27 results about "Risk ranking" patented technology

The invention discloses a virtual simulation and security evaluation method and system based on a network security target range, and the method comprises the steps: constructing a network asset knowledge graph for describing a virtualized target range environment through information collection; secondly, based on an attack strategy grammar rule base, adopting a Monte Carlo Tree Search (MCTS) algorithm to carry out intelligent attack simulation on the knowledge graph so as to discover a nonlinear and multi-stage attack path; thirdly, constructing a Bayesian attack graph (BAG) based on the knowledge graph, and performing probabilistic and systematic quantitative evaluation on the security risk of each asset node in the network through Bayesian reasoning; and finally, integrating the attack path and the quantitative risk value, and generating a comprehensive assessment report containing a visual path, a risk sequence and a reinforcement suggestion. According to the target range simulation method and device, the problems that existing target range simulation is insufficient in confrontation authenticity, one-sided in safety evaluation and lack of predictability are solved by combining the strategy simulation of the MCTS and the global quantitative analysis of the BAG.

The invention provides a coastal oilfield produced water conveying pipeline safety risk evaluation method and system, and the system comprises an online monitoring system which is used for obtaining pipeline parameters, and calculating a pipeline safety risk index R according to the pipeline parameters; d is the pipeline damage probability, and ML is the maximum leakage quality; the larger the pipeline safety risk index R is, the higher the pipeline risk is. The online monitoring system is used for collecting operation data of the pipeline, risk calculation and judgment are carried out in combination with pipeline design and operation parameters, an equipment risk sequence is obtained and used for guiding formulation of an inspection plan, and the method has the advantages of being good in real-time performance, high in efficiency and feasible in field calculation.

The invention relates to a Delphi consensus-based non-negative ridge regression risk sorting method and system, and the method comprises the following steps: 1) collecting expert scoring data based on a Delphi method, and constructing a scoring matrix containing post labels; 2) adopting K-means clustering in combination with nonparametric test, and extracting score divergence features; 3) constructing a sorting target variable, and introducing Gaussian disturbance to simulate score uncertainty; and 4) performing ridge regression modeling with non-negative constraint to obtain the sorting weight of each risk factor. According to the method, the stability and the organization suitability of a sorting result are improved, and the method is applied to a risk assessment scene in a complex engineering environment; the system is used for implementing the method. According to the method, the stability and organization suitability of the sorting result are improved, and the method is applied to a risk assessment scene in a complex engineering environment.

The application provides a power grid dispatching control software verification method and device for a disturbance scene. It belongs to the technical field of power dispatching automation. The method comprises the following steps: generating a disturbance verification scene based on verification requirements; running the power grid dispatching control software to be verified under the disturbance verification scene, and performing response simulation on the power grid dispatching control software to be verified through a power grid and a device response simulation environment to form a closed-loop interaction process between the power grid dispatching control software to be verified and the power grid and the device response simulation environment; collecting data generated in the interaction process to form a standardized data set; reconstructing a propagation path of the disturbance in a closed-loop control chain of the power grid dispatching control software to be verified based on the standardized data set to obtain a path analysis result; calculating evaluation indexes, and outputting a risk ranking result and an abnormality attribution conclusion according to the path analysis result and the evaluation indexes. The method improves the systematicness and risk positioning capability of the power grid dispatching control software verification under complex disturbance conditions.

The invention relates to the technical field of medical diagnosis assistance, and discloses an ear-nose-throat endoscope intelligent diagnosis assistance system based on AI image recognition, and the system comprises an image collection module which is used for receiving and preprocessing video stream data from an electronic nasopharyngolaryngoscope or an otoscope in real time; the process quality monitoring module is used for analyzing the video stream data in real time so as to judge the motion state and the image definition of the endoscope; the focus recognition and marking module is internally provided with a trained AI image recognition model, image quality is guaranteed through preprocessing of the AI image acquisition module, and inspection operation is standardized through the process quality monitoring module; the AI model accurately identifies and classifies multiple types of lesions, warns and assists doctors in focusing key problems in real time, improves the detection rate of the lesions and reduces the risk of missed diagnosis; a risk sorting report is automatically generated and hospital uploading is supported, so that the burden of doctors is relieved and the efficiency is improved; and in combination with a feedback optimization model, long-term clinical adaptation is realized, and stable and intelligent assistance is provided for ear-nose-throat endoscope diagnosis.

This invention provides an improved FMEA device that considers the relationship between uncertainty and failure modes. It features the following components: an SVTraNN scoring conversion module for obtaining the SVTraNN evaluation matrix; an expert weight calculation module for calculating expert weights; an expert evaluation summarization module for calculating the SVTraNN decision matrix; a subjective weight calculation module for calculating the final subjective weight of each risk factor; an objective weight calculation module for calculating the objective weight of each risk factor; a comprehensive weight calculation module for calculating the comprehensive weight of each risk factor; a comprehensive score calculation module for calculating the comprehensive score of each risk mode; a risk ranking module for obtaining the risk ranking result of each risk mode; and a risk level module for obtaining the risk level of each risk mode. In summary, this method can effectively assess the risk level of risk modes.

This invention relates to the field of power system technology. To address the problem that existing technologies cannot meet the requirements of conservative dispatching and risk ranking for sensitivity to tail risks, it provides a method and system for risk ranking of distribution network branches for high-penetration renewable energy sources. The method for risk ranking of distribution network branches for high-penetration renewable energy sources includes: constructing a steady-state closed-loop relationship considering electro-thermal coupling; establishing a load factor-failure rate mapping model based on renewable energy and load uncertainty models, while simultaneously considering the steady-state closed-loop relationship of electro-thermal coupling, to obtain the branch failure rate; constructing a joint optimization model of risk-aware minimum load shedding and network reconfiguration to obtain the accident consequence assessment result; constructing accident landing point weights based on the branch failure rate, and then calculating the EVAR (Effective Value Assigned) branch risk contribution and ranking each branch based on the accident consequence assessment result to obtain a list of high-risk branches. This method can achieve the screening and ranking of high-risk branches while maintaining the feasibility of the engineering process.

The application discloses a network security target-based virtual simulation and security evaluation method and system. First, a network asset knowledge graph describing a virtual target environment is constructed through information collection. Second, intelligent attack simulation is performed on the knowledge graph based on an attack strategy syntax rule library using a Monte Carlo tree search (MCTS) algorithm to discover nonlinear and multi-stage attack paths. Third, a Bayesian attack graph (BAG) is constructed based on the knowledge graph to probabilistically and systematically quantitatively evaluate the security risks of asset nodes in the network through Bayesian inference. Finally, the attack paths and quantitative risk values are integrated to generate a comprehensive evaluation report containing visualized paths, risk rankings and reinforcement suggestions. The application solves the problems of insufficient realism of existing target simulation, one-sided security evaluation and lack of predictability by combining strategic simulation of MCTS and global quantitative analysis of BAG.

This invention relates to the field of energy storage battery safety technology, and particularly to a method, apparatus, equipment, and computer-readable storage medium for risk assessment and fire protection configuration of energy storage battery compartments. The method includes: collecting and preprocessing operational data of the energy storage battery compartment; calculating a comprehensive risk score for each battery module based on the preprocessed operational data, and constructing a comprehensive risk ranking list based on the comprehensive risk scores; and dynamically configuring fire safety strategies and assessing heat distribution based on the comprehensive risk ranking list. By introducing a dynamic grouping mechanism based on health status and a spatiotemporal risk index, a spatial context risk index and a temporal dynamic enhancement factor are constructed, significantly improving the accuracy of fire risk assessment for non-uniformly aging battery compartments.

This invention relates to the field of logistics technology and discloses a statistical method for trunk line transportation, comprising: collecting trunk line transportation data; constructing a timeout risk prediction model by inputting influencing factor data and time characteristics into a trained timeout risk prediction model to obtain a timeout risk probability value; calculating the timeout rate by calculating the timeliness compliance rate based on the timeout risk probability value and calculating the predicted timeout rate of the route to generate a risk ranking; generating optimization strategies by automatically matching and generating differentiated dynamic optimization strategies for routes with different risk levels based on the predicted timeout rate and risk ranking; and outputting the strategies by outputting the generated dynamic optimization strategies in the form of specific optimization measures and suggestions. This invention achieves accurate quantitative assessment of transportation timeout risk, can automatically calculate the predicted timeout rate of the route and rank the risks, and generate and output dynamic optimization strategies; effectively improving the timeliness management level and risk response capability of trunk line transportation.

This invention discloses a large-scale grounding grid corrosion status assessment system based on big data analysis, comprising: a data acquisition module for collecting node resistance, potential, conductivity, humidity, and temperature, and recording time and node data; a data processing module for denoising, normalizing, and correcting data to generate a structured data matrix; a feature extraction module for extracting corrosion feature vectors based on historical records; a corrosion assessment module for probabilistic model inference and scoring to generate corrosion levels, risk matrices, and health trends; a corrosion distribution module for interpolation mapping to generate a full-network corrosion distribution map and marking high-risk nodes; a trend prediction module for simulating evolution to generate future corrosion level change curves and high-risk rankings; and a report generation module for generating a status assessment report. This invention utilizes big data analysis technology to achieve accurate corrosion assessment, risk visualization, and health trend prediction for grounding grids, improving maintenance decision-making efficiency.

This invention relates to a gas metering verification method based on big data, comprising: collecting historical gas consumption data and multi-source external information to obtain a gas consumption sequence and influencing factor dataset; processing the gas consumption sequence using a time series decomposition algorithm to obtain seasonal patterns and trend components; analyzing the influencing factor dataset based on the seasonal patterns and trend components to determine the fluctuation range of gas consumption and assess the probability of metering anomalies; weighting and adjusting the probability of metering anomalies to obtain a refining deviation signal list; and sorting high-risk metering points according to the refining deviation signal list to determine priority verification target sequences. This invention, through multi-level data processing and algorithm integration, achieves full-process automation from anomaly detection to risk ranking, effectively improving the accuracy and efficiency of gas metering anomaly identification and verification, and providing intelligent decision support for related fields.

The invention relates to the technical field of medical images, in particular to an accurate coronary artery lesion evaluation method based on image fusion, which comprises the following steps of: measuring a blood vessel angle radius coordinate through CT angiography to establish a structural map, calculating a blood flow proportion through MRI phase contrast imaging, analyzing an angle relation to obtain an abnormal indication, and evaluating the coronary artery lesion according to the abnormal indication. The method comprises the following steps: acquiring periodic marked blood flow by an echocardiogram, extracting an abnormal flow rate by CNN to obtain pathological features, calculating shear stress based on a radius, screening anomalies, evaluating a risk by combining a sorting position, matching the risk with a position threshold value, and judging a positive region to obtain a diagnosis result. Blood flow velocity vectors and branch angle laws are analyzed, cardiac cycles are marked to capture blood flow changes, abnormal flow velocity is identified through deep feature extraction, blood flow structure coupling is quantified, wall shear stress and risk sorting are jointly evaluated, narrow risk fine grading is achieved, evaluation accuracy and reliability are improved, and early detection is more excellent.

An object of a system according to an embodiment is to provide effective measures against nuisance calls and fraudulent calls.SOLUTION: A system includes an automatic response part, a database operation part, and a risk evaluation part. The automatic response unit automatically responds to the nuisance call by using the generated AI. The database operation unit collects information on the nuisance call responded by the automatic response unit and registers the information in the database. The risk evaluation unit displays a risk ranking on an incoming call screen on the basis of the information on the nuisance call registered by the database operation unit.SELECTED DRAWING: Figure 1

This invention discloses a method for ranking the causes of carbon emission exceedance risks in integrated energy systems based on sPCE, comprising: 1. For the three risk causes affecting IES carbon emission exceedances—photovoltaics, electricity prices, and load—variability factors are extracted through de-benchmarking processing, and a marginal cumulative distribution function and equivalent correlation matrix of "source-price-load" are constructed; 2. Samples are collected using Latin hypercube and restored to time-series sample values ​​of photovoltaics, electricity prices, and load in physical space. The optimal carbon emission response is solved using the IES operation optimization model, and a sPCE model with sparse polynomial chaotic expansion is constructed and solved using the least angle regression algorithm; 3. A set of carbon emission risk causal factors is defined, and a risk ranking based on the sPCE model is constructed and executed using both sensitivity and tail risk contribution indicators. This invention enables rapid assessment and refined quantitative source tracing of system carbon emission risks, providing quantitative decision support for the low-carbon robust scheduling of the system.

This invention provides a chemical detection system for scraping residue on printed tape, relating to the field of data processing technology. The system includes: identifying residue categories in a sample; ranking each residue category by risk to obtain a risk ranking result; correcting the risk ranking result using spatial anisotropic weighting to obtain a key residue selection list; and a judgment module, used to establish quantitative detection models for each residue category based on the key residue selection list, analyze the sample, and obtain the measured content value of each residue category; correcting each measured content value using spatial anisotropic weighting to obtain a corrected content value; and comparing the corrected content value with the safety threshold of the corresponding residue category to obtain a judgment result. This invention achieves effective identification, accurate quantification, and reliable judgment of scraping residue on printed tape even when the distribution is uneven.

This invention discloses a statistical system and method for monitoring the condition of construction machinery based on narrowband Internet of Things (IoT) technology. The system includes the following steps: collecting operating status signals of construction machinery and generating continuous raw time-series data; preprocessing the data to construct a cumulative deviation statistical sequence; performing change point detection at the edge to generate structured change point events; transmitting the structured change point events to the cloud via narrowband IoT; constructing a change point event sequence based on the received structured change point events in chronological order; defining stable operating segments based on the time intervals between adjacent change points; constructing time-dependent covariates within each stable operating segment and reconstructing them as new change point events arrive; inputting the time-dependent covariates into a proportional hazards model and using the change point time as a risk function to generate a risk value sequence segmented by nodes; forming a equipment risk ranking result and outputting a maintenance priority list. This invention enables remote monitoring, statistical analysis, and maintenance decision-making for the operating status of construction machinery.

Embodiments of the present application disclose a contract risk identification method, device, equipment and storage medium. The method comprises: performing segmentation processing on a to-be-processed contract to generate a plurality of contract sentences; receiving user input business attention risk information; performing preset mode expansion on the business attention risk information; based on the expanded business attention risk information, using a preset algorithm to confirm a suspected attention risk contract sentence from the plurality of contract sentences in multiple dimensions; using a risk ranking model to calculate the similarity between the business attention risk information and each suspected attention risk contract sentence; and according to the similarity, ranking the suspected attention risk contract sentences to determine a maximum risk contract sentence from the suspected attention risk contract sentences. The technical solution of the embodiments of the present application can cope with variable contract risk scenarios, has good scalability and generalization ability, and improves the risk identification accuracy.

This invention relates to a method, apparatus, equipment, and medium for risk ranking of circumferential welds in in-service pipelines. The method includes: acquiring multiple risk factors for the welds to be ranked; for each risk factor, acquiring the weight corresponding to the risk factor and the level score of the risk factor under a specified attribute feature; for each risk factor, determining the comprehensive risk value of the risk factor based on the weight and level score; and ranking the risks of the pipeline circumferential welds based on the comprehensive risk values ​​of each risk factor. The method of this invention can be used for risk ranking of welds in in-service gas transmission pipelines, guiding the current domestic circumferential weld inspection, effectively reducing excavation volume, and improving excavation accuracy.

The invention relates to a road train operation and maintenance decision-making system and method based on a dynamic knowledge graph, belongs to the technical field of road train operation and maintenance, and solves the problem that fault mode recognition and performance degradation analysis in the prior art depend on empirical rules or static models and cannot dynamically adapt to complex and changeable operation environments. Comprising the following steps: S1, acquiring original data of a road train; s2, decomposing each equipment unit structure, and identifying key components and connection modes thereof; s3, establishing a data mining model; s4, establishing a comprehensive reliability evaluation model of the equipment, and calculating to obtain a reliability index of each key component of each equipment unit; s5, calculating equipment health indexes, and dividing equipment states; s6, constructing an equipment knowledge graph; s7, performing global reliability analysis based on the equipment knowledge graph, and determining a fault risk sequence and a weak link as a reliability evaluation result; and S8, generating a maintenance plan.

The invention relates to the technical field of power systems, and provides a power distribution network branch risk sorting method and system for high-permeability new energy in order to solve the problem that the prior art is difficult to meet the requirements of conservative scheduling and risk sorting for tail risk sensitivity. The high-permeability new energy oriented power distribution network branch risk sorting method comprises the following steps: constructing a steady-state closed relation considering electrothermal coupling; based on a new energy and load uncertainty model, a load rate-fault rate mapping model is established by considering a steady-state closed relation of electrothermal coupling, and a branch fault rate is obtained; constructing a risk-aware minimum load shedding and network reconstruction joint optimization model to obtain an accident consequence evaluation result; and constructing an accident drop point weight based on the branch fault rate, calculating an EVaR branch risk contribution degree in combination with an accident consequence evaluation result, and sorting the branches to obtain a high-risk branch list. According to the method, high-risk branch screening and sorting can be realized on the premise of keeping the project process to be on the ground.

The application discloses a power grid monitoring and risk early warning method and system based on digital twinning and AI, which comprises the following steps: comparing, under the same time slice, the synchronous acquisition data with the nodal and branch flow calculation results and reference quantities of the digital twinning power grid model, and generating a residual vector; constructing an influence relationship matrix based on the power flow Jacobian matrix, the branch power balance relationship and the upstream and downstream power conservation relationship; solving each cause group disturbance variable by using weighted least squares estimation and sparse group constraint, and determining the confidence; correcting the digital twinning power grid model according to the confidence, updating the data reliability and labeling the historical acquisition samples; training the load prediction model based on the labeled samples, generating representative working conditions, performing power flow calculation, counting the out-of-limit conditions and obtaining the risk ranking. The application can improve the accuracy of power grid anomaly attribution, load prediction and risk early warning.

The application discloses a line trip-out probability calculation method and system based on an improved cellular automaton forest fire model, and belongs to the technical field of power systems.The method comprises the following steps: adjusting geographical and meteorological environment parameter fields in the cellular automaton forest fire model to generate a forest fire spreading model; performing three-stage closed-loop evolution simulation on the forest fire spreading model; setting a dynamic time threshold termination criterion; and calculating simulation data of the closed-loop evolution simulation according to the termination criterion to determine the line trip-out probability.The application establishes the correlation between the breakdown voltage and the operating voltage, and realizes dynamic solution of the line trip-out probability and risk ranking output under the assumption of probability distribution, thereby improving the accuracy and engineering applicability of the line trip-out probability evaluation under the forest fire scenario.

The invention relates to the field of quality and reliability engineering, and discloses an industrial robot manufacturing process potential failure mode risk analysis method, which comprises the following steps: S1, describing the risk of a potential failure mode in an industrial robot manufacturing process by using a graduatory fuzzy language; s2, solving evaluation personnel and influence factor weights based on a best worst method and a water injection theory; s3, performing compromise calculation on risk values of the failure modes by adopting a multi-criterion iterative decision-making method, and determining a risk sequence of the failure modes; considering that the structure of the industrial robot is complex and the evaluation personnel have a blind area in understanding the failure mode, the hesitant psychology of the evaluation personnel is captured by adopting the graduatory fuzzy language with higher degree of freedom, and meanwhile, the weight of the evaluation personnel is calculated to quantify the ability of the evaluation personnel, so that the evaluation result is more objective; according to the method, a multi-criterion iterative decision method is adopted by considering that the consequence is serious when the industrial robot fails, and the weight of an influence factor must be considered in failure mode risk calculation.

The invention discloses a pneumoconiosis early-stage refined screening method based on progressive risk sorting, and solves the technical problems that traditional screening depends on subjective judgment of experts, an existing model ignores a disease progressive rule, and local and global features cannot be effectively integrated. The method comprises the following steps: firstly, constructing a training data set containing a chest radiograph global image and six sub-lung region images according to GBZ70-2015 standards, constructing a screening network taking VisionTransform as a backbone, jointly extracting global and local features, generating sub-lung region continuous risk scores, performing differential sequencing modeling, and obtaining a sub-lung region continuous risk score; the local classification results are aggregated through a binary relaxation differentiable decision module, a diagnosis score is generated in combination with global feature intensity, a screening model is obtained through multi-task joint training, and sub-lung region anomaly classification and chest radiography level diagnosis results can be output by inputting an image to be screened. The disease progressive law is obviously modeled, the misclassification risk is reduced, the local and global diagnosis consistency is ensured, the screening accuracy and interpretability are improved, the dependence on experts is reduced, and the clinical applicability is high.

The application provides a dynamic scale risk ranking method and device, terminal equipment and a computer readable storage medium. The method comprises the following steps: associating effective dynamic scales in a plurality of dynamic scales in a logistics network to obtain an associated dynamic scale network; and ranking the associated dynamic scales in the associated dynamic scale network to obtain a dynamic scale risk ranking result. The dynamic scale risk ranking method provided by the application can rank the dynamic scales in the associated network. The more forward the dynamic scale in the ranking, the greater the risk. Therefore, the ranking can visualize the risk, and the automatically generated ranking can save manual ranking on the basis of the prior art, thereby reducing the labor cost.