286 results about "Safety index" patented technology

The embodiments of the present disclosure belong to the technical field of data processing. Provided is a multimodal scenario risk determination method based on a generative AI large language model. The method specifically comprises: step 1, acquiring multimodal data to form a target data set, wherein the multimodal data comprises visual data and text data; step 2, using an ALBEF algorithm to extract key features corresponding to the target data set, and fusing the key features into a comprehensive scenario representation; and step 3, on the basis of a preset safety index and a large language model, evaluating a risk degree corresponding to the comprehensive scenario representation, comparing the risk degree with a risk threshold, and determining whether the scenario corresponding to the comprehensive scenario representation is a high-risk scenario. By means of the solution in the present disclosure, a high-risk scenario can be rapidly recognized and identified, so as to provide a basis for taking emergency measures, thereby enhancing the real-time response capability.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

An available transfer capability evaluation method and apparatus for a multi-region power system, belonging to the technical field of new energy grid connection. The method comprises the steps: in view of multi-dimensional uncertainty of new energy output and a load demand, on the basis of a conditional generative adversarial network method, determining a typical daily source-load scenario set; constructing an initial operation point set on the basis of the typical daily source-load scenario set, and determining a limit operation point of a multi-region power system; on the basis of the initial operation point set and the limit operation point, constructing an ATC evaluation model on the basis of safety indexes of multi-region power grid operation; and, on the basis of the ATC evaluation model and the typical daily source-load scenario set, determining available transfer capability probability distribution of the multi-region power system.

The invention relates to a dynamic planning method, device and equipment for a low-altitude route of an unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring real-time dynamic environment change data and obstacle distribution information of a low-altitude flight area, constructing a preliminary data set, and generating an initial flight path; according to the initial flight path, predicting energy consumption data, and if the energy consumption data exceeds a threshold value, performing optimization to obtain an optimized path; performing risk assessment quantification, combining a safety margin adjustment mechanism to obtain a safety index, and if the safety index is lower than a threshold value, performing safety distance adjustment to generate a safety path; monitoring flight conditions, generating a condition monitoring update report, acquiring new obstacle position information, and generating a dynamic obstacle avoidance path; and generating an execution instruction set of the unmanned aerial vehicle according to the dynamic obstacle avoidance path. By adopting the method, the dynamic environment real-time adaptability of the unmanned aerial vehicle can be improved, and the route meeting the task requirement can be planned in time when the unmanned aerial vehicle faces sudden obstacles or environment changes.

The invention provides a small and medium-sized reservoir operation safety evaluation system based on sky-ground work, and belongs to the technical field of hydrological monitoring. The device comprises a space-time alignment layer, a feature weighting layer and a dynamic coupling layer which are connected in sequence, the space-time alignment layer is used for performing time alignment and space alignment on the input data of the sky-land water conservancy project; the feature weighting layer is used for quantifying contribution degrees of different monitoring features to safety assessment according to the data after time-space alignment; and the dynamic coupling layer adopts a dynamic weight distribution strategy for different modules through a multi-module coordination engine, fuses feature weighting to obtain a fusion result, and outputs a small and medium-sized reservoir operation safety index according to the fusion result. The evaluation result is more accurate, and the timeliness is better.

The invention relates to the technical field of data processing, and discloses a motor vehicle safety performance classification method and system based on multi-factor coupling influence. The method comprises the steps of collecting multidimensional safety data of a motor vehicle, calculating a mutual information value and transfer entropy to generate a coupling relation network diagram, constructing a three-layer safety index system to form a comprehensive score, obtaining low-dimensional features through phase-space mapping, determining a dynamic boundary value by applying density clustering, and classifying real-time states to output risk early warning information. According to the technical method for realizing accurate classification and early warning, the defects of neglect of factor coupling relation, lack of scene adaptability, insufficient dynamic prediction capability and the like in the prior art are overcome, and the accuracy and practicability of safety performance evaluation and early warning of the motor vehicle are improved.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

The invention discloses an unmanned aerial vehicle adaptive path planning method and system based on double-layer search, and relates to the technical field of unmanned aerial vehicle inspection. At present, the unmanned aerial vehicle is poor in transformer substation inspection safety and autonomy and low in efficiency. The method comprises the steps that the position, type and safety distance data of electrical equipment in a transformer substation are collected in real time through a sensor carried by the unmanned aerial vehicle; dynamically calculating a three-dimensional safety distance between the unmanned aerial vehicle and the equipment according to the type and the position of the equipment in combination with the real-time state information and the environment data of the unmanned aerial vehicle; based on the edge computing nodes, generating an optimal security path by adopting a double-layer path planning algorithm; the planned path is sent to the unmanned aerial vehicle, the flight state is continuously monitored, and if a deviation path or a sudden obstacle is detected, a dynamic path adjustment or hovering instruction is triggered; after the task is completed, flight efficiency and safety indexes are evaluated, algorithm parameters are optimized, and system settings are updated. According to the technical scheme, accurate obstacle avoidance, efficient planning, real-time response and adaptive evolution are realized.

The invention discloses a data driving-based route flight safety risk assessment method. The assessment method comprises the steps of obtaining historical QAR data and performing data preprocessing; constructing a flight safety risk index system which comprises a plurality of index events influencing the flight safety in the predetermined flight scene, dividing the index events into a plurality of risk levels, and setting a corresponding risk index coefficient for each risk level; carrying out standardization processing on the risk index coefficient; calculating information entropies of the plurality of index events, and determining weight coefficients of the plurality of index events according to an entropy weight method; and for the flight to be assessed, calculating a flight safety assessment risk value of the flight. According to the method, the QAR data can be comprehensively utilized to establish a safety index monitoring model of complete team operation, an airline and other related parties can be helped to more comprehensively grasp the safety risk of an aircraft in the course operation, and the safety risk in the course operation can be objectively and comparatively evaluated.

The invention discloses a food processing and production intelligent control method and system based on the Internet of Things, and relates to the technical field of food processing and production intelligent control. The system collects data of a supplier index data set and a food processing site in real time by integrating an Internet of Things platform and a sensor set, and preprocesses the collected data; uploading the data to a block chain; a raw material source credibility index, a processing environment fluctuation index, a machine operation consistency index and a packaging integrity index are calculated, a food production safety index is calculated in a summarized mode and compared with a preset production safety threshold value, and the production safety condition is preliminarily evaluated. The method comprises the following steps: extracting traceability data through an interface, calculating a quality traceability index, calculating a comprehensive production compliance evaluation index in combination with a food production safety index, performing secondary comparison with a preset production compliance evaluation threshold, generating a production adjustment and optimization suggestion, ensuring food safety and improving production compliance.

The invention discloses a LoRa-based ship environment multi-parameter intelligent wireless monitoring system, which is characterized in that environment state data and ship operation auxiliary data are acquired through a data acquisition module, and a data processing module is used for preprocessing and standardizing the acquired data to obtain a standardized data frame; the environment safety quantification module calculates a single parameter safety score, a comprehensive safety index and a safety situation level SL, the intelligent LoRa communication module dynamically selects a communication strategy according to the SL and the current link quality and generates and sends a safety data packet, the LoRa gateway receives the safety data packet, redundancy filtering is carried out, receiving metadata is added, a final data record is formed and uploaded, and the LoRa gateway sends the final data record to the intelligent LoRa communication module. And the data center performs visual display and data storage on the final data record, and generates a network optimization instruction based on the network performance data, so that the problem that transmission reliability, real-time performance and low power consumption are difficult to consider in ship environment monitoring is solved, and the intelligent level of ship safety management is improved.

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

The invention belongs to the technical field of intelligent hydropower, and discloses a hydropower station operation period dam safety classification and grading early warning system and method, through man-machine collaborative inspection of an anti-interference optical fiber sensor and an unmanned aerial vehicle, in combination with edge calculation filtering, YOLO model detection and multispectral analysis, data acquisition precision and preprocessing quality are improved; dynamic risk assessment is realized by adopting a fusion formula, a finite element and LSTM time sequence prediction, and the limitation of a fixed threshold value is broken through; according to safety index grading early warning, the Mesh ad hoc network and the GIS system are linked, and it is ensured that response is timely and accurate; self-evolution optimization is achieved through digital twin iteration and sensor and equipment health management, the false alarm rate and maintenance cost are reduced, information isolated island is eliminated, the accuracy and reliability of dam safety early warning are improved, and the problems that in the prior art, a dam early warning system is single in monitoring means and insufficient in data analysis, multi-parameter coupling risks are difficult to dynamically evaluate, and the safety early warning effect is poor are solved. And missing report or false report is easy to occur.

The invention relates to the technical field of video recognition, in particular to a construction safety real-time high-precision detection method and system based on environmental characteristics. Arranging a plurality of sensors in a to-be-detected area to collect field environment data in real time; analyzing the illumination change of the to-be-detected area through the light and shadow analysis model, calculating a light-safety misjudgment index, and identifying a potential light misjudgment area; a visual processing model is utilized to analyze the worker video, human skeleton key points are recognized in real time, personnel behavior safety indexes are obtained, and potential dangerous behaviors are detected; detecting local wind speed and turbulence conditions and vibration and resonance phenomena of a to-be-detected area in real time through a local breeze and vibration detection model, and evaluating a local environment stability index; and the safety level of the to-be-detected area is calculated in real time by using the light-safety misjudgment index, the personnel behavior safety index and the local environment stability index, so that multi-dimensional and real-time construction safety risk assessment is realized.

The invention relates to an automatic driving automobile test method based on multi-traffic-participant collaborative hunting confrontation, which comprises the following steps: dividing a test scene into a plurality of space-time resource grids based on scene information, and constructing a conflict event set according to tracks of a tested automobile and traffic participants; defining a conflict degree index, a safety index and passing time; a multi-target speed optimization search model is constructed based on the conflict degree index, the safety index and the passing time, a plurality of traffic participants are utilized to cooperatively surround the tested vehicle, the multi-target speed optimization search model is solved in the cooperative surrounding process, and a space-time resource occupation table is obtained; carrying out collision detection, and if no conflict exists, generating an optimal track of all traffic participants; otherwise, returning to execute collaborative hunting; and obtaining a comprehensive index of the tested vehicle when the traffic participant executes the optimal trajectory, and carrying out performance evaluation. Compared with the prior art, a more accurate automatic driving test is realized by constructing a complex scene considering the collaboration between traffic participants.

The invention provides a traffic tunnel preventive monitoring and risk early warning system based on an unmanned aerial vehicle group, and relates to the technical field of traffic tunnel safety monitoring, and the method comprises the steps: laying reference mark points on the inner surface of a tunnel as monitoring nodes, collecting the crack width, surface temperature and deformation of each node through the unmanned aerial vehicle group, and calculating the crack width, surface temperature and deformation of each node; and constructing a multi-dimensional feature vector and calculating a space-time weighted distance in combination with the space coordinates and the timestamps. And adjusting a neighborhood search radius according to the tunnel curvature, identifying a risk region through a density clustering algorithm, and marking unclustered abnormal nodes as isolated risk points. And dividing risk levels based on comparison of the structure safety degree index and a dynamic threshold value, and registering a result with the BIM model to realize three-dimensional visualization. Trend early warning is triggered for an area with monotonically decreasing monitoring safety index for three continuous times, full-process automatic monitoring from data acquisition, intelligent analysis to risk early warning is realized, and the accuracy and timeliness of tunnel structure health monitoring are remarkably improved.

The invention discloses a BIM-based building construction monitoring method, and the method comprises the steps: arranging a data collection device at a construction site, and obtaining multi-source data, such as structural members, construction machinery, spatial positions of operators, construction states, safety indexes and environmental parameters; automatically mapping multi-source data to a BIM three-dimensional model through an adaptive coordinate transformation and semantic recognition algorithm, matching the multi-source data with construction plan nodes, and generating a BIM construction state model reflecting a field state in real time; the system automatically compares the model with a construction plan model, identifies a state and calculates deviations such as progress, position, size, quality and safety; the method comprises the following steps: displaying deviation parts and potential safety hazards in a BIM interface in a highlight manner, superposing rectification suggestions, issuing a construction adjustment instruction confirmed by a manager to a field terminal, and storing monitoring, comparison, early warning and processing records in a database according to a time sequence to form a digital file; and real-time intelligent monitoring and visual management of construction progress, quality and safety are realized.

The invention belongs to the technical field of hydraulic engineering, and provides a dam monitoring method based on data anomaly detection, which comprises the following steps: S1, acquiring multi-source heterogeneous data generated in a dam operation process, and performing time synchronization, data cleaning, missing value filling and normalization processing on the multi-source heterogeneous data; s2, aiming at the preprocessed multi-source heterogeneous data, extracting feature vectors of data modals respectively, and carrying out preliminary splicing and fusion on the feature vectors to form an initial multi-modal feature sequence; s3, inputting the initial multi-modal feature sequence into a mixed space-time diagram attention network, and outputting a high-dimensional space-time feature sequence containing dynamic relevance; s4, inputting the high-dimensional spatial-temporal feature sequence into a multi-view Transform decoder for prediction, and outputting a prediction time sequence of the key safety index; and S5, based on the prediction time sequence of the key safety indexes, comprehensively evaluating the future overall safety state of the dam through a multi-index decision evaluation model.

The invention relates to the technical field of mine safety monitoring, solves the technical problems that miner behavior monitoring is insufficient, equipment fault diagnosis is not accurate enough, and dangerous condition judgment is not intelligent, and particularly relates to a mine safety production behavior monitoring and data analysis system. Acquiring miner position information, miner behavior image information and equipment operation state data in a mine; the behavior analysis module is used for analyzing the miner behavior image information based on an image recognition algorithm to obtain miner behavior safety indexes; the fault judgment module is used for judging whether the equipment breaks down or not by using a fault diagnosis algorithm; the danger judgment module judges whether a dangerous condition that the miner gets close to the fault equipment exists or not, and a dangerous condition index is obtained; and the safety evaluation module comprehensively evaluates mine safety production behaviors. The intelligent level of miner behavior monitoring is improved, the accuracy of equipment fault diagnosis is enhanced, and danger association analysis between miners and equipment is realized.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and device fusing lane dynamic evaluation and safety indexes and a medium. Real-time collision risks are calculated by collecting dynamic data of a vehicle and surrounding vehicles, multi-lane traffic flow data are collected under triggering of specific requirements, and characteristics such as density, speed and flow are extracted; and a lane basic passage score is calculated by fusing the flow and the large vehicle influence, and correction is carried out by combining the lane changing intention of surrounding vehicles to obtain a dynamic lane evaluation result. And constructing a multi-objective decision function based on the safety index and the lane evaluation result, and finally generating and selecting a trajectory with the minimum comprehensive cost value in a decision period for execution. According to the invention, collaborative evaluation of the vehicle close-range safety risk and the lane passing potential is realized, and the lane evaluation can reflect the traffic flow interaction influence. The driving efficiency and the lane adaptability are optimized, and the decision-making ability of the automatic driving system in the traffic environment is improved.

The invention relates to the technical field of traffic engineering facility monitoring, and particularly discloses a traffic engineering facility safety state monitoring system and method based on the Internet of Things, and the method comprises the steps: deploying distributed intelligent sensing nodes comprising multiple types of sensors and edge calculation units at key monitoring points of facilities; nodes collect data by adopting a dynamic self-adaptive sampling mechanism, and an edge computing unit preprocesses the data and performs preliminary abnormity early warning; the data is transmitted to a cloud platform through a hierarchical communication network; the cloud platform performs deep analysis by using a federated learning safety evaluation model, and calculates a safety index to generate an evaluation report; and when the monitoring value exceeds the threshold value, four-stage early warning is triggered corresponding to different inspection or disposal processes. According to the invention, through the whole process optimization of dynamic sampling-layered transmission-federated learning evaluation-multi-stage early warning, efficient, accurate and low-consumption monitoring of the traffic engineering facility safety state is realized, and the contradiction between data privacy protection and evaluation precision is solved at the same time.

The invention discloses a man-machine interaction safety assessment method and system for an intelligent cabin, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining scene element data which comprises environment elements, vehicle state elements and interaction task elements; generating a dynamic test scene sequence for testing the intelligent cabin man-machine interaction system based on the scene element data; operating a to-be-evaluated intelligent cabin man-machine interaction system under the dynamic test scene sequence, and obtaining multi-dimensional performance data of a user state, an interaction behavior, system performance and a vehicle environment; based on a preset causal graph model, performing causal inference analysis on the multi-dimensional performance data, and quantitatively estimating key factors influencing safety indexes; and according to the result of the causal inference analysis, outputting a safety assessment report containing risk root localization, thereby solving the static limitation of a test scene in the traditional intelligent cabin man-machine interaction safety assessment, and improving the accuracy of safety assessment.

The invention discloses an ESG index evaluation method and system based on multi-source data fusion, and relates to the technical field of medical information management, the system comprises a data acquisition module, a data analysis module, an evaluation module and a report module, the data analysis module is responsible for processing a data set acquired from the data acquisition module; the data analysis module is used for calculating a clinical medical registration missing index, a medical equipment failure rate, a medicine statistics failure rate and an environment monitoring failure rate, in addition, the module is also responsible for generating a hospital network port safety index, and the evaluation module is used for evaluating the clinical medical registration missing index by utilizing indexes provided by the data analysis module and combining evaluation of a hospital network client. All aspects of a hospital are comprehensively evaluated, powerful support and clear guidance are provided for improvement and optimization of a hospital management system, and a report module carries out detailed analysis and arrangement on information of the hospital in the aspects of clinical medical treatment, medical equipment, medicines, patients and environment according to information provided by an evaluation module, so that the information of the hospital in the aspects of clinical medical treatment, medical equipment, medicines, patients and environment is reported. And formulating a summary report containing improvement measures.

The invention provides a big data environmental protection management system and method based on a smart park, and relates to the technical field of environmental protection management, and the method comprises the steps: collecting air quality parameters, water quality parameters, meteorological parameters and noise levels of all representative places in the park; calculating an environmental health index, a water quality safety index and a noise influence index; each intermediate parameter is compared with a threshold value, and when the parameters exceed the threshold value, corresponding improvement measures are taken; constructing a comprehensive evaluation model according to the environmental health index, the water quality safety index and the noise influence index, generating a weight in the comprehensive evaluation model through an analytic hierarchy process, and generating a comprehensive environmental quality index by using the comprehensive evaluation model; and setting different environment quality grades according to the comprehensive environment quality index, and making corresponding improvement measures according to the grades. According to the systematic management mode, the precision and response speed of park environment monitoring are effectively improved, and the pertinence and effectiveness of environment treatment measures are ensured.

The invention discloses a truck driver safe driving evaluation method based on SEDEA, and the method comprises the steps: regarding the vehicle characteristics, behavior characteristics, road characteristics and environment characteristics of a vehicle in a driving process as external resources of a driver safe driving task, and regarding the characteristics of overspeed driving, fatigue driving, distraction driving and the like as risk results after the resources are utilized; the method comprises the following steps: taking resource utilization efficiency evaluation as a core means to measure the safe driving ability of a truck driver, and constructing a weight system containing multiple inputs and multiple outputs and a resource utilization efficiency evaluation model to calculate a safe driving score of the driver; and various safety indexes and quantitative classification management measures of the driver are given based on the model. The model is in close contact with the safe driving management situation of the truck driver, not only considers the resource input and the efficiency output of the safe travel task of the truck driver, but also sets the optimization direction between different inputs and outputs through a weight system.

The invention discloses a real-time monitoring and early warning method and system for hoisting operation of a wind generating set, and relates to the technical field of wind generating set construction, the method comprises the following steps: establishing a digital twin model of a hoisting operation site, collecting state data in real time for dynamic coupling, and constructing a virtual monitoring network; performing short-term advanced parallel simulation in the network to obtain future state trajectory clusters under different potential interferences; extracting a key safety index time sequence from the trajectory cluster, and calculating a future instability risk probability; and when the risk probability exceeds an adaptive early warning threshold, generating multi-level early warning information and triggering active intervention. The technical problem that the instability risk is difficult to recognize and intervene in time due to the fact that the hoisting operation of the existing wind generating set lacks real-time prediction of the system state in a complex environment is solved, and the technical effects of real-time early warning and effective intervention in the hoisting process are achieved by constructing the digital twinborn monitoring network and carrying out risk propagation prediction.

The invention relates to the technical field of natural gas transportation safety, in particular to a hydrogen embrittlement risk online monitoring system and method for a hydrogen-doped natural gas pipeline based on multi-parameter fusion, and the system comprises a sentry sensing unit which adopts a metal short pipe which is the same as a to-be-monitored hydrogen-doped natural gas main pipeline in property, material, process, wall thickness and service history, an internal circulating medium is kept consistent with the main pipeline; the risk sensor is provided with a multi-parameter sensor to obtain risk sensing parameters including hydrogen permeation current, pipe wall micro-strain and temperature signal parameters; the signal processing unit is connected with the sentry sensing unit, receives the risk sensing parameters and analyzes and generates hydrogen embrittlement risk indexes through a risk analysis model; and the risk warning unit is connected with the signal processing unit, and triggers a danger prompt signal when the hydrogen embrittlement risk index is greater than a preset safety index. The method has the effect of meeting the danger monitoring effectiveness of safe operation of the hydrogen-doped natural gas pipeline.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety monitoring system based on the Internet of Things, which comprises a sensing layer, a network layer, a platform layer and an application layer, the platform layer comprises a data management module, a model training module and an early warning decision module; by arranging the platform layer, seepage pressure, strain, displacement and external environment data can be deeply fused through the data management module, three types of parameters including the structure, the environment and the load are covered, and the problem that a single sensor monitors a blind area is solved; the dam body safety index is obtained after weight correction is introduced through the weighted Bayesian network, the uncertainty fusion problem of multi-source data is solved, the credibility of the data is described through probability distribution, noise interference of single data is effectively avoided, meanwhile, parameter contribution is quantized, and the accuracy of data fusion is improved. The relative importance of osmotic pressure, strain and displacement to dam body safety is determined through weight correction, and the defect that all parameters are treated equally is eliminated.

The invention provides a multi-parameter fusion foundation pit safety state monitoring method based on dynamic weight adjustment, which belongs to the technical field of state monitoring and comprises the following steps: collecting deformation of an enclosure structure, settlement and displacement of surrounding structures, underground water level variation and soil pressure variation in real time; performing fluctuation analysis on the change trend of each parameter, and identifying the current change state; dynamically calculating the weight of each parameter in comprehensive evaluation according to the change trend and historical stability of each parameter; multiplying each parameter by the corresponding dynamic weight, and then carrying out weighted summation to obtain a current comprehensive safety index; and comparing the comprehensive safety index with a preset threshold value, judging a current safety state and triggering a corresponding early warning level. According to the method, a dynamic weight distribution mechanism is introduced, so that the evaluation model can adapt to changes of different construction stages and geological conditions, the accuracy of safety state judgment is remarkably improved, and the occurrence of misinformation and missing report conditions is reduced.