22 results about "System risk" patented technology

This invention proposes a multi-energy safety assessment method and system considering multiple time scales and coupled faults. The method includes: establishing an intermediate time-scale model and a long-time-scale model of the integrated energy system; conducting a safety assessment for each fault scenario in a fault scenario set; wherein, using the long-time-scale model to perform an N-1 safety scan of the fault scenarios considering coupled faults, calculating system risk assessment indicators; when the system risk assessment indicators reflect that the fault scenario may lead to water hammer consequences, using the intermediate time-scale model to calculate the overpressure value generated during the water hammer process; and generating the final safety assessment result of the integrated energy system based on the safety assessment results of each fault scenario. This invention can capture the water hammer effect of the heating network and the cascading faults caused by the coupling of the logical and geographical layers, which are often overlooked in traditional assessments, achieving a more comprehensive and accurate safety assessment of the integrated energy system and helping to improve the operational safety of the system.

This invention relates to the field of electronic digital data processing technology and discloses a power system risk assessment method based on a hierarchical regional graph neural network. The method includes: dividing the power system into multiple regions and constructing a local graph structure for each region; establishing connections between regions to form a regional-level graph; acquiring multi-source heterogeneous operational data of the power system as input features for corresponding nodes; using a graph neural network to learn and aggregate the node features in each local graph to obtain a regional feature representation for the corresponding operational region; aggregating the regional feature representations in the inter-regional connection graph based on a graph attention mechanism to fuse risk information; and generating node-level, regional-level, and system-level risk assessment results based on the feature representations of nodes and regions, as well as the risk information. This method solves the problems of effective regional layering, unclear risk propagation mechanisms, and lack of multi-level output, achieving the goals of multi-source data fusion, multi-level assessment, and dynamic adaptation to topology changes.

The application provides a micro-service architecture-oriented intelligent chaos test method and device, relates to the technical field of software testing, and solves the problems of one-sided fault coverage of traditional tests, rigid strategies, easy triggering of system risks and disconnection with business scenarios. The method comprises the following steps: collecting a service call chain through a distributed tracking technology, and constructing a dynamic dependency graph comprising node importance and link throughput; adopting an LSTM neural network to analyze a business log sequence to identify a business process stage, and establishing a "stage-link-fault" triplet rule; integrating topology features, business stages and real-time operation indexes to construct a state space, and optimizing a fault injection strategy based on a reinforcement learning algorithm and a multi-objective reward function; and limiting the injection intensity and range through a conflict detection and pre-check mechanism. The application is used for reliability verification of a micro-service system, and improves test efficiency and safety.

This application relates to a risk assessment method, apparatus, device, storage medium, and program product for secondary equipment. Based on the fluctuations and trends of alarm frequency sequences associated with the inherent risk factors of the target equipment, the risk index of the inherent risk factors of the target equipment at the current moment is calculated. Based on the risk index of the target factors, the dynamic conditional probability of the target factors is determined using a logistic regression model. The target factors are inherent risk factors with parent nodes in a dynamic Bayesian network. The dynamic conditional probability is input into the dynamic Bayesian network, and the systemic risk probability of the target factors is determined through a Bayesian inference algorithm. Based on the systemic risk probability of the target factors, their operational impact factors, economic impact factors, and safety impact factors, the risk assessment result of the secondary equipment is determined using a comprehensive risk index expression. This improves the accuracy of the obtained risk assessment results for secondary equipment.

This invention discloses a method, apparatus, electronic device, and storage medium for determining key nodes in a power system based on physical attack and defense parameters, belonging to the field of power system security assessment and defense. The method includes: acquiring real-time network topology data, real-time node load data, and physical attack and defense parameters under an attack and defense simulation scenario; calculating the attack success probability and defense success probability of each node based on the physical attack and defense parameters, and further obtaining the damage probability of each node; establishing an optimization model with the goal of maximizing system risk based on the damage probability of each node, the real-time network topology data, and the real-time node load data; and determining the combination of attack nodes that maximizes system risk as key nodes in the power system by solving the optimization model. By implementing this invention, the problem of accurately identifying real key nodes in complex physical attack and defense scenarios using existing technologies can be solved.

This invention proposes a method and system for screening typical fault scenarios in active distribution networks under extreme weather conditions. The method first constructs a component failure rate model of the active distribution network based on extreme weather and operational information, obtaining the component failure rate distribution under extreme weather conditions. Then, based on the component failure rate distribution under extreme weather conditions, it determines the evolution of system state probability over time and calculates the system failure state probability distribution. Finally, the time when the system failure state probability exceeds a threshold or reaches its maximum is used as the selection time for typical fault scenarios, generating typical fault scenarios for active distribution networks under extreme weather conditions. This invention comprehensively considers the environmental disturbances of extreme weather, component failure coupling, and the dynamic evolution characteristics of system states, achieving effective screening of typical fault scenarios for active distribution networks under extreme weather conditions. This improves the pertinence and accuracy of system risk assessment and provides a decision-making basis for distribution network emergency dispatch and resilience enhancement.

The invention discloses a power system cascading failure simulation method and device considering a new energy off-network risk, a medium and a product, and relates to the technical field of power system risk assessment, and the method comprises the steps: obtaining training data; according to the training data, training a new energy off-network simulation hybrid neural network by using an active learning method to obtain a new energy off-network simulation hybrid neural model; constructing a cascading failure dynamic evolution environment; the cascading failure dynamic evolution environment is integrated with a line overload factor model, a frequency deviation factor model, an emergency control model and a new energy off-network simulation hybrid neural model; in a cascading failure dynamic evolution environment, a reinforcement learning algorithm is adopted to train an intelligent agent, so that the intelligent agent learns a failure evolution law and is used for deducing and simulating a cascading failure development path. According to the method, the active learning neural network replaces the new energy plant station to quit simulation, and a complex and time-consuming simulation process does not need to be carried out when cascading failure simulation is carried out.

The application relates to a complex system risk assessment method based on a complex network, and belongs to the field of complex system risk assessment. For all risk nodes, a risk propagation probability matrix between the risk nodes is generated through a P-I probability matrix. Meanwhile, according to the characteristics of the risk nodes, a suitable complex network model is selected as the basis of a risk propagation network graph of the risk nodes. The risk propagation probability matrix between the nodes is taken as the output to output the risk propagation network graph of the system. Whether the actual situation is met is judged. If not, the P-I probability matrix is reconstructed, the complex network model is reselected, and the operation is performed again until a risk propagation network graph more in line with the actual situation is output. Meanwhile, the Monte Carlo thought is used to analyze the degree distribution of each node, and the degree distribution of the node is taken as an evaluation index for evaluating the influence size of the risk node in the system. The application realizes clearer understanding of the risk propagation in the complex system, and simplifies the risk assessment of the complex system.

The application discloses a kind of interbasin water transfer system risk control methods, comprising: obtaining the inflow scenario data of system to be analyzed, water demand scenario data and preconfigured water transfer scheme set;Water quantity scheduling simulation is carried out based on water transfer scheme set to scenario data, determine the water shortage risk index and water abandonment risk index of each spatial node in system under different schemes;Based on the above two types of risk indicators, the risk response function that the risk level evolves with the relationship of water transfer quantity is fitted;Based on the risk response function, the risk transfer marginal substitution index that the risk replacement efficiency between each spatial node is represented is calculated;The efficient balanced water transfer interval of system is determined in combination with risk transfer marginal substitution index and preconfigured risk space equilibrium criterion.The application can scientifically quantify the risk transfer efficiency of cross spatial unit, realize the double-target collaborative optimization decision of system total risk optimization and risk space equilibrium.

This invention discloses a risk assessment method for wind farm SCADA systems based on the fusion of asset marginal loss and failure probability. First, it unifies the modeling of the wind farm SCADA system's partition structure, channel attributes, asset types, multi-relationship dependencies, and threat scenarios. Then, it quantifies the consequences of asset failure to obtain the asset marginal loss weight. Next, it constructs a probabilistic attack graph, combining the attack attempt probability within a time window, the vulnerability exploitation success probability, environmental correction factors, and credential threshold factors to calculate the asset failure probability. Finally, it fuses the marginal loss weight with the asset failure probability to obtain a risk output and identify high-risk propagation paths. This invention can transform the partition channel constraints, permission links, and structural dependencies of a wind farm SCADA system into a computable model, achieving interpretable and verifiable quantitative risk assessment without relying on real-time alarm data.

This invention provides a method, system, device, and storage medium for power system risk control decision-making, belonging to the field of power system technology. The method includes: acquiring power system fusion data and power market text information, and labeling the text information with market sentiment tags; training an optimal electricity price sentiment mapping and an optimal sentiment mapping correction quantity based on the tags and text information, and extracting market sentiment features based on the optimal mapping and correction quantity; acquiring a market feedback time decay mapping based on the aforementioned text information, and fusing the text information, time decay mapping, and market sentiment features to obtain power market expectation features; then combining the power fusion data to predict the base predicted electricity price and the market predicted electricity price; and obtaining the base price tendency quantity under gating parameters based on the predicted electricity price to obtain the estimated electricity price; and generating a power anomaly alarm based on the estimated electricity price for risk control decision-making. This invention can improve the reliability of power system risk control decision-making.

This application discloses a method, apparatus, device, and medium for risk control of cryptographic systems in response to quantum computing threats, relating to the field of information security technology. The method includes: constructing a dependency graph of cryptographic operation data, the cryptographic system, and the business system based on the operational data of the cryptographic system from the business system; predicting target parameters characterizing the ability of a target quantum algorithm to crack encryption algorithms; determining quantum resistance score, impact score, and operational status score based on the dependency graph and target parameters; constructing a system risk score based on preset score weights; determining the target risk and risk type of the cryptographic system based on the dependency graph and system risk score; and executing corresponding risk mitigation strategies according to the risk type. Introducing quantum threats and establishing the association between cryptographic information and the system through a dependency graph, determining a comprehensive risk score related to business operations, and executing corresponding mitigation strategies helps improve risk mitigation efficiency and enhance the dynamic security capabilities of the cryptographic system.

The application discloses a risk feature extraction method for a main microgrid, and comprises the following steps: firstly, constructing a new energy output probability model and a system element operation model, and generating multiple system operation scenarios by using a sequential Monte Carlo method; then, determining an active power and reactive power dispatching optimization model, wherein the active power optimization takes the minimum system operation cost as an objective function, and the reactive power optimization takes the minimum system operation network loss as an objective function; subsequently, determining decision variables and constraint conditions of the dispatching evaluation model, and solving system state physical quantities by power flow calculation; then, constructing a risk criterion, determining a risk state data set of each scenario, inputting a random forest algorithm model to quantitatively analyze the risk importance of system features. Finally, determining a risk quantification evaluation method, and constructing a multi-level risk index system according to system risk features. The application can identify key influencing factors under a risk scenario.

This application relates to a method, apparatus, and computer equipment for assessing the operational safety of a microgrid. The method includes: determining the joint prior distribution of the scenario variable set corresponding to the microgrid using Gaussian Copula, and determining the risk index corresponding to each operational scenario based on a pre-trained risk predictor; constructing a sampling probability density function based on the joint prior distribution and the risk index corresponding to each operational scenario, and generating a set of high-risk scenarios for the microgrid based on the sampling probability density function; simulating each high-risk scenario in the high-risk scenario set using a digital twin model of the microgrid to obtain test data sets for each high-risk scenario; determining the system risk entropy corresponding to each high-risk scenario based on the test data sets for each high-risk scenario, and determining the corresponding safety assessment report for the microgrid based on the system risk entropy corresponding to each high-risk scenario. This method can improve the reliability of the microgrid operational safety assessment results.

The application discloses a kind of dynamic scheduling method and system of energy storage resource, applied to electric power system dispatching field, including determining risk state estimation value based on meteorological prediction data and power grid operation data;Synchronization phasor data is analyzed to obtain disturbance entropy and disturbance complexity;Risk perception result representing risk accumulation state is obtained based on risk state estimation value, disturbance entropy and disturbance complexity;According to risk perception result, the economic characteristic parameters and operating characteristic parameters of energy storage resource are combined to adjust the objective function of energy storage optimization scheduling model;With disturbance entropy as constraint condition, the adjusted model is solved, and scheduling instruction is obtained and executed.The energy storage resource dynamic scheduling method provided by the application fuses meteorological prediction, power grid operation state and real-time monitoring information, constructs scheduling architecture of risk perception guided optimization target and real-time state constraint solution boundary, and realizes dynamic matching of scheduling strategy and system risk level.

The embodiment of the application provides a kind of underground hydrogen storage library injection-production system risk factor intelligent identification method, device, equipment and product.The method is by obtaining the operation parameter of underground hydrogen storage library, operation risk factor and structure causal model, structure causal model is used to indicate the correlation of multiple operation risk factors of underground hydrogen storage library.According to structure causal model, determine the accident hazard type matched with operation parameter and operation risk factor, and the benchmark risk value of accident hazard type.According to benchmark risk value, determine the influence proportion of each risk factor on accident hazard type, and influence proportion is used to represent the processing priority of risk factor.The method can comprehensively monitor, accurately analyze and scientifically control the risk factors of underground hydrogen storage library, can identify potential risk in advance, so that risk control is targeted.

The application discloses a system risk management method and device, terminal equipment and a storage medium, and the method comprises the following steps: acquiring business system information of a bank trust business system; performing rule configuration on the business system information based on a pre-set configuration rule; performing risk signal grading on the business system information after rule configuration to obtain a risk grading signal; and managing business system risks based on the risk grading signal. The application can evaluate the importance of information risks, thereby focusing on key businesses.

This invention discloses a knowledge base-based security assessment method and system for multi-agent systems, comprising: mathematical modeling and structural analysis of the target multi-agent system; constructing a poisoning sample and injecting it into an external knowledge base at a low ratio; constructing a joint optimization objective function that integrates uniqueness, compactness, and coherence constraints, and optimizing and generating triggers that can induce retrieval behavior to hit the poisoning sample; inputting a test task integrating the trigger into the target multi-agent system, and performing full-link monitoring and logging of each agent; comparing and quantifying the retrieval offset of the target multi-agent system under normal input and triggered input, tracing and reconstructing the risk propagation path triggered by the offset based on the collaborative topology graph; quantifying the system risk level based on the retrieval offset rate and code risk rate, and finally outputting a security assessment report. This invention, by constructing a proactive testing and quantitative analysis mechanism, can achieve controllable and realistic security assessment.

The application provides a power system network risk defense method based on Markov rule reasoning, and realizes dynamic identification and defense of power system network risk. First, a fuzzy reasoning model for multi-source power system operation data is established, a rule capable of reflecting differences in distribution of different data characteristics is constructed in combination with triangular, trapezoidal and Gaussian membership functions, and accurate description of input state is realized; second, a rule updating mechanism based on Markov decision process is designed, state reasoning, action selection and score evaluation are performed, it is ensured that the rule can continuously adjust with the change of system state, and dynamic optimization of the number and parameters of the rule is realized; finally, a decision method for generating risk levels and defense strategies is constructed in combination with comprehensive membership output and loss function feedback, so that the model can maintain accuracy and stability when facing power grid system risk situations, and the reliability of power system network risk defense is improved.

This invention specifically relates to a risk assessment method for hydrological-power coupled systems under rainstorm disasters, belonging to the technical field of distribution network system risk assessment. By establishing a four-dimensional assessment system encompassing hazard, exposure, vulnerability, and adaptability, this invention not only quantifies the direct damage of hydrological disasters to the power system but also innovatively defines secondary power hazards, characterizing the disaster amplification feedback loop of "power failure → drainage failure → exacerbated flooding → further damage to power facilities" at the mechanistic level. This invention introduces adaptability as a risk adjustment factor, comprehensively calculating the adaptability coefficient through indicators such as infrastructure density, road density, and the proportion of educated population. It scientifically demonstrates that improving regional adaptability and resilience can directly reduce overall risk, indicating a strategic transformation path from simply mitigating disasters to enhancing system resilience.

This invention discloses a machine learning-based method and system for predicting power grid risks, relating to the field of power safety technology. By acquiring monitoring data of main equipment in the target power grid and preprocessing it to obtain feature vectors, these feature vectors are input into a pre-trained state recognition model to obtain the failure probability of each main equipment. Based on the failure probabilities, key nodes exceeding a preset threshold are selected, corresponding failure scenarios are constructed, and failure exit simulations and consequence assessments are performed to obtain power grid risk analysis results. This invention utilizes a machine learning model to predict the failure probability of main equipment and, based on the prediction results, conducts targeted scenario construction and risk analysis, reducing the computational load of traditional traversal, improving analysis efficiency, achieving dynamic integration of equipment health status and system risk, and enhancing the real-time performance, accuracy, and scientific nature of scheduling decisions in risk assessment.