46 results about "Risk ranking" patented technology

The invention relates to the technical field of electrical equipment defect monitoring, in particular to a high-voltage equipment defect positioning system and method based on multi-modal image fusion, and the system comprises an ultraviolet triggering module, a modal synchronization module, a space positioning module, a structure recognition module and a fusion diagnosis module. According to the method, automatic calibration of a suspected defect area can be realized by performing quantitative threshold judgment on a corona discharge signal in an ultraviolet image, the consistency of three-mode data in space and time dimensions is ensured, and the accuracy of the defect area is improved by comparing three types of coordinates of a lead hot spot, boundary geometry and a spot center in the image and performing registration offset correction. The image feature alignment precision is improved, the insulator chain light spot distribution path is extracted, the continuous distribution length is calculated and corresponds to the pollution level standard, automatic identification of the pollution level is achieved, a multi-modal image fusion feature block is constructed, and a risk sorting label is given. And the defect positioning accuracy and the comprehensive evaluation capability on the defect property and the influence degree are effectively improved.

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 relates to the technical field of risk identification, in particular to a user high-risk behavior identification method and system based on machine learning, the method integrates target confidence, category risk weight, relative speed and distance attenuation factor through a saliency formula to form a dynamic risk sequence, and compared with a traditional algorithm, the method is more in line with actual driving risk logic, and is more efficient. The priority management of targets in a complex traffic scene is realized; through a participation coordinate transformation matrix in a camera, the sight of a driver is accurately projected to a front view image plane, so that concentration assessment is directly associated with actual road risks; a time sliding window is adopted to smooth sight fluctuation, and when a close-range high-speed target is detected and a driver does not watch, taking-over is triggered immediately, and waiting for fixed time is not needed.

The present invention relates to the field of computer technology, and discloses a method, device, and storage medium for predicting the default of a bond issuer based on a semi-supervised model, comprising: S1: obtaining the subject data of the bond issuer, wherein the subject data includes news and public opinion information, industrial and commercial information, market evaluation information, and external information, and constructing an indicator system for the credit default risk of the bond issuer through the subject data; S2: constructing underlying features from statistical analysis, business judgment, and derivation to generate underlying factors; S3: establishing a semi-supervised model based on a combination of an unlabeled sample weighting method and a scoring card model; S4: judging and predicting the default risk of the bond issuer based on the semi-supervised model. The modeling method for this time is based on a combination of an unlabeled sample weighting method and a scoring card model, utilizing the risk ranking capability of the XGB classifier trained with positive samples and unlabeled samples to expand the scale of positive samples, using samples with the highest probability of high risk as new positive samples, training the scoring card model, and constructing a semi-supervised model.

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 discloses an intelligent operation and maintenance method of photo-thermal energy storage key equipment and a related device, and relates to the field of photo-thermal system risk management and control, and the method comprises the steps: obtaining a failure mode, a failure position, a failure reason and a failure result of groove type photo-thermal system key equipment; performing criticality analysis on the failure mode of each device to obtain risk factors of the failure mode of each device; determining the expert evaluation aggregation fuzzy number of each risk factor and the weight of each risk factor by adopting a Z-number improved AHP method; weighting the expert evaluation aggregation fuzzy number of each risk factor and the weight of each risk factor to obtain a risk priority number of the key equipment of the slot type photo-thermal system; sorting the risk priority numbers of the key equipment of the slot type photo-thermal system to obtain a failure mode risk sequence; and determining corresponding management and optimization measures based on the failure mode risk ranking. According to the invention, the safety risk management and control level of the photo-thermal system can be improved, so that the risk is stably controlled.

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.

The invention discloses a high-proportion photovoltaic access power distribution network power flow out-of-limit management strategy risk assessment method, and belongs to the technical field of power system risk assessment. The method comprises the steps of establishing a power distribution network flow out-of-limit risk index system according to an operation risk theory; calculating weight assignments of the indexes based on an improved analytic hierarchy process, an evaluation method and a subjective and objective combination method; and based on index weight assignment, carrying out risk sorting on the power flow out-of-limit management strategies by adopting an alternative strategy sorting method based on a compromise strategy. The power flow out-of-limit strategy risk assessment method can realize accurate power flow out-of-limit strategy risk assessment, and provides important support for safe and effective operation of a power distribution network power flow out-of-limit management strategy with high-proportion photovoltaic access.

The present invention relates to the field of management technology and is a reservoir slope seepage management method and system based on intelligent sensors. The specific method comprises: obtaining reservoir water level change data, slope geological structure data, water flow velocity field and particle concentration distribution data; performing slope seepage dynamic anomaly analysis based on a three-dimensional model of the reservoir slope and the particle concentration distribution data to obtain a first seepage threat index; performing seepage environmental impact analysis based on historical reservoir water level changes, particle deposition data and slope area location to obtain a second seepage threat index; obtaining a comprehensive seepage threat index based on the first seepage threat index and the second seepage threat index, and performing risk ranking and early warning on the reservoir slope based on the comprehensive seepage threat index. The present invention solves the problem of delayed or misjudgment of slope instability risk assessment in the prior art.

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.

The invention discloses a method for evaluating the risk ranking of perfluorinated compounds in dairy products, which belongs to the field of risk evaluation of perfluorinated compounds in dairy products, and comprises the following steps: step 1, carrying out experimental detection on the evaluated dairy products, and taking the detection rate and concentration of each detected perfluorinated compound as exposure risk factors; step 2, evaluating the hazard potentials of different perfluorinated compounds to human health by calculating persistence data, bio-enrichment data, elimination data, ecological toxicity data, human exposure toxicity data and tolerable weekly intake data to obtain corresponding hazard potential data; and step 3, integrating the exposure risk factor and the hazard potential data by using a toxicology priority index scoring system to preferentially evaluate the risk data of each perfluorinated compound on human health, and obtaining the risk sequence of different perfluorinated compounds in the dairy product. According to the method, exposure factors and toxic factors are fused, ToxPi multi-dimensional risk integration modeling is used, and accurate sorting of risks of multiple perfluorinated compounds in dairy products is achieved.

The invention relates to the technical field of railway important material transportation, in particular to an important material transportation risk assessment and prevention method, which qualitatively performs working structure-risk structure decomposition (WBS-RBS decomposition) on important material transportation in a transition stage, improves a 0-1 coupling matrix of a traditional WBS-RBS according to a triangular intuition fuzzy number theory, and improves the risk assessment and prevention of important material transportation. And converting into a triangular intuitionistic fuzzy number based on an expert evaluation language, constructing a linear programming model according to an evaluation result, solving the weight occupied by each expert, and finally obtaining a work-risk comprehensive sorting value. And according to the obtained work-risk sorting value, improvement suggestions and measures are put forward in a targeted manner. According to the method, the risk degree of each work flow and risk factor in the important material transportation in the transition stage can be visually expressed, and improvement measures can be determined and implemented.

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 present invention relates to the field of risk identification technology, and in particular to a method and system for identifying high-risk user behaviors based on machine learning. The method integrates target confidence, category risk weight, relative speed, and distance attenuation factor through a saliency formula to form a dynamic risk ranking. Compared with traditional algorithms, this method is more in line with actual driving risk logic and realizes priority management of targets in complex traffic scenarios. The method accurately projects the driver's line of sight onto the front-view image plane through the in-camera participation coordinate transformation matrix, so that the concentration assessment is directly related to the actual road risk. A time sliding window is used to smooth line of sight fluctuations. When a close-range high-speed target is detected and the driver is not looking, the takeover is immediately triggered without waiting for a fixed time.

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.