106 results about "Risk scenario" patented technology

The embodiment of the invention provides a voltage instability risk assessment and high-risk scene screening method and related equipment, and belongs to the field of power systems and automation thereof. The method comprises the following steps: extracting a voltage distribution balance degree according to a spatial distribution characteristic of a sample; constructing a voltage instability risk assessment index in combination with the voltage distribution balance degree, the new energy transmission margin ratio and the voltage out-of-limit risk factor; according to the voltage instability risk assessment index, abnormal samples in the sample set are screened through an isolation forest algorithm; performing K-means clustering analysis on the screened abnormal samples to subdivide the candidate scene into subclass clusters with clear risk characteristics; performing class cluster risk assessment according to a clustering result to obtain a class cluster comprehensive score, and dividing class clusters into different risk levels; through dispersion analysis, a centralized extreme scene and a discrete extreme scene are distinguished, and class cluster center and boundary scene screening is carried out respectively. According to the invention, efficient and comprehensive high-risk scene screening can be realized.

The invention discloses a novel power system supply and demand risk assessment method based on multiple spatial-temporal scales, and relates to the technical field of power system supply and demand risk assessment, and the method comprises the following steps: obtaining a time-varying fault rate set and a dynamic transmission limit set of power grid equipment based on extreme weather prediction data; fusing the time-varying fault rate set, the dynamic transmission limit set and power supply, load and new energy power generation data to obtain a risk scene library; screening the risk scene library to obtain a typical risk scene set; analyzing the typical risk scene set, and outputting a supply and demand risk index under each time-space unit; and performing regional division on the power system based on the supply and demand risk indexes, and generating corresponding early warning and management and control suggestions. According to the method, the problem that the evaluation result is not accurate due to the fact that the time-varying characteristics of the equipment state are ignored in extreme weather is solved, and accurate decision support is provided for disaster prevention and supply protection of a power system.

The invention relates to the technical field of pipeline safety monitoring, in particular to a long-distance pipeline risk event distinguishing monitoring system and method based on artificial intelligence. The artificial intelligence-based long-distance pipeline risk event distinguishing monitoring method comprises the following steps of S10, acquiring humidity time sequence data and anode output current time sequence data of soil in an area where a target pipe section is located, and respectively drawing a humidity time curve and a current time curve; acquiring a vibration signal acquired by the optical fiber sensing network; and S20, periodically fluctuating according to the current-time curve. According to the method, the relation between the regional self-adaptive humidity saturation interval and the corrosion rate is established, the huge cost for customizing a vibration analysis model for each region is avoided, the event recognition accuracy is improved under high-risk scenes such as coating damage by utilizing deep coupling of the corrosion state and vibration, and the event recognition efficiency is improved. And passive alarm is converted into active and accurate early warning based on humidity prediction.

A system and method for operational and cyber risk assessment that utilizes a data-driven approach to evaluate the current security posture and identify areas for improvement based on the user's desired target profile. This process involves estimating the costs and benefits associated with various security program enhancements, increased, hiring, and control uplifts. The system and method then quantify these benefits in terms of reduction in tail value at risk, expected losses, cyber insurance premiums, and the amount of risk capital set aside. The system simulates attack paths associated with various risk scenarios and uses a risk scenario model to compute losses associated with each attack path for each risk scenario. The results of the simulation may be used to determine one or more business outcomes associated with the costs and benefits of implementing security enhancements.

The invention relates to the technical field of data analysis, in particular to a data and knowledge combined driven intelligent decision-making method and system.The method includes the steps that multi-source heterogeneous data and knowledge base concepts are unified into a structured graph structure through multi-dimensional feature extraction and multi-head attention fusion context prompt, and a structured semantic basis is provided for intelligent decision-making; by combining data-driven mining and knowledge prior constraints, the accuracy and robustness of causal reasoning are improved, and the reliability of root cause diagnosis is ensured; a root cause path is searched and enumerated in a reverse breadth-first manner by taking an abnormal entity as a starting point, so that rapid and traceable abnormality positioning is realized, and a user is assisted to focus a high-probability root cause; through modular assembly and full-link element traceability, while the analysis threshold is reduced, decision full-link transparency is realized, and the auditing requirement of a high-risk scene is met; by automatically converting natural language requirements into executable scripts, the decision-making efficiency of non-technical users is improved.

The invention discloses an electric vehicle transportation risk scene construction method and system based on multi-source data clustering, and the method comprises the following steps: collecting a multi-source heterogeneous data source, and obtaining original data in the transportation process of an electric vehicle; preprocessing the original data to obtain a standardized data set; performing feature extraction on the standardized data set to form a feature vector set; performing clustering analysis on the feature vector set by using a clustering algorithm to obtain a plurality of data clusters; and constructing an electric vehicle transportation risk scene model based on the data clustering cluster. According to the invention, through multi-source data fusion and clustering analysis, a potential risk mode in electric vehicle transportation can be automatically identified, a fine-grained risk scene is constructed, and the accuracy and efficiency of risk management are improved. In the embodiment, data quality is ensured by data preprocessing, dynamic risk factors are captured by feature extraction, natural data grouping is realized by clustering analysis, and interpretable risk classification is provided by a risk scene model.

The invention discloses an industrial big data visual management system based on big data analysis, and relates to the technical field of industrial data management, and the system comprises a data collection module, a data analysis module, a visual early warning module and a strategy optimization module. The data analysis module integrates the three-dimensional data to form a single-main-body full-process and single-element full-access double-track chain, the single-main-body full-process and single-element full-access double-track chain is fused to obtain a behavior track chain, and abnormal nodes are identified through double verification; the visual early warning module visually presents data in a layered manner through a statistical layer and a risk layer, and triggers real-time early warning; the strategy optimization module extracts risk scenes based on the visual data, and adopts different optimization strategies respectively. According to the method, complete tracing of the operation track, accurate identification of abnormal nodes and dynamic optimization of strategies are realized, and the industrial data security control and operation and maintenance efficiency is improved.

This application relates to a method and system for intelligent policy analysis and risk monitoring based on a family graph. The method includes: acquiring multi-source policy data and performing structured processing; constructing a family policy relationship graph based on the relationships between policyholders and insured persons in the dataset; acquiring a predefined risk scenario library, containing multiple preset risk scenarios and corresponding coverage types for each scenario; calculating the total coverage amount based on the family graph by identifying related policies for each risk scenario; acquiring a risk exposure benchmark value, comparing the total coverage amount with the benchmark value, and generating a quantitative result of the coverage status; combining a pre-built insurance product knowledge graph, generating coverage optimization suggestions based on the quantitative results; and re-executing the calculation and generation operations in response to user lifecycle events or periodic assessment instructions, and generating risk warning notifications based on the analysis of result changes. This application achieves intelligent analysis of family policies, dynamic risk monitoring, and coverage optimization, improving risk management efficiency.

The application discloses a kind of based on risk scene's electric power market parallel clearing method and system, it is characterized by: S1, multi-dimensional risk scene synchronization generation;S2, scene space-time feature extraction;S3, scene clustering group;S4, distributed parallel clearing calculation;S5. Risk index aggregation analysis.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.

This invention discloses a method for improving the grid-load interaction capability of high-energy-consuming industrial users under carbon emission constraints. The method includes: real-time acquisition of dynamic power output characteristic data from new energy sources, capturing key dynamic features such as instantaneous power fluctuations, ramp rate changes, and spatiotemporal correlations of wind farms and photovoltaic power plants; constructing an adaptive generation model for multimodal risk scenarios, and using cluster analysis and stochastic process theory to summarize the complex stochastic fluctuations of new energy power output into several representative typical risk modes. The robustness-first rolling decision optimization mechanism of this invention prioritizes ensuring system power balance and safety under all generated risk scenarios in its core objective function and constraints. This fundamentally overcomes the decision failure risk caused by prediction bias in traditional methods, thus providing the power grid with excellent resilience and anti-interference capability under complex and uncertain operating conditions.

This invention discloses an accelerated safety assessment method and system for low-altitude navigation systems based on alternating cross-entropy and importance sampling, relating to the fields of computer simulation and safety assessment technology. The method includes: constructing a probabilistic risk assessment model and initializing a proposal distribution; entering an alternating iterative loop: first, performing importance sampling assessment, calculating accident rate estimates and statistical errors; if the error does not meet accuracy requirements, initiating cross-entropy optimization, updating the proposal distribution parameters using simulation data; the new distribution is fed back to the next round of assessment. This loop continues until the results converge, and an assessment report is output. This invention, through a closed-loop feedback mechanism of alternating assessment and optimization, enables the sampling distribution to adaptively approximate the optimal distribution, intelligently focusing simulation resources on high-risk scenarios, solving the computational efficiency bottleneck of assessing extremely low probability events, achieving an order-of-magnitude improvement in assessment efficiency, and possessing high adaptability and engineering practical value.

The application discloses a water conservancy project quality supervision method and system based on a large model, relates to the field of water conservancy projects, and comprises the following steps: collecting multi-source heterogeneous data of a water conservancy project and performing pretreatment to form a spatiotemporally aligned original data pool; constructing a water conservancy project knowledge cube through a large model, inputting a natural language supervision query of the water conservancy project knowledge cube into the large model; decomposing the natural language supervision query into structured subtasks, performing reasoning on data source evidence retrieved from the water conservancy project knowledge cube by using the structured subtasks, and generating a traceable supervision report; inputting an adversarial prompt instruction into the large model, generating an adversarial risk scenario description, and reversely constructing a minimum virtual modification set of part of entities or attributes in the water conservancy project knowledge cube by the large model. The application realizes a fundamental change from information presentation to intelligent insight in water conservancy project quality supervision.

A reinforcement learning obstacle avoidance method and system based on a dynamic temporal discount factor is disclosed. The method acquires state observation data of the agent and obstacles; calculates the minimum safe distance deviation, maximum relative approach speed, and obstacle density index; and generates a risk urgency scalar by calling a risk urgency mapping function based on these three parameters. The temporal discount factor is dynamically adjusted according to this scalar, and the generalized advantage estimation function is further reconstructed in the near-end policy optimization algorithm to update the policy network. This application improves obstacle avoidance response speed in high-risk scenarios and optimizes trajectory globality in low-risk scenarios through an environment-adaptive discount mechanism, reducing collision rate and energy consumption, while also exhibiting low computational overhead and strong compatibility.

This invention discloses a water-energy coupled water resources planning method, apparatus, equipment, and medium. The method includes: collecting and processing historical index values ​​corresponding to water demand prediction for each water-using sector to obtain the upper and lower limits of each index for the planning year, and inputting them into a trained water demand prediction model to obtain the water demand interval prediction results for each water-using sector; collecting and preprocessing historical water resource availability and energy availability data; constructing a water-energy system coupling risk analysis model based on the Copula function to quantitatively analyze the coupling risk of the water-energy system, and setting four coupling risk scenarios: low, medium-low, medium-high, and high; constructing a multi-objective interval stochastic chance constrained programming model, which is decomposed into two deterministic sub-models; solving the two deterministic sub-models using a hybrid intelligent algorithm, and integrating the solution results to obtain and output the regional water resources optimization allocation results adapted to the four risk scenarios of low, medium-low, medium-high, and high.

The invention discloses a power grid fault deduction method and device based on scene evolution, equipment and a medium, and the method comprises the steps: constructing a risk evolution scene library of a target power grid according to the pre-obtained multi-source heterogeneous data of the target power grid; wherein the multi-source heterogeneous data comprises historical accident data, equipment parameter data, power grid operation data and external environment data; the risk evolution scene library comprises a plurality of scene triples; the scene triad is composed of states, reasons and judgment; mapping the risk evolution scene into a corresponding Petri network dynamic model; and calculating and setting a trigger probability for each transition in the Petri network dynamic model, and performing random cascading failure evolution simulation on the Petri network dynamic model for a plurality of times to obtain a cascading failure deduction result of the target power grid. According to the method, cascading failure dynamic deduction can be carried out on brand new risk scenes which are lack of historical data support or are caused by multi-factor complex coupling, and the generalization ability of power grid cascading failure deduction is improved.

The challenge is to generate more accurate risk scenarios than before in the event of an incident. [Solution] To address this challenge, we propose a supply chain risk scenario creation support device that identifies similar groups of incident information that meet the risk scenario creation conditions (target information) in risk scenario creation, using at least one of the following: the time from incident occurrence to the occurrence of a reduction in parts supply, the time and quantity of the reduction in parts supply, and the time and quantity of recovery from the reduction in parts supply. Based on these similar groups, it creates risk scenarios and calculates a risk procurement plan based on the created risk scenarios.

The invention relates to the technical field of power system stability control, and provides a new energy black-start partition optimization method considering unconventional risks, which comprises the following steps: simulating unconventional risks possibly encountered by a power system by adopting Monte Carlo sampling; extracting the output characteristics of wind power under the unconventional risk by adopting variational mode decomposition, and performing output prediction in combination with a long-short-term memory neural network; a power system recovery partition is divided based on an LPA algorithm, and the LPA algorithm is improved by considering a partition size balance principle, so that the partition area is more balanced, and the tag oscillation effect is reduced. According to the method, the black-start partition optimization model fusing new energy output uncertainty modeling and unconventional risk scene generation is designed, and the partition scheme is cooperatively solved by adopting the improved label propagation algorithm, so that the robustness and the recovery efficiency of the black-start partition are effectively improved; and rapid and stable power supply of a power system under complex disturbance can be realized.

This invention relates to a multi-level simulation testing method and system for intelligent driving. The method includes: hierarchically labeling different test scenarios based on a six-dimensional complexity evaluation model to obtain expected functional safety scenario events and functional safety failure events; generating precise timestamps corresponding to the expected functional safety scenario events and functional safety failure events; synchronously triggering the expected functional safety scenario events and functional safety failure events based on the precise timestamps to simulate corresponding risk scenarios; selecting the verification level of the risk scenario based on the type of functional safety failure event and executing the corresponding simulation instance to obtain test data; and conducting risk assessment based on the test data using a risk assessment model. This application achieves joint injection of scenarios and failures, continuous accumulation of multi-level verification data, and composite risk quantification assessment, improving the comprehensiveness and efficiency of safety verification for intelligent driving systems.

The application discloses a kind of command and control system safety risk assessment methods based on linear influence.First, the physical connection diagram and information flow diagram of command and control system are established, and the bottom map is aggregated to form the bottom map of causal diagram in layers;Non-causal relationship in the bottom map is eliminated from the opposite direction of the form of spanning tree to the cause transmission;An index abstraction system is established, and the resource nodes in the causal diagram are decomposed into index nodes;Input system operation raw data is normalized and data interpolation processed;The linear relationship of abnormal node to each path on the subnode is calculated by least square estimation;After traversing all paths of abnormal node, the influence degree of abnormal node on each path is calculated;The influence degree of abnormal node on subsequent nodes, influence domain range and alarm level are output.The method can effectively calculate the influence result of abnormal node under different risk scenarios, improve the reliability and safety of command and control system.

The invention relates to a restricted space accident risk dynamic assessment method and system based on the Internet of Things. The method comprises the following steps: acquiring multi-source heterogeneous time-space sequence data, constructing a time-space data cube based on the data, inputting the time-space data cube into a physical constraint generative adversarial network, creating a large amount of high-quality virtual risk scene data conforming to a physical rule, and combining the virtual risk scene data with real data to obtain a real risk scene. And training a risk assessment model, seamlessly connecting the trained model with a real-time data stream, and assisting with a smooth filtering technology to obtain a comprehensive accident risk dynamic assessment result. By adopting the method, small sample limitation can be overcome, a physically compliant virtual risk scene is generated, and accurate dynamic assessment of the accident risk in the limited space is realized.

The application relates to the technical field of data security audit, in particular to a data security risk audit element construction method based on identification, which comprises the following steps: constructing a security risk audit element library, wherein audit elements carried in the security risk audit element library cover the whole process of data flow, including data elements, carrier elements, user elements and operation behavior elements; selecting a target audit rule, and auditing data security risks based on the security risk audit element library, wherein the target audit rule is determined based on known or preset risk scenarios and unknown risk scenarios. By applying the method, data audit omissions can be avoided, the interpretability of a data element extraction process can be improved, the workload can be reduced, and the flexibility of data security audit strategy configuration is improved.

A production risk analysis device (100) comprises: a risk scenario pattern acquisition unit (112) that, on the basis of product-related information (121) including at least one of product specifications and production process information, uses a language model (see a language model server (200)) to acquire a risk scenario pattern pertaining to the production of the product; a causal model generation unit (113) that extracts causal relationships between factors and results included in the risk scenario pattern and generates a causal model (130); and a detailed risk scenario acquisition unit (114) that, on the basis of the product-related information (121) and the causal model (130), uses the language model to acquire a detailed risk scenario pertaining to the production of the product.

This invention relates to the technical field of foundation pit engineering monitoring, and provides a method and system for dynamic determination of multi-parameter risk status in foundation pit monitoring. The method includes: analyzing on-site monitoring data of the foundation pit to identify correlation trend information among multiple monitoring parameters caused by changes in deep soil; when the identified correlation trend information meets preset triggering conditions, initiating a risk scenario simulation; during the risk scenario simulation, based on the current state of the digital model, simulating the foundation pit response under preset external factors, simulating the chain reaction caused by changes in deep soil, and calculating the probability and expected time of the chain reaction reaching a preset safety limit in the future; generating early warning information of foundation pit risk based on the probability and expected time, and displaying the risk evolution trend graphically. This invention improves the safety and reliability of foundation pit engineering.

The application discloses an ecological restoration risk intelligent early warning method and system, the method comprises the following steps: acquiring real-time monitoring data of a current ecological restoration area; executing a first risk judgment process and a second risk judgment process in parallel, the first risk judgment process comprises the following steps: inputting the real-time monitoring data into a preset LSTM time sequence prediction model, and generating a predictive early warning signal based on the output prediction result, the second risk judgment process comprises the following steps: matching the real-time monitoring data according to a preconfigured rule set, and generating a real-time alarm signal according to the matching result; generating a current early warning event according to the predictive early warning signal and / or the real-time alarm signal, and pushing the current early warning event to a corresponding target person in charge. Compared with the traditional method which only relies on post-threshold judgment, the application realizes the complementary cooperation of predictive early warning and real-time alarm, can cover various risk scenarios, and thus significantly improves the comprehensiveness and timeliness of risk identification.

This application relates to a method, apparatus, computer equipment, and storage medium for engineering risk management. The method includes using engineering status data as input to a target model, outputting a comprehensive risk index for the project through the target model. The target model is a digital twin model constructed based on Building Information Modeling (BIM), which establishes a spatiotemporal correlation mapping of the project's geometry, attributes, real-time status, and real-time environmental data. When the comprehensive risk index meets preset requirements, multiple sets of random samples are determined based on key factors affecting engineering risk. Loss data from risk scenario simulations corresponding to each random sample are obtained through the target model, and a risk assessment report is generated. The risk assessment report is pushed to the corresponding insured parties according to their permissions. If a reinsurance ratio adjustment is triggered, a reinsurance ratio adjustment suggestion is generated based on the current reinsurance ratio and risk linkage coefficient of each insured party and pushed to each insured party. This method can improve the reliability of engineering risk management.

A risk scenario evaluation system includes: a damage record structuring section for generating damage record structured information storing a damage target, a damage scale, and a damage period as a database in an available format from damage record information storing as a text of a natural language on the basis of damage classification master data storing a damage target that is a type of a damage to be noted, a method of defining the damage scale, and a method of defining the damage period; and a damage record abstracting section that generates damage record abstracted information storing the risk type value from the damage record structured information in association with the damage target, the damage scale, and the damage period on the basis of risk response master data storing the risk type value that is an index of a magnitude of an impact on the damage target by risk type.

This invention discloses an adaptive gradient optimization method and system based on statistical safety boundaries, belonging to the field of machine learning optimization technology. The method includes: acquiring the gradient norm during the warm-up phase, initializing an online quantile estimator and system boundary after tail reduction processing; dynamically updating the statistical reference upper bound at each step through the quantile estimator, fusing gradient direction consistency and amplitude anomaly detection to distinguish signal from noise; calculating the risk probability based on the relative increase of a dual-scale exponential moving average; calculating the dynamic cruise coefficient based on the risk probability and signal quality; and outputting the step size coefficient through a hysteresis-constrained three-modal state machine. This invention replaces a fixed threshold with statistical quantiles, achieves signal acceleration and noise deceleration through signal-noise separation, and eliminates overshoot through a damping ramp. In high-risk scenarios such as large language model training and reinforcement learning, it effectively prevents training collapse caused by gradient spikes while maintaining convergence efficiency.

The embodiment of the invention provides an offshore wind field electromagnetic interference risk prevention and protection method and device. The method comprises the steps of obtaining multi-source and multi-dimensional rainfall data to generate a rainfall data set; performing three-dimensional variation assimilation processing on the rainfall data set to generate a high-resolution rainfall field, and extracting corresponding characteristic parameters; inputting the characteristic parameters into a pre-trained Bayesian network model, and outputting risk data and a confidence interval of electromagnetic interference; under the condition that the width of the confidence interval does not exceed a width threshold value, matching risk data output by the model with a multi-level early warning threshold value, and identifying a risk scene; and triggering a corresponding protection strategy according to the risk scene. Accurate prediction and graded protection of electromagnetic interference risks are realized, the equipment fault probability is reduced, the stability of a communication system is guaranteed, and the operation safety and reliability of an offshore wind plant are improved.

The invention discloses a water resource collaborative robust scheduling method considering supply and demand dual uncertainty, which comprises the following steps of: evaluating risk exposure contribution rates of incoming water and demand water to system risks, and constructing an asymmetric incoming water and demand water robust uncertainty set according to the risk exposure contribution rates; a supply and demand risk coupling amplification index is calculated, the joint uncertainty space is screened and reconstructed based on the index, and a joint scene tree is generated; inputting the scene tree into a water resource hierarchical collaborative scheduling model, performing cross-period optimization by using a global resource allocation layer, and determining a staged risk quantification parameter representing the risk constraint strength of each stage; and a local scheduling decision-making layer is utilized to take the parameters as cross-layer risk constraints, and a dynamic scheduling strategy meeting global risk control requirements is generated. Through risk scene construction and cross-layer risk explicit conduction driven by a physical mechanism, the problems of excessive conservative and extreme risk defense failure in the scheduling process are effectively solved.

The application discloses a kind of multi-modal risk event anomaly detection method and system based on fast and slow double systems and size model cooperation, it is related to artificial intelligence and multi-modal content security field.The application reconstructs risk event anomaly detection as evidence perception verification task, and constructs a kind of fast and slow double system framework inspired by cognitive science double process theory.Fast system uses visual language big model to detect overall anomaly clues to multi-modal input, and by language model reflection abstract is compact fast cognitive signal;Slow system decomposes the content to be detected into verifiable atomic statements, and forms slow cognitive signal through multi-source evidence retrieval and statement-evidence consistency analysis.The application significantly improves the recognition ability and judgment interpretability of semantic level risk events while maintaining high detection accuracy, and can provide reliable technical support for high-risk scenarios such as public opinion security.