128 results about "Safety index" patented technology

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 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 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 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 relates to the technical field of power system operation and control, in particular to a safety operation and maintenance scheduling device and method for a wind, light and storage integrated system, and the device comprises a field sensing unit which is used for collecting the real-time operation data of wind, light and storage equipment; the edge calculation and autonomous control unit is used for local data processing, short-time prediction and emergency control when communication is abnormal or a safety index is out of limit; the central optimization and safety management platform is used for fusing and standardizing the data, evaluating the safety state of the system based on a double-domain safety redundancy index, evaluating energy storage life consumption through a coupling model, generating a scheduling instruction considering economical efficiency, safety and life by adopting opportunity constraint optimization, and sending the scheduling instruction to the central optimization and safety management platform; security verification and stable execution of the instruction are guaranteed through shadow simulation, strategy progressive switching and a security instruction shielding module; and the man-machine interaction application unit is used for visual monitoring, strategy recommendation and history redisk.

The utility model aims to provide a pet water dispenser which mainly comprises a water storage device, a heating device for heating water stored in the water storage device, a cooling device arranged above the water storage device, a water drinking tray arranged below the cooling device and the like. Wherein the stored water is heated by the heating device to form water vapor, and when the water vapor flows upwards through the cooling device, the water vapor is cooled to form condensed water, and then the condensed water falls back into the drinking water tray. Compared with the prior art, the pet water dispenser provided by the utility model has the advantage that the safety index of drinking water is improved.

The invention discloses a curve tunnel personalized lane change early warning system and method considering different speed conditions. The method comprises the following steps: collecting multi-source data; driving behavior characteristics are extracted based on the obtained data, and drivers are divided into a rash type, a stable type and a conservative type; establishing a nonlinear basic safety distance model under different speed intervals and different relative speed intervals of the two vehicles, and dynamically correcting a formula of the basic safety distance model in combination with a slope angle and a curve radius to obtain a three-dimensional safety model; generating an early warning rule in combination with the TTC threshold value and the safe distance model; and graded real-time early warning is conducted, specifically, graded early warning is triggered according to the safety indexes and early warning rules matched with the current situation, and early warning comprises visual prompt, sound warning and active braking intervention adopted according to the driving style. According to the method, driving style classification and a dynamic curvature-slope compensation algorithm are fused, and accurate lane changing early warning under different speed conditions in the curve tunnel is achieved.

The invention relates to a cyberspace security protection capability index measurement method, apparatus and device, and a medium. The method comprises the steps of calculating a static protection strength reference based on historical data and an index system; then collecting and analyzing real-time threat intelligence, and quantizing to generate an attacker intention vector; inputting the static protection intensity reference and the attacker intention vector into a non-cooperative game model for deduction, simulating attack-defense interaction and solving an equilibrium strategy, thereby identifying a protection vulnerability set and obtaining an optimal defense strategy suggestion; dynamically correcting the membership degree of each safety index according to the vulnerability point and the intention of an attacker; and fusing the dynamic membership degree and the optimal defense strategy, and performing weighted synthesis to obtain a final safety protection capability index. According to the method, the prospective and dynamic measurement of the network security protection capability is realized by introducing attacker intention analysis and game deduction.

The embodiment of the invention discloses a safety assessment method and device for a receiving station, equipment and a medium, and the method comprises the steps: determining a risk event according to a hazard source in a to-be-assessed receiving station, and enabling the risk event to represent a hazard source out-of-control event; determining a safety index of the to-be-evaluated receiving station according to historical data associated with the risk event of other receiving stations and the technological process of the to-be-evaluated receiving station; a comprehensive influence matrix is determined according to the index scores of the safety indexes, a safety evaluation graph of the to-be-evaluated receiving station is determined based on the comprehensive influence matrix, and the safety evaluation graph is used for investigation operation of risk events of the receiving station and characterization of the causal relationship and the hierarchy of the safety indexes. Based on the safety assessment diagram provided by the embodiment of the invention, accident cause factors, causal relationships and levels can be determined, the accuracy of risk disposal can be improved, and meanwhile, through the safety indexes and the safety assessment diagram, the accident occurrence mechanism can be known, the accident prevention capability can be improved, and the occurrence of accidents can be avoided.

The invention relates to a highway construction area risk situation perception method based on environmental perception, and the method specifically comprises the following steps: deploying a multi-source sensing equipment network along a highway construction area and at a key structure position to collect data, and marking the data; constructing a highway construction area risk situation awareness model, training the model, and jointly optimizing all trainable parameters of the model through back propagation to obtain a trained model; and deploying the trained model to a construction area edge computing platform, outputting a final prediction result through the trained model according to data acquired in real time, judging whether to perform early warning or not according to the prediction result, calibrating a risk position and a risk level, and continuously screening a high-confidence sample through a pseudo-tag self-enhancement module to perform fine adjustment on the model. According to the method, the model is constructed and trained, so that the construction risk can be predicted more quickly and more accurately, workers are timely guided to carry out emergency disposal, and the safety index in the construction process is improved.

The invention provides a small reservoir standardized operation management evaluation method and system and a storage medium, and the method comprises the steps: obtaining the historical operation data of at least one complete hydrological year of a to-be-evaluated reservoir, combining seasonal hydrological features and historical risk event records, and carrying out the clustering generation of a reference operation probability distribution model of two working conditions: a flood season and a non-flood season; calculating probability density or statistical distance to obtain a running state deviation degree characteristic value; updating a preset index sensitivity matrix, and multiplying the two matrixes to obtain the current weight of each basic evaluation index; setting respective punishment trigger thresholds according to the weights of three types of safety indexes, namely dam body seepage flow, structure displacement and inspection record abnormity; when the deviation degree characteristic values of any two types of indexes exceed the limit at two continuous time points, generating a penalty score through a function; and calculating a three-dimensional initial evaluation score of structure safety, scheduling specification and data completeness, deducting a penalty score from the structure safety initial score, and combining the penalty score with the other two types of scores to obtain a multi-dimensional operation management evaluation value.

The invention discloses a power distribution network operation safety assessment method, system and device and a storage medium, and relates to the technical field of power distribution network safety operation and renewable energy access assessment, and the method comprises the steps: collecting the multi-source operation data, random noise and condition vectors of a power distribution network, constructing an assessment guide condition according to the multi-source operation data of the power distribution network, and carrying out the assessment of the power distribution network. Constructing an adversarial network model by evaluating guide conditions; inputting the random noise and the condition vector into the adversarial network model, and outputting a photovoltaic and load combined output sample; performing simulation calculation on the joint output sample, and generating a safety index set and an economic index set according to a simulation calculation result; and performing comprehensive risk calculation by taking the safety index set and the economic index set as input, and outputting a safety assessment report. The method provided by the invention has better effects in the aspects of multi-source data fusion processing, extreme working condition simulation generation, risk quantification and closed-loop feedback.

This invention relates to a maintenance and testing device for an aircraft air conditioning system. An engine testing module emits sound waves, which are detected by a piping testing module. The collected signals are analyzed and processed in the main controller to obtain a diagnostic result regarding whether the air conditioning system is damaged. It can accurately determine the physical health status of the air conditioning system and accurately identify any physical damage, with high accuracy and a simple testing method. Simultaneously, it detects temperature parameters at the engine, piping, and cockpit to determine the air conditioning system's operational health index. By detecting the air composition in the cockpit during air conditioning operation, it can detect the cockpit safety index. It can effectively analyze the air conditioning's operating efficiency and the cockpit safety index, ensuring that aircraft that pass the inspection will not pose a danger during missions.

The invention provides a multi-modal data-based driver post suitability evaluation method, an edge computing device and a medium. The method comprises the steps of obtaining behavior data, physiological data and vehicle driving data of a driver in a simulated driving environment; on the basis of the behavior data, the physiological data and the vehicle driving data, post suitability evaluation indexes of the driver are calculated, and the post suitability evaluation indexes at least comprise a safety index, an attention stability index and a physiological anti-fatigue ability index; and determining the post suitability level of the driver based on the post suitability evaluation index. The method can reduce the evaluation cost of the driver post suitability and improve the accuracy of the evaluation result.

The invention discloses a positive pressure type anti-explosion industrial robot intelligent control method and device and a medium, and relates to the technical field of industrial robot positive pressure anti-explosion control. Calculating the positive pressure safety index of each grid point according to the grid point division of the working area, and carrying out grading marking to obtain a positive pressure safety grid; converting the positive pressure safety grid into a positive pressure safety guide graph which takes grid points as nodes and determines side cost according to a positive pressure safety index between adjacent grid points, and obtaining a path grid point sequence which has the minimum cost and avoids dangerous grid point nodes on the positive pressure safety guide graph through path search; and determining a ventilation gear and a speed gear in a grading manner according to the positive pressure safety index of each grid point in the path grid point sequence, converting the ventilation gear and the speed gear into a continuously executed robot motion instruction and a positive pressure ventilation control instruction, and forming an operation record. And the anti-explosion safety of the positive pressure type anti-explosion industrial robot is improved.

The invention provides a comprehensive security index evaluation method based on multi-task learning, and belongs to the technical field of network security management, and the method comprises the steps: synchronously collecting original data from a plurality of heterogeneous security data sources; a multi-task learning neural network model is constructed, and the model comprises a shared feature coding network for outputting shared features; the at least two heterogeneous task decoding networks are used for respectively mapping the shared features into preliminary security assessment results with different attributes; the fusion network is used for carrying out weighted fusion on the preliminary security assessment result to generate a comprehensive security index; using the multi-source security data set to train the multi-task learning neural network model; and inputting real-time security data of a to-be-evaluated system into the trained model, and outputting a comprehensive security index. The method has the advantages that the shared feature coding network can extract high-level feature representations with commonality from different data sources, and feature engineering dependence for a single data source or a single task is avoided.

The application discloses a kind of path planning systems of dike breach rescue equipment, comprising: environment perception module, for obtaining environmental data and position information, constructs rescue environment digital model;Path planning model is used to determine path planning based on rescue environment digital model;Path planning model is strategy network and safety prediction network coupling reward function, fusion reliability safety index component intelligent agent decision model, is trained based on PPO algorithm and is constructed generation;Wherein, strategy network is used to obtain shared features, to safety prediction network output action mean, according to total reward determines the path trajectory of equipment target;Interaction module is used to output real-time path planning result, risk probability and safety classification.According to the above technical scheme, the dynamic coupling of geological bearing capacity, water flow power, boat bridge stability and equipment dynamics is comprehensively considered, and global safety constraint is formed;Ensure the transportation safety of equipment in complex environment, greatly improve the efficiency of rescue.

The invention belongs to the technical field of intelligent manufacturing and industrial robots, and particularly relates to a battery assembly robot grabbing method based on deep learning, and the method comprises the steps: constructing a physical support map based on three-dimensional point cloud data, and calculating the metastable state support index of each battery to evaluate the collapse risk; a dynamic shearing safety index is calculated by combining robot kinematics and battery physical attributes, and a grabbed object is prevented from slipping in high-speed movement; the global disclosure gain is evaluated by simulating the grabbing action, the grabbing sequence is optimized, and follow-up grabbing difficulty is avoided; through a multi-target optimization strategy, on the premise of ensuring physical stability and dynamic safety, an optimal grabbing target capable of maximizing subsequent operation convenience is selected. The safety, the efficiency and the intelligent level in the battery grabbing process are improved.

The embodiment of the invention provides an evaluation method, system and device for the lane changing effect of an intelligent driving heavy truck and a medium, and belongs to the field of automotive electronics and intelligent driving. The method comprises the following steps: reading a steering lamp state signal, real-time lane position information and rear axle coordinate information, and determining a lane changing scene; calculating a target lane obstacle time distance, a weather coefficient, a road condition coefficient and a special vehicle coefficient to obtain a risk score, and dividing risk levels according to the risk score; setting a safety index and a comfort index; adjusting the weights of the safety index and the comfort index according to the risk level, calculating the score of the safety index and the score of the comfort index, and obtaining the comprehensive score of the lane change evaluation through weighted summation. The evaluation reference dimension is increased, and the accuracy and scene adaptability of heavy truck lane change evaluation are improved. According to the characteristics of the heavy truck, a multi-dimensional coefficient is introduced, safety and comfort weights are adjusted based on the risk degree, differentiated requirements of different scenes are accurately adapted, and high-precision matching of evaluation and operation scenes is realized.

The application discloses a train strong wind adjustment control method and device, medium and train, relates to the field of rail trains, and solves the problem of low real-time performance of monitoring environmental strong wind changes. A vehicle-mounted wind measuring device is used to monitor the wind field in a certain range in front of the running direction of the train, and the real-time monitoring of the whole global continuous wind field around the train, including the wind speed and direction, obtains safety indexes such as the derailment coefficient and the wheel load reduction rate of a section of line in front, and is used for finding the strong wind condition that may affect the driving safety of the vehicle in advance, guiding the train to evaluate the maximum running speed according to the wind field near the line, and improving the line operation efficiency.

The invention relates to a method and computer program product for providing a neural network and system for classifying objects for a vehicle. The method according to the invention comprises the steps of providing a hazard and risk matrix for objects to be classified (S10), determining and assigning a safety weight to a misclassification depending on a risk rating (S20), determining and assigning a training weight to a misclassification (S30), and training a neural network by means of a plurality of iterations (S30). In this process, assigned training weights are dynamically adjusted to optimize a safety index, and the safety index is determined depending on misclassifications and assigned safety weights.

The invention discloses a method for improving the servicing safety of a new energy automobile, electronic equipment and a storage medium. The method comprises the following steps: acquiring monitoring data acquired by any monitoring device in a plurality of monitoring devices; based on the monitoring data, evaluating a target area corresponding to the monitoring data to obtain a security evaluation result of the target area; and under the condition that the safety evaluation result represents that the charging safety index does not meet the preset condition, outputting target alarm information based on the monitoring data and the safety evaluation result. According to the invention, the technical problem of poor monitoring timeliness of potential safety hazards in the new energy automobile servicing process in the prior art is solved.

The invention relates to the technical field of dynamic and static tank monitoring, solves the technical defect that the prior art cannot adapt to dynamic change of working conditions and lacks differential monitoring logic of dynamic and static tanks, and particularly relates to a dynamic and static tank monitoring alarm function implementation method based on a DCS (Distributed Control System). A plurality of operation variables are set, a timing function module is initialized, the set values of the operation variables are associated, the current liquid level value in the storage tank is collected in real time and preprocessed, the current liquid level value is latched at a preset period node, the current monitoring reference value of the liquid level is dynamically calibrated, and the absolute deviation value of the liquid level is calculated. The limitation of a traditional independent monitoring framework can be broken through, the hardware complexity and deployment cost of the system can be greatly simplified, meanwhile, the requirements of two opposite working conditions can be flexibly met, the universality of the system in a multi-type storage tank scene is improved, real-time monitoring and abnormal alarm prompting of dynamic and static tank liquid level deviation of the storage tank are achieved, and safety accidents caused by human negligence are avoided; the safety index is improved.