safety accidents is reduced, and the defects of traditional safety measures can be made up.

The invention relates to the technical field of steel safety management, and provides a method and a device for improving a safety management system of a steel enterprise. The method comprises the following steps: organizing an expert to determine at least one current problem point on site in advance according to a safety standard, and collecting a multi-view picture set of each current problem point; generating an initial report table according to the optimal view angle picture in the multi-view angle picture set; and obtaining to-be-rectified information of each current problem point filled in the initial report table by an expert according to the safety standard to obtain a target report table. On the basis of on-site investigation of the expert, the expert is assisted to complete the target report table by collecting the multi-view picture set and generating the initial report table according to the optimal view picture, and the problems that in the prior art, report table filling efficiency is low, the generated report table is inconvenient to read, and the report table cannot be read easily are solved. And the accident prevention and control effect level is influenced.