26 results about "Risk element" patented technology

The invention discloses an unmanned aerial vehicle crossing risk avoidance training system based on AI behavior prediction, and relates to the technical field of unmanned aerial vehicles, and the system comprises an unmanned aerial vehicle behavior prediction module which predicts obstacles, weather changes and other sudden risks possibly encountered by an unmanned aerial vehicle in the flight process based on the flight path, environment information and sensor data of the unmanned aerial vehicle; the risk evasion strategy generation module is used for generating a real-time evasion strategy according to the predicted risk information; the training data acquisition module is used for collecting flight data of the unmanned aerial vehicle in different flight environments as training samples; and the risk avoidance training module is used for training the unmanned aerial vehicle through a reinforcement learning model based on the collected flight data and the generated risk avoidance strategy. According to the method, obstacles, weather changes and sudden risks in flight are monitored in real time through AI behavior prediction, intelligent evaluation and sorting are performed in combination with multiple risk factors, coping strategies are optimized, and it is guaranteed that the unmanned aerial vehicle safely and efficiently executes tasks under various conditions.

The invention belongs to the field of natural language processing, particularly relates to an aviation safety accident report analysis method based on topic modeling and a word co-occurrence network, and aims to solve the problems that an existing topic modeling method is limited in understanding ability in aviation safety accident text analysis and cannot quantitatively reveal key causes. The method comprises the steps that a report text is acquired and preprocessed; based on a preset semantic fusion enhanced topic modeling engine keyword, obtaining a semantic fusion enhanced feature vector, and extracting a topic structure by adopting an improved deep embedding clustering model; and constructing an aviation safety accident word co-occurrence network based on a dynamic cosine similarity threshold value, performing network analysis, and determining key risk factors. According to the method, structured topic mining and deep learning semantic extraction are dynamically fused, and a network analysis method is combined, so that deep analysis and risk identification of the aviation safety accident report are realized, and the limitation of a traditional method on capturing text deep semantic information and subtle semantic difference is broken through.

The invention provides a road dangerous chemical transportation system risk coupling assessment method based on FRAM-ISM-NK, and belongs to the technical field of road transportation safety. Comprising the following steps: identifying functional composition of a dangerous chemical transportation system based on a functional resonance analysis method (FRAM), determining risk functional elements of three core stages of transportation preparation, accident occurrence and emergency rescue, and analyzing variability; constructing a hierarchical structure relationship of risk function elements of each stage based on an interpretation structure model (ISM), and determining an action hierarchical relationship and weight between the elements; quantifying a coupling relationship between risk function elements in different stages by combining an NK model, and revealing a risk coupling mechanism; and based on the analysis result, establishing a risk transfer quantitative model, and identifying key risk function elements and risk control paths in each stage. The system depicts the multi-stage risk factor coupling relation of road hazardous chemical substance transportation, quantitatively identifies the risk transmission path and the key control link, and provides effective technical support for improving the safety of the transportation system.

The invention discloses an unmanned aerial vehicle path planning and risk prediction method and system based on deep learning, and a medium, and the method comprises the steps: obtaining a starting point and an end point of a flight task of an unmanned aerial vehicle, searching a historical path corresponding to the starting point and the end point, and carrying out the segmented planning, and obtaining a plurality of planning path segments, determining a current path section from the plurality of planned path sections; environment data corresponding to the current path segment is collected, the environment data is processed based on a preset deep neural network model, and risk factors of the current path segment are predicted; and correcting the current path segment according to the risk factors, and controlling the unmanned aerial vehicle to fly based on the corrected current path segment. According to the invention, the historical path is divided into the plurality of planning path sections, the corresponding environment data is acquired for each planning path section, the risk factor is predicted by the preset deep neural network model, and the current path section is corrected according to the predicted risk factor, so that the risk prediction capability and the path dynamic re-planning capability of the unmanned aerial vehicle are improved.

The application discloses a water resource system risk multi-link conduction method, mainly comprising the following steps. Firstly, risk element data in a researched area are collected and standardized processing is conducted; then, water resource system risk links are constructed by using the correlation between risk elements; then, a risk conduction mechanism is defined: ①risk opening stage: simple mechanism of risk conduction and opening condition of risk are described; ②resistance threshold effect: the resistance ability of a system in risk conduction is described, according to the conduction characteristics of the risk link, the risk conduction mechanism and the resistance threshold effect of risk links with different structures are revealed; ③recovery threshold effect: natural and social repair forces make the risk not exist all the time, therefore, the risk recovery effect is defined; ④threshold value updating calculation: the resistance and recovery abilities are increased by risk prevention and control, therefore, the threshold value is updated; ⑤risk termination stage: the risk termination condition is deduced according to the risk conduction mechanism; ⑥risk intensity and conduction speed relationship: the greater the risk disturbance intensity is, the faster the risk conduction speed is. By exploring the risk link conduction mechanism, the water resource risk evaluation system under complex conditions is improved.

The invention provides a content security-oriented risk mining and replying method, equipment, a medium and a product. The content security-oriented risk mining method comprises the following steps: obtaining a problem sample to be subjected to content security risk identification from at least one data source, and obtaining multiple types of risk elements from a preset security knowledge base; constructing cue words based on the problem samples and the multiple types of risk elements, and inputting the cue words into a trained risk identification model to indicate the risk identification model to execute the following identification operations: identifying whether the problem samples hit any item in the multiple types of risk elements; if not, carrying out content security risk analysis on the problem sample to judge whether a new risk element exists or not; and if the risk identification model outputs a risk identification result with the new risk element, updating a risk identification process of the online reply system based on the new risk element in the risk identification result.

The invention discloses a low-altitude flight safety control method and device, computing equipment and a storage medium, the low-altitude flight safety control method is executed in the computing equipment, and the method comprises the following steps: based on a risk factor item, identifying a risk source existing in a flight task, the risk factor items comprise aircraft factors, personnel factors, environmental factors, airspace factors and management factors; calculating a risk score of each risk source by using a pre-constructed risk assessment model; the risk grade of the risk source is determined through the risk score, and the risk grade comprises high risk, medium risk or low risk; and selecting a corresponding flight strategy according to the risk level, and controlling the aircraft to execute the flight strategy. According to the method, the risk quantification model is constructed, a digital basis is provided for the feasibility of a flight task, and a powerful guarantee is provided for flight safety.

This invention discloses a method for runway landing event risk analysis based on association rules and complex networks, comprising the following steps: extracting risk factor keywords from runway landing accident investigation reports using Jieba word segmentation technology; constructing a runway landing event risk factor dataset based on the risk factor keywords; performing data mining processing on the runway landing event risk factor dataset using an association rule mining algorithm to obtain frequent itemsets and association rules; constructing a complex network based on frequent itemsets and association rules; the nodes of the complex network are frequent itemsets, and the edges of the complex network are the association rules; determining key risk nodes based on node degree and node centrality in the complex network, and marking them as key risk factors for runway landing events; and implementing prevention and control measures based on the key risk factors. This invention combines association rule mining and complex networks to analyze key risk factors for runway landing events and quantitatively calculates and analyzes the criticality of risk nodes.

The invention discloses a flight area non-stop construction data management method and system and a medium, and relates to the field of airport construction management.The flight area non-stop construction data management method comprises the steps that a dynamic Bayesian network model is introduced into flight area construction risk management, and by dividing construction time slices and establishing the time sequence dependency relationship between risk factor nodes and target risk nodes, the risk factor nodes are constructed; the accurate modeling of the dynamic evolution of the risk in the whole construction process is realized; by means of the diagnostic reasoning technology, prediction of the occurrence probability of different risk factors in each construction stage is achieved, and a visual risk distribution thermodynamic diagram is generated, so that risk management is converted into a new mode of intelligence, dynamic prediction and accurate intervention from traditional manual patrol, static evaluation and post response, and the risk management efficiency is improved. The accuracy of risk identification, the timeliness of early warning and the effectiveness of management measures are remarkably improved, and the safety and the high efficiency of airport operation in the non-stop construction period are fundamentally guaranteed.

Risk element information indicating risk elements is acquired. An asset capable of becoming a fault due to a risk element indicated in the risk element information, and a fault probability that the asset becomes the fault are specified based on static configuration information. A risk model in which the asset capable of becoming the fault and the fault probability are associated is generated in advance. In response to a designated input, an one asset to be evaluated is specified as an evaluation target asset, based on the designated input. A risk model related to the evaluation target asset is specified. A risk evaluation value being an index indicating a risk of the evaluation target asset is calculated based on the fault probability of the evaluation target asset and the static configuration information. The risk evaluation value of the evaluation target asset is associated with the asset of the risk model.

The invention belongs to the technical field of airline operation safety assessment and risk prevention and control, and provides a unit dispatching risk prevention and control method and system, and the technical scheme is as follows: obtaining various historical data related to unsafe events generated during the operation of each airline; analyzing internal laws and relevance of the various types of preprocessed historical data to obtain an index system of key risk factors and risk links which influence the operation safety of the air route; aiming at key risk factors and risk links which influence the flight line operation safety, corresponding risk prompt standards, response measure training standards, dispatching cooperation standards and technical defect training standards are formulated; and analyzing the actual route based on the constructed index system of the key risk factors and the risk links influencing the route operation safety to obtain a risk prompt, coping measure training, dispatching cooperation and technical defect training scheme in the scene. And the operation safety of the whole route is improved.

This invention discloses a method, system, and medium for managing construction data during non-stop operation in the airfield, relating to the field of airport construction management. This invention introduces a dynamic Bayesian network model into airfield construction risk management. By dividing construction time slices and establishing temporal dependencies between risk factor nodes and target risk nodes, it achieves accurate modeling of the dynamic evolution of risks throughout the entire construction process. Utilizing diagnostic reasoning technology, it predicts the probability of occurrence of different risk factors at each construction stage and generates an intuitive risk distribution heatmap. This transforms risk management from traditional manual inspections, static assessments, and post-event responses into a new model of intelligent, dynamic prediction, and precise intervention, significantly improving the accuracy of risk identification, the timeliness of early warnings, and the effectiveness of management measures, fundamentally ensuring the safety and efficiency of airport operations during non-stop construction.

The application provides an airport operation risk time sequence prediction system based on a graph neural network, comprising: a data acquisition module, which acquires airport operation risk and operation situation data, cleans the data, and divides the data into time sequence data according to time periods; a graph structure construction module, which calculates the correlation between risks based on the risk time sequence data, and forms a directed acyclic graph structure with various risks as nodes; a weight generation module, which calculates the correlation of associated nodes with the risk nodes as the center, and generates edge weights according to the strength of the correlation; and a risk prediction module, which processes the graph structure data formed by the risk nodes by using a graph neural network, and realizes the prediction of the risk values of the risk nodes at future moments. The application solves the problem that the coupling relationship of airport operation risks is complex and difficult to accurately predict, fully considers the coupling and correlation relationship of risk factors, and improves the accuracy of risk prediction.

This invention provides a method and system for aviation emergency rescue risk assessment based on a large model, belonging to the field of aviation safety management technology. It addresses the limitations of existing technologies, such as strong subjectivity in assessment, insufficient multi-source data fusion analysis, and weak adaptability to extreme conditions. The method involves acquiring multi-source heterogeneous data associated with the flight to be assessed, fusing it to generate a standardized feature vector. This feature vector is then input into a pre-deployed risk assessment model, which is collaboratively constructed by a lightweight large model and a multi-factor dynamic fusion algorithm. The multi-factor dynamic fusion algorithm dynamically adjusts the weight coefficients of each risk factor based on real-time input, and then calls the lightweight large model to perform inference analysis on the correlation between risk factors, calculating the risk assessment result for the single flight. This invention significantly enhances the timeliness, safety, and overall effectiveness of rescue operations.

The invention provides a cast-in-situ bored pile construction risk analysis method and system, and belongs to the technical field of risk identification, and the method comprises the following steps: determining a top event of a cast-in-situ bored pile construction risk by using an accident tree analysis method, analyzing an intermediate event and a basic event of an accident according to the top event, and constructing an accident tree; solving a minimum cut set of top events in the accident tree by using a Boolean algebraic method; drawing a dual tree of the accident tree by using duality of the minimum path set and the minimum cut set, and solving the accident tree by using a Boolean algebraic method to obtain the minimum path set of the top event; judging the structural importance degree of the basic event to the top event according to the occurrence frequency of the basic event in the minimum cut set or the minimum path set; and judging the risk factor of the top event through the minimum cut set, the minimum path set or the structural importance degree. According to the invention, the occurrence of accidents can be comprehensively analyzed, and prevention measures with strong pertinence are provided for avoiding the occurrence of accidents.

The invention discloses a risk early warning and inducement identification method and system for an aircraft runway rushing / deviating event. The method comprises the steps that S0, a key parameter set and a risk factor set of the runway rushing / deviating event are obtained through FOQA standards, SOP and QRH; s1, constructing a corresponding relation between key parameters and risk factors based on a Delphi method; s2, constructing a runway running and deviating risk prediction model based on Transform, and realizing online risk identification; and S3, obtaining the most similar inducement based on a hypothesis testing method, and completing risk factor identification.

The invention is suitable for the technical field of parachute backup, and particularly relates to a parachute-based unmanned aerial vehicle dual-backup redundancy safety control method and system, and the method comprises the steps: delimiting the flight range of an unmanned aerial vehicle, recognizing risk factors, configuring corresponding weights, collecting the real-time values of the risk factors, integrating the weights and the real-time values, and obtaining the unmanned aerial vehicle dual-backup redundancy safety control system. A risk value is calculated, the risk value is segmented into a plurality of segments, and each segment corresponds to a countdown length; the countdown length is uploaded to a timer deployed in the unmanned aerial vehicle in advance, the timer is started, set time is selected from the countdown length, and a self-inspection signal is generated and sent to a main control system when the set time comes; by activating the standby system and stopping the main system, the stability and reliability of taking over of the standby system can be guaranteed, fault diffusion is avoided, unmanned aerial vehicle redundancy safety control is effectively achieved, and the safety of the unmanned aerial vehicle is greatly improved.

The invention belongs to the field of over-limit event risk analysis, and particularly discloses a flight training over-limit event relevance risk analysis method, system and device and a storage medium, and the method comprises the following steps: S1, pre-processing over-limit event data; s2, WGAN-GP data enhancement is carried out; s3, clustering interval division is carried out; and S4, correlation analysis and visualization are carried out. According to the invention, an improved WGAN-GP model is introduced to carry out data enhancement on flight features, and a training process is stabilized through a gradient penalty mechanism, so that the generated samples approach real data in distribution. According to the method, the variational Bayesian Gaussian mixture model is adopted to replace a traditional K-Means method, the optimal clustering number is automatically determined, and parameter interval label division is achieved. According to the method, mining results are constructed into a knowledge graph, structural expression among risk factors, flight states and overrun events is achieved, core risk nodes and key risk paths can be visually recognized through visual analysis, data analysis efficiency and result interpretability are greatly improved, and visual support is provided for flight safety decision making.

The specification provides a content security-oriented risk mining and answering method, device, medium and product. The content security-oriented risk mining method comprises: obtaining a question sample to be subjected to content security risk identification from at least one data source, and obtaining a plurality of risk elements from a preset security knowledge base; constructing a prompt word based on the question sample and the plurality of risk elements, and inputting the prompt word into a trained risk identification model to instruct the risk identification model to perform the following identification operation: identifying whether the question sample hits any of the plurality of risk elements; if not, performing content security risk analysis on the question sample to determine whether there is a new risk element; and if the risk identification model outputs a risk identification result that there is a new risk element, updating a risk identification process of an online answering system based on the new risk element in the risk identification result.

The application belongs to the technical field of data security, and discloses a risk element dynamic identification method and system based on a knowledge base updating mechanism, which comprises the following steps: formally describing a data cross-border flow business process, and extracting knowledge data involved in the business process; coding the knowledge data to form structured information, and storing the structured information into a knowledge base; performing step-by-step coding and concept refining on the structured information in the knowledge base, screening out a core category and other categories, and an association relationship between the core category and the other categories; and identifying risk elements in the data cross-border flow business process according to the association relationship. The application can realize continuous improvement and intelligent identification of data cross-border risk elements, thereby improving the completeness and accuracy of risk identification, and enhancing the overall efficiency of data cross-border security management.

The application discloses a kind of aircraft rush / cause runway event risk early warning and cause identification method and system, method includes: S0: by FOQA standard, SOP and QRH obtains the key parameter set and risk factor set of rush cause runway event;S1: based on the correspondence of key parameters and risk factors is constructed by Delphi method;S2: construct the rush cause runway risk prediction model based on Transformer, realize online risk identification;S3: based on hypothesis testing method obtains the most similar cause, completes risk factor identification.

The present invention discloses a digital generation method, system and medium for risk management of substation maintenance tasks, and belongs to the technical field of digital generation of risk management measures. The present invention is based on a text-enhanced risk factor causal relationship extraction method to obtain the causal relationship of the evolution of substation operation and maintenance task accidents, and construct a risk accident knowledge map for operation and maintenance tasks; according to the maintenance plan, key safety measures information is obtained, and a work ticket language rule base is established using a correlation analysis method; historical work tickets are obtained to establish a historical work ticket knowledge base; based on the risk accident knowledge map, the work ticket language rule base and the historical work ticket knowledge base, safety measures are automatically generated using a text similarity TF-IDF model. The present invention utilizes the risk accident knowledge map of operation and maintenance tasks, combined with the work ticket language rule base and the historical work ticket knowledge base, to automatically generate risk pre-control points for maintenance tasks, thereby improving the pertinence and scientific nature of the formulation of safety measures management and control plans.

The invention provides a shipping accident risk identification and evaluation method based on an enhanced rough BWM evaluation method. The method comprises the following steps: constructing an identification prediction model by using a Bayesian network based on a neural network; determining the weight of a shipping accident risk factor through decision making; carrying out weight improvement by utilizing ROWA to obtain a weight coefficient of the shipping accident risk factor; constructing a rough decision matrix based on an elastic approximation factor, and evaluating shipping accident risk factors; and determining the relative weight of each shipping accident risk factor by adopting an aggregation evaluation method, converting the risk factors into numerical values, and arranging and grading the risk factors according to the numerical values. According to the method, a plurality of shipping accident risk factors are selected, the priority ranking problem under different standards is researched, a risk identification and evaluation method is fused, common risk factors in the shipping industry are deeply discussed, and effective evasion strategies are provided for different industries, so that the scientificity and practicability of risk management are improved.

The present invention relates to a risk assessment method for cruise ship glass curtain walls based on ordered comprehensive weighted FMEA, comprising the following steps: identifying failure modes of cruise ship glass curtain walls; obtaining risk factor and failure mode evaluations from an expert team, and converting the experts' subjective evaluations of risk factors and failure modes using intuitionistic fuzzy numbers; aggregating subjective scores of failure modes and subjective weights of risk factors using an intuitionistic fuzzy weighted average operator; using the maximum value of the subjective score of the failure mode as a positive reference sequence; using the minimum value of the subjective score as a negative reference sequence; calculating ordered comprehensive weights; calculating the grey correlation degree of the failure mode; and ranking the risk priorities of the failure modes.

The invention provides a risk assessment system and method for low-altitude flight based on a risk assessment model, and relates to the technical field of traffic management. The system comprises a model management module which is used for establishing a new risk assessment model according to a model name and an application service range, and managing the newly established risk assessment model and a historically established risk assessment model; the model configuration module is used for displaying a plurality of preset risk categories and risk factors thereof to the user based on the model name and the application service range, configuring a newly built risk assessment model according to the selection and / or input of the user, and sending the configured risk assessment model to the model management module; and the risk assessment module is used for acquiring a target risk assessment model selected by a user from the model management module according to the business type of the to-be-assessed flight application, and performing risk assessment on the to-be-assessed flight application based on the target risk assessment model, thereby realizing low-altitude flight risk assessment with good adaptability and good flexibility.

This disclosure provides a method for controlling a hoisting mechanism used to lift an aircraft in near space, comprising: setting the ultimate load that the aircraft must meet under target operating conditions based on risk factors that may cause abnormalities in the aircraft; establishing a finite element model to characterize the aircraft to be lifted and the hoisting mechanism used to lift the aircraft, and using the ultimate load as the excitation input of the finite element model to calculate the mechanical simulation under the target operating conditions to obtain displacement simulation data of the aircraft under the target operating conditions; manufacturing a solid scale-down model including the aircraft and the hoisting mechanism; adjusting the hoisting rope structure with the structural strength of the solid scale-down model of the aircraft as the limit, so that the solid scale-down model of the aircraft can switch between multiple target operating conditions, and constructing the allowable force envelope of each hoisting rope of the hoisting rope structure; controlling the hoisting mechanism carrying the aircraft based on the allowable force envelope of each hoisting rope, so that the aircraft can switch between multiple target operating conditions, from the lifting stage to the release stage.