9264 results about "Risk level" patented technology

The invention discloses an unmanned aerial vehicle routing inspection line adaptive obstacle detection method and system based on a monocular camera. The method comprises the steps of obtaining initial flight parameters, evaluating and optimizing the flight parameters based on scene complexity, collecting an original image sequence and carrying out quality evaluation and exposure compensation. Constructing a multi-resolution feature pyramid, extracting feature descriptors, and performing feature matching and verification; performing multi-constraint pose optimization and depth estimation based on a feature matching result, and reconstructing a three-dimensional point cloud; calculating direction enhancement response to extract a power line candidate area, performing multi-view line segment matching and three-dimensional reconstruction, and analyzing dynamic characteristics of the power line; and based on the power line three-dimensional model and the depth map, carrying out obstacle segmentation, calculating an obstacle distance, evaluating a risk level, and generating early warning information and a monitoring scheme. According to the invention, different scenes can be adaptively processed, the obstacle risk can be accurately identified and evaluated, and the inspection efficiency and reliability are improved.

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

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

The invention provides a building safety alert level dynamic adjustment method and system based on artificial intelligence driving, and the method comprises the steps: receiving original monitoring data, spatial position data and data quality indexes of a sensor array, and sequentially carrying out the space-time alignment, feature extraction, quality evaluation and anomaly detection through a multi-source heterogeneous data preprocessing frame; performing multi-dimensional feature fusion and risk pattern recognition analysis on the preprocessed data, and sequentially realizing feature space construction, risk pattern extraction, correlation analysis and grade evaluation; feature fusion, threshold optimization, level adjustment and effect evaluation are realized through a warning level dynamic adjustment mechanism based on the risk level and the confidence coefficient; and finally, completing information fusion, content generation, grade classification and push optimization through early warning information intelligent generation and graded push processing. According to the invention, the accuracy and timeliness of building safety early warning are obviously improved.

The invention belongs to the technical field of coal mine disaster early warning, and discloses a coal mine fire and gas disaster risk prediction method and system, and the method comprises the steps: collecting multi-source heterogeneous data collected at a mine site, and carrying out the data preprocessing and conversion; for the preprocessed image and video data, a Video Swin Transform model is adopted to extract spatial and temporal features, and a cross-modal association fusion Transform model is adopted to realize multi-modal feature unified fusion; mapping the fused multi-modal feature representation to a disaster-causing factor index space, calculating a risk assessment index containing a secondary interaction item, and dividing risk levels; and generating a disaster early warning strategy according to risk level analysis, issuing an early warning notice, performing regular maintenance and updating, and outputting a decision report. According to the method, unified processing and fusion representation of multi-source heterogeneous data can be realized, complex association among disaster-causing factors is accurately captured, the risk index with physical significance and engineering reference value is calculated, and high-quality decision support is provided for prevention and control of coal mine fire and gas disasters.

The invention discloses a predictive maintenance strategy optimization method for transformer area equipment maintenance, relates to the technical field of transformer area equipment maintenance, and is used for solving the problem of poor maintenance effect of transformer equipment. Sensors are installed in a transformer area to collect current, voltage, temperature and other key data, preprocessing, frequency alignment and fusion are carried out to generate the same equipment state data, and when data fluctuation exceeds a threshold value, re-collection and calibration are triggered, a health state model is constructed, and a health index is calculated to evaluate the equipment state. The method comprises the following steps: predicting a future health state through an LSTM model, triggering early warning when the future health state is lower than a baseline, dividing risk levels according to health indexes and risk factors, setting maintenance priorities based on health assessment and fault prediction, applying a multi-objective optimization algorithm in combination with an emergency degree and equipment importance to assess resource requirements, and dynamically monitoring resource use conditions. The prediction precision is improved, and the equipment resource allocation efficiency and the maintenance response speed are improved.

The invention discloses a method, a system and a device for monitoring multifunctional parameters of a direct-current drilling machine, and relates to the technical field of manufacturing of industrial automatic control system devices. The method, system and device for monitoring the multifunctional parameters of the direct-current drilling machine comprises the steps that S1, various data are collected and subjected to standardization and normalization processing, and a standardized working condition data set is constructed; s2, multi-dimensional disturbance characteristics are analyzed, and the stability level of a drilling system is quantified; s3, evaluating a dynamic evolution trend of a working condition, and updating a risk level, a response strategy and a monitoring priority; and S4, identifying an abnormal state based on the key disturbance value and the trend evolution value, and generating a monitoring report. The problems that an existing direct current drilling machine display device is insufficient in key working condition feature extraction capacity, deep understanding and trend analysis of the equipment operation state are difficult to support, and then the early warning timeliness and judgment accuracy of the abnormal state are limited are solved.

The invention relates to the technical field of airport safety management, in particular to an airport safety management method and system, and the method comprises the steps: obtaining various data of airport operation in real time through a multi-source heterogeneous data collection module, carrying out the feature fusion processing through a dynamic risk assessment model, generating a three-dimensional risk thermodynamic diagram, and outputting a risk level and a type identifier; triggering a hierarchical response mechanism, generating a resource configuration scheme through a resource scheduling optimization algorithm, and sending a scheduling instruction; in the emergency disposal stage, a multi-terminal collaborative platform is used for monitoring the disposal effect, when the risk diffusion coefficient exceeds a preset threshold value, a cross-department collaborative disposal protocol is started, and the resource allocation weight is dynamically adjusted. According to the method, the risk detection precision is improved through multi-source data fusion and space-time correlation analysis, multi-target optimization configuration is realized by adopting a dynamic resource scheduling matrix, a closed-loop mechanism of hierarchical response and cross-department co-processing is constructed, the traceability of the process is enhanced in combination with digital twin and block chain technologies, and the airport safety management level is effectively improved.

The invention discloses a wind power plant booster station multi-source data fusion anti-misoperation locking intelligent decision-making and early warning method, and relates to the technical field of intelligent misoperation prevention of a power system, and the method comprises the following steps: collecting multi-source heterogeneous data, obtaining the data through a distributed sensor network, and carrying out the edge calculation preprocessing; performing data space-time alignment and fusion, performing equipment state evaluation, and constructing a deep belief network and Bayesian network hybrid model to calculate a health index; anti-misoperation rule modeling is carried out, and operation logic verification is carried out based on a Petri network and an expert knowledge base; risk early warning decision making: fusing multi-source early warning information to divide risk levels; intelligent locking control is carried out, and a locking strategy is optimized through reinforcement learning; and performing decision support and visualization, constructing a three-dimensional digital twinborn model, and displaying operation guidance and risk early warning in combination with an AR technology. Through multi-source data fusion and intelligent decision making, the anti-misoperation locking accuracy and efficiency are improved, and the safety and the operation and maintenance level of the booster station are remarkably enhanced by equipment fault early warning three months ahead of time.

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

The invention provides a risk control big data mining method and system based on the Internet of Vehicles, and the method comprises the steps: collecting a real-time operation data flow of a target vehicle in an Internet of Vehicles environment, including vehicle state time sequence data, a driving behavior event set, external environment perception data and a vehicle-mounted communication interaction record; and then respectively extracting a combined operation feature vector, an abnormal behavior mode identifier and a dynamic environment risk factor from different data, converting to generate a structured communication feature matrix, constructing a space-time association risk analysis map based on the structured communication feature matrix, and carrying out driving risk dynamic assessment on a target vehicle through the space-time association risk analysis map. And finally, generating a risk control decision instruction set according to the result, and feeding back the risk control decision instruction set to the vehicle-mounted control terminal and the Internet of Vehicles cloud platform, thereby realizing comprehensive assessment and effective management and control of vehicle risks.

The invention relates to the field of fault early warning, in particular to a wind power fault dynamic early warning method and system based on multi-source heterogeneous data fusion. According to the method, multi-source data such as SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator are collected in real time, standardization processing is carried out, and a multi-dimensional feature vector is constructed. And generating a fusion data set by using an adaptive weighted fusion algorithm, constructing a fault prediction model based on a deep convolutional neural network, and outputting a health state assessment value and a fault risk level in real time after historical fault sample supervised training. And when the risk level exceeds a threshold value, generating an early warning signal containing a fault type and a positioning and repairing suggestion, dynamically adjusting a monitoring parameter weight, iteratively updating a model, and realizing adaptive optimization of an early warning strategy. The problem that an existing method depends on single data source and multi-source data fusion is solved, and accurate dynamic early warning is achieved.

The invention provides a method for intelligently generating a complete dispatch report for financial unfavorable assets, relates to the field of management systems, and aims at deeply fusing multi-source heterogeneous data such as legal instruments and financial statements and forming a comprehensive context sensing model for target assets through multi-modal feature extraction, semantic alignment and knowledge graph construction technologies; a potential and non-dominant risk factor combination deeply coupled with asset characteristics is automatically mined by applying a genetic algorithm and other evolutionary calculation methods, and a self-adaptive risk assessment network is constructed to dynamically and quantitatively assess and predict the comprehensive risk level; carrying out attribution analysis on a risk assessment result by adopting an interpretable artificial intelligence model, and clearly revealing a key influence path and core data evidence; and according to a report logic framework and a narrative template which can be dynamically adjusted by a user, automatically outputting a financial non-performing asset full-duty survey report including deep analysis, risk early warning, diversified disposal suggestions and compliance review key points.

A chemical industrial park hazardous chemical substance vehicle real-time dynamic scheduling system based on multi-dimensional data fusion and intelligent decision-making comprises a vehicle monitoring module used for collecting dynamic information of transport vehicles in real time and monitoring the running state of each vehicle; the risk assessment module is used for calculating the current risk level of the transportation task based on the data collected in real time in combination with a risk assessment model; the path optimization module is used for calculating and adjusting an optimal driving path through a dynamic A * path planning algorithm; and the emergency response module is used for automatically starting when the high-risk condition is detected, so that the safety in the dangerous chemical transportation process is ensured. The invention aims to realize real-time and dynamic scheduling of the hazardous chemical vehicles in the chemical industrial park through multi-dimensional data fusion and intelligent algorithm design. By integrating multi-dimensional data of vehicles, goods, external environment, time, space and the like, the system can be ensured to automatically adjust a scheduling scheme in an emergency, and an efficient emergency response mechanism is provided.

The invention relates to the technical field of geological risk monitoring and early warning in tunnel construction, in particular to a karst tunnel risk intelligent early warning method, device and equipment based on deep learning, and the method comprises the steps: obtaining multi-source coupling data; performing high-dimensional fusion on the processed multi-source coupling data by using a generative adversarial network, and reconstructing a high-precision three-dimensional model of the detection area; performing geological risk grade classification on the detection area to generate a corresponding risk distribution map; generating a dynamic risk evaluation index, and triggering an early warning signal when the risk level exceeds a preset threshold value; the method comprises the steps of multi-source data acquisition, data preprocessing, high-precision three-dimensional model reconstruction, risk grade classification, dynamic risk monitoring and early warning and the like, and can solve the problems of single risk detection means, insufficient evaluation precision and poor real-time performance in existing karst tunnel construction.

The invention provides a multi-dimensional data authority management and privacy protection method for an electric power information network, and the method comprises the steps: dynamically selecting the type of a watermark according to the leakage risk level of a screenshot event, and enabling the watermark type to comprise a simple character watermark, a character and image mixed watermark, an invisible watermark, and a two-dimensional code watermark; user screenshot content factors are added in a screenshot event distribution curve of user historical data to judge the risk level of a user, the risk level of a user group is updated, and when the screenshot frequency, time interval, screenshot mode, distribution mode and curve volatility of the user are all normal and screenshot content only aims at sensitive data screenshot, the risk level is improved; a screen capture auditing and monitoring mechanism is established, full-life-cycle recording and tracking are carried out on screen capture behaviors of a user, and screen capture events, watermark embedding conditions and abnormal behavior information in a system are monitored in real time through a visual instrument panel and a report.

The invention relates to the technical field of pipeline data processing and health state evaluation, and discloses a pipeline interior detection method and system based on multi-sensor fusion. The method comprises the following steps: performing health index calculation on pipeline state evaluation data to obtain a pipeline health state index, and performing risk prediction on the pipeline health state index to obtain a risk level; performing pipeline defect feature recognition to obtain defect feature data, and performing multi-category classification processing on the defect feature data to obtain defect type data; performing accurate position analysis on the defect type data to obtain defect space distribution data, performing structural integrity evaluation to obtain a defect severity quantitative data set, performing multi-criterion decision analysis on the defect severity quantitative data set and risk levels to obtain defect repair priority ranking data, and performing defect repair priority ranking on the defect repair priority ranking data; and performing intelligent repair scheme generation on the defect repair priority ranking data to obtain repair suggestion data. According to the invention, the state of the pipeline can be evaluated more comprehensively and accurately.

The invention provides an information system engineering supervision project risk adaptive assessment method and system. According to the project management scheme, a dynamic knowledge graph is formed by constructing a data model fusing a building information model and a geographic information system. The three-dimensional coordinate track of the construction machinery is captured in real time by using an ultra wide band positioning device array, and the space occupied hot area distribution is generated by using a multipath interference suppression technology. An operation simulation environment is established based on a digital twinning technology, and a space-time risk topology network is established to early warn path conflicts and safety spacing problems. And according to network node density characteristics, dynamically adjusting a risk assessment parameter weight, and assessing an equipment collision probability and a construction process conflict index. Finally, the stress threshold value and the signal sampling frequency are adjusted according to the composite risk level result feedback, and construction management and safety guarantee are optimized. According to the technical scheme provided by the invention, the efficiency and safety of information system engineering supervision project risk adaptive assessment can be improved.

The invention provides an intelligent vulnerability mining platform construction method and system based on a large model. The intelligent vulnerability mining platform systematically identifies and analyzes potential security risks, collects and identifies code snippets with the security risks, arranges the code snippets into risk code paragraphs, identifies target code paragraphs, determines known vulnerabilities, generates context information, captures key events in actual operation, and records real execution paths and state changes. The method comprises the steps of generating a dynamic execution track, simulating different paths, generating an attack vector path, predicting unknown vulnerabilities and credibility scores of the unknown vulnerabilities, testing prediction results of high-credibility scores, confirming and generating real vulnerability information, recording the information to a vulnerability knowledge base, and generating a security report containing vulnerability description, influence ranges, risk levels and repair suggestions. According to the technical scheme provided by the embodiment of the invention, the detection accuracy and efficiency of the security vulnerabilities in the software system are improved.

The invention belongs to the technical field of intelligent machining path control, and discloses an intelligent numerical control machine tool automatic programming path optimization method based on workpiece modeling, which comprises the following steps: acquiring a CAD model, machine tool sensor data, tool wear data and historical machining logs, generating a workpiece characteristic parameter set, and fusing a three-level compensation mechanism to generate a dynamic error parameter set; then, dividing a preliminary risk level of the processing area, and performing secondary risk assessment to generate a comprehensive risk level; extracting a risk level conflict area, and determining a final risk level; constructing a static / dynamic cost matrix to obtain a path priority map; thirdly, generating an initial path, smoothing an optimized path trajectory, and performing multi-objective optimization to generate an optimized path planning table; cutting parameters are adjusted in real time, the path feasibility is verified, and a real-time control instruction set is generated; and finally, constructing a quality-process correlation model, generating a global strategy packet, forming closed-loop iteration, and completing system self-evolution.

The invention provides a charging pile line fire-fighting early warning method based on big data and a storage medium, and the method comprises the steps: collecting a line operation data set of a target charging pile cluster, the line operation data set comprises multi-source time sequence monitoring data, carrying out the cross-modal feature alignment of the multi-source time sequence monitoring data, and generating a time-space correlation feature matrix; inputting the space-time correlation feature matrix into a pre-trained fire risk prediction model, generating a line abnormal risk probability distribution diagram, generating a layered early warning signal set according to risk levels corresponding to space nodes in the line abnormal risk probability distribution diagram, and triggering a dynamic protection mechanism based on the layered early warning signal set. The dynamic protection mechanism includes performing a current cut-off operation on a high-risk line segment, and performing a power attenuation operation on an adjacent line segment. According to the invention, the reliability and the intelligent level of the charging pile line fire-fighting early warning system can be comprehensively improved.

The invention relates to the technical field of vehicle insurance marketing management, in particular to a vehicle insurance channel whole-process marketing management method and system, and the method comprises the steps: collecting and integrating various data of clients from multiple data sources to construct a resource library; carrying out customer segmentation and accurate portraying by utilizing algorithms such as clustering analysis and the like; predicting demands based on the portraits, historical behaviors and seasonal factors; personalized pricing is made in combination with characteristics such as risk levels; predicting customer loss by means of logistic regression and the like; implementing a corresponding customer management strategy according to the loss prediction; carrying out personalized recommendation by adopting a deep learning recommendation algorithm; advertisement putting is optimized in combination with a dobby machine and reinforcement learning; evaluating the risk by using multiple algorithms; fraud is prevented and controlled through anomaly detection; optimizing a claim settlement process based on the claim settlement data; and the marketing and advertising effects are evaluated through A / B testing and the like. According to the method, the precision marketing level can be improved, pricing and risk control are optimized, efficient operation and decision support are realized, and the market competitiveness and sustainable development capability of companies are enhanced.

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

Dynamic geofences may be generated and updated based on locations of related assets. For example, locations of a group of related assets, including a primary asset and one or more secondary assets, may be determined. A geofence associated with the primary asset may then be analyzed to identify any geofences of secondary assets that overlap. This process may be repeated for each overlapping geofence to identify additional overlap and expand or move a combined dynamic geofence. Environmental aspects such as time of day, weather conditions, and risk levels may be considered for dynamic geofence adjustments. The combined geofence and asset locations may be displayed on a user interface.

The invention discloses a distribution cable branch box state monitoring method based on state recognition, and particularly relates to the technical field of power monitoring, and the method comprises the steps: collecting multi-dimensional operation data of a plurality of branch boxes in a continuous time period, and constructing a state evolution sequence; calculating a state offset score and an adjacent equipment state consistency score to judge whether an evaluation process is triggered or not; after triggering, constructing a state influence probability map, identifying an abnormal influence path, extracting a state disturbance diffusion index and a cooperative behavior deviation index, inputting into a pre-trained risk identification model, generating a risk level interval and a cause probability distribution vector, executing an influence regulation and control measure, and updating a state identification logic; according to the method, dynamic perception of the state of the branch box is realized by constructing a state evolution sequence, combined judgment of individual and group behaviors is realized by combining a state offset score and an adjacent equipment state consistency score, and a risk level interval and cause probability distribution vector are used for driving regulation and control strategies and map updating. And the monitoring precision and the self-adaptive capability of the system are improved.

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention relates to the technical field of wire disaster prevention analysis, and discloses an overhead transmission line icing monitoring and disaster prevention method and system. The method comprises the following steps: acquiring meteorological parameters, stress data and icing images through distributed sensing nodes; performing time-space synchronization processing on the data; calculating icing state information; evaluating an icing risk level; selectively starting the deicing device; and storing the data and extracting a law to optimize a disaster prevention plan. According to the invention, accurate monitoring of the icing state, scientific assessment of risks, intelligent selection of deicing measures and continuous optimization of disaster prevention strategies are realized, so that the ability of the power grid to deal with icing disasters is significantly improved, and economic losses and potential safety hazards are reduced.

The invention provides a management method and system for hazardous chemicals in a laboratory, and relates to the technical field of hazardous articles, and the method comprises the steps: obtaining hazardous article operation video data of an operator through an image collection device, extracting key node coordinates and skeleton movement track information, and mapping the information into standardized operation behavior sequence data; and a time sequence behavior evaluation model constructed based on a long and short term memory network is used to identify the non-standard operation fragments, a risk level coefficient is calculated in combination with a dangerous goods attribute information base, and a multi-dimensional evaluation report is generated. And according to the evaluation report, calling a correction strategy library to generate an improvement suggestion containing characters, videos and three-dimensional models, and pushing the improvement suggestion to an operator. By monitoring the behavior of the operator after executing the improvement suggestion and dynamically updating the risk score and the time sequence behavior evaluation model, the dynamic optimization of the evaluation standard is realized, and the safety of the laboratory hazardous chemical substance operation is effectively improved.