150 results about "System risk" patented technology

The invention relates to the technical field of electric power system risk assessment, and discloses a risk assessment system based on an electric power transaction market, which realizes real-time acquisition and dynamic processing of electric power transaction price data through a price data real-time acquisition module in combination with a real-time deviation analysis module, calculates a price deviation value in real time and captures related influence factors, thereby realizing risk assessment of the electric power transaction market. The dynamic parameter adjustment module can establish a correlation model, automatically trigger parameter adjustment when real-time factor fluctuation exceeds a threshold value, and realize automatic updating of computational logic by combining scene adaptation and emergency adjustment subunits, so as to ensure that a price deviation value accurately reflects an actual risk; the risk assessment module combines dynamic parameters to construct a model, calculates risk indexes and divides grades, the risk early warning module generates early warning information in time, and the risk conduction analysis and pressure test unit provides comprehensive risk pre-judgment, assists market subjects to scientifically make plans, and maintains the stability of the power transaction market.

A power monitoring system risk dynamic disposal method based on multi-modal data fusion and a knowledge graph is characterized by comprising the following steps: step 1, accessing multi-source heterogeneous data in real time, and performing standardization processing and data fusion on the multi-source heterogeneous data to obtain a fused data unit; 2, constructing an electric power risk knowledge graph model by using the fusion data unit, and storing and updating the electric power risk knowledge graph model; and step 3, risk reasoning and blocking are carried out based on the atlas so as to generate real-time risk matching and strategies. Blocking feedback data is collected in real time, manual intervention is reduced through online learning updating of the knowledge graph and the rule base, the rule base updating period is shortened, and the method adapts to dynamic changes of a power system.

Risks associated with installing a software package on a computer system can be automatically detected and mitigated using techniques described herein. In one example, a system can generate severity scores for software components. Each severity score may correspond to a software component and indicate a severity of its vulnerabilities. The system may generate a risk score based on the severity scores. The risk score may represent an overall level of risk associated with installing the software package on the computing device. The system may also determine that an alternative software component is correlated with a software component of the software package, determine respective severity scores for the software component and the alternative software component, and compare their respective severity scores. The system can determine that the severity score of the alternative software component is lower and output a notification indicating the risk score and the alternative software component.

The embodiment of the invention provides an information system security evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an evaluation index model corresponding to a security evaluation request, and obtaining a simulation system corresponding to a target information system; performing vulnerability scanning on the simulation system based on the scanning missing tool and the MDATA knowledge base to obtain a corresponding first scanning result and a corresponding second scanning result, and performing scanning result fusion analysis based on the MDATA knowledge base to obtain a system risk assessment result of the simulation system; obtaining an optimized attack strategy based on the system risk assessment result and the MDATA knowledge base, and obtaining an optimized defense strategy based on the system risk assessment result and the expert knowledge base; the attack and defense test is performed on the simulation system based on the optimized attack strategy and the optimized defense strategy to obtain the attack and defense test result, and the security evaluation result of the target information system is obtained based on the evaluation index model and the attack and defense test result, so that the accuracy of evaluating the information system is improved.

The invention discloses a method for enhancing robustness of an artificial intelligence system. The method comprises the following steps: collecting system logs, performance indexes and user behaviors of system operation to form a data set; carrying out real-time processing on the data set by adopting a distributed stream processing platform, wherein the real-time processing comprises time alignment and space alignment; performing heterogeneous analysis through a graph neural network, and identifying normal data and abnormal data; normal data are sent to feature storage, and cross-dimension detection including isolated forest detection and rule engine reinspection is carried out on abnormal data; performing real-time risk assessment based on a heterogeneous analysis result and a cross-dimension detection result, and calculating a final risk level through a fusion formula; executing corresponding current limiting, blocking or monitoring operation according to the risk level; and completing adaptive adjustment of system parameters. According to the method, the system risk is dynamically evaluated in real time, compared with a traditional adversarial sample method, the calculated amount is reduced, a more flexible and comprehensive protection mechanism is provided, and the robustness of an artificial intelligence system is effectively enhanced.

The invention relates to the technical field of electric power system risk perception, in particular to an electric power asset risk perception method and device based on a multi-dimensional time sequence and a large model and a medium, and the method comprises the steps: collecting asset fingerprints of an electric power information system, matching the asset fingerprints with a standard component knowledge base, and standardizing the asset fingerprints; obtaining a standardized asset fingerprint ledger library; extracting vulnerability feature information from the multi-source unstructured risk early warning information to obtain a structured risk intelligence information base; performing vectorization representation on asset features in the standardized asset fingerprint machine account library and vulnerability influence component features in the risk information library, calculating vector similarity between the asset features and the vulnerability influence component features, performing feature matching according to the vector similarity, positioning affected assets, and generating an asset risk list; and driving a risk disposal process through a process engine based on the asset risk list. The asset risk perception efficiency is improved, and the response period is shortened.

A method for risk assessment of a power system in extreme weather conditions considering deep feature mining for risk assessment of a power system. The risk assessment method includes: dividing the entire power system into regions based on geographical locations; combining historical wind speed data to construct a correlation model for strong wind extreme weather in multiple regions of the power system; constructing a strong wind scenario sample set for each region based on the correlation model; obtaining the probability of transmission line failure in the corresponding region under each strong wind scenario in the strong wind scenario sample set; randomly assigning a power system operating condition to each strong wind scenario in each region, and obtaining the operating risk value of the power system under the corresponding operating condition; constructing and training a risk assessment model; and using the model to provide a power system operating risk score assessment.

The invention relates to the technical field of computer system performance and security testing, in particular to a computer performance security testing method and system. The method specifically comprises the steps that performance indexes, behavior characteristics and security event data of a target system are obtained in real time and subjected to standardization processing, clustering modeling is conducted on the behavior characteristics subjected to dimension reduction through a Gaussian mixture model so as to establish a normal behavior pattern cluster, and time sequence analysis is conducted on a behavior sequence in combination with a hidden Markov model so as to recognize a system state transition path; adopting a weighted combination strategy to mix and inject a normal load and an aggressive load, and adjusting an abnormal behavior chain through a disturbance function; constructing directed weighted association between the abnormal event and the performance index based on an event-driven causal performance diagram, quantifying a system risk index and triggering an alarm; outputting performance bottlenecks, security vulnerabilities and optimization suggestions, and updating the knowledge base to optimize subsequent models. According to the invention, deep coupling of performance and safety testing, intelligent scheduling of dynamic loads and interpretable positioning of risk paths are realized.

The invention discloses a quantitative early warning method for coupling risks of an urban lifeline system, and the method comprises the steps: collecting the real-time data of each system, and constructing a multi-level network which reflects the physical connection and function dependence relation between the systems; determining risk source nodes in the network, and traversing all communicated potential risk transmission paths along a dependency direction; for each path, a path cascade failure probability is calculated by fusing a node historical fault probability and a real-time state; quantitatively evaluating the risk value of each path in combination with the key level of the affected node; and finally, according to the risk value sorting, outputting the path in the front of the sorting and the key node thereof as early warning information. According to the method, global identification and quantitative evaluation of the cross-system risk conduction chain are realized, weak links of the network can be accurately positioned, and a scientific decision basis is provided for targeted risk prevention and control.

The invention discloses a system risk prediction method and system based on big data analysis, and relates to the technical field of risk prediction and management. The method comprises the following specific steps: S100, edge node data acquisition and feature recognition; S200, embedding structured dyeing labels into detected abnormal features; S300, executing a differential transmission strategy; S400, analyzing the dyeing labels to generate a system risk probability map; according to the method, early warning and accurate positioning of the risk are realized through the edge computing driven risk identification and abnormal data dyeing marking technology, the edge computing node monitors the original data stream in real time at the data source, the potential risk can be quickly identified, rich risk information is given to the data through the dyeing label, and the risk identification accuracy is improved. According to the early risk identification mechanism, the data transmission quantity and the calculation burden of the central server are remarkably reduced, the real-time performance and the response speed of risk prediction are improved, and the risk loss is effectively reduced.

The invention discloses a data element transaction risk assessment method based on variational Bayesian reasoning. The method comprises the following steps: S1, collecting and constructing a multi-channel multi-source data set from a data element transaction platform; s2, generating structured data; s3, forming a multi-granularity risk feature set; s4, constructing a multi-granularity risk representation model; s5, iteratively optimizing the hierarchical variational Bayesian inference model by using a variational inference method, continuously updating model parameters of each risk layer, and enabling posterior distribution of risk factors of each layer to approach real distribution; and S6, generating a dynamic change trend of the individual risk, the platform risk and the system risk, performing comprehensive calculation to generate a unified risk score, and performing multi-granularity risk quantification on risk factors for real-time risk early warning, triggering an early warning signal and outputting a risk assessment result to a risk management system. The method has higher layered response sensitivity and risk conduction visibility, and the accuracy of platform-level fraud detection and system-level mobility risk identification is obviously improved.

The application discloses a kind of autonomous ship operating state evaluation methods considering multi-state component degradation, establishes the function control structure of autonomous ship in remote control mode, and determines the multi-state safety function of autonomous ship subsystem and system risk function, to obtain the time of predicted guarantee response, the time of backup response and the critical time when system risk exceeds the critical state system risk level set, to evaluate the operating state of autonomous ship in remote control mode.The application can more accurately capture the unique risk control process of autonomous ship through the function control structure of system in multiple operating states and the interaction mechanism between multi-component subsystems, and provides basic support for scientific risk management strategy and decision-making process in autonomous ship operation, and provides theoretical support for preventing autonomous ship from deviating from operating boundary.

The invention relates to the technical field of train wireless network communication, in particular to an information security risk assessment method for a train wireless network control system, which comprises the following steps of: establishing an attack tree model according to a risk assessment object, assessing the occurrence possibility of a security event by using a triangular fuzzy number, analyzing attack paths, and calculating the interval probability of each attack path, the method comprises the following steps: obtaining a point probability according to an attack path interval probability, calculating an attack occurrence possibility, evaluating a security event influence by using a fuzzy analytic hierarchy process, calculating a security event risk value by quantifying the occurrence possibility and an influence value of the security event, and determining a risk level and a security level of a system according to a system risk evaluation value. And formulating corresponding protection requirements. According to the method, the information security risk of the train wireless network control system is effectively evaluated and managed, a basis is provided for formulating an effective security protection strategy, and the overall security capability of the train wireless network is improved.

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

The application discloses a system risk assessment method and device and a charging equipment operation and maintenance detection method. The system risk assessment method comprises the following steps: acquiring system change data of a target system within a target time; the system change data comprises system early warning information and device names corresponding to system changes; based on the system change data, early warning information corresponding to the target system change is determined by using a natural language processing technology; time series data of the target system within the target time is acquired, and a change trend of a monitoring index corresponding to the target system is obtained based on the time series data by using a machine learning algorithm; and the target system is subjected to system risk assessment based on the early warning information and the change trend. According to the application, the risk of system change is assessed by using the early warning information of system change obtained based on the natural language processing and the monitoring index trend anomaly obtained based on the machine learning, and the accuracy of system change risk assessment under a micro-service architecture is improved.

The invention relates to the technical field of digital twinning technology and radioactive waste treatment, and discloses a digital twinning-based radioactive large tank dismantling scheme optimization method and system. The method comprises the steps of obtaining multi-working-condition operation data and operator behavior data to generate a digital twinborn model basic data set, generating a system risk state quantitative index by using a system critical state evaluation model, identifying a potential operation error type based on an error pattern library, generating a risk coupling index by using a system-human factor risk coupling model, and establishing a twinborn model based on the risk coupling index. And generating a risk pre-control intervention strategy based on the risk coupling index. According to the method, the technical problem that system risks and human risks are separated in a traditional method is solved, and cooperative prevention of composite risks is achieved.

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.

The invention discloses a power grid safety supervision service system risk monitoring method and device, a terminal and a medium, according to the scheme, when a network state is abnormal, timestamp information of each task in different task stages is extracted according to historical task records of a power grid safety supervision service system, and actual response time of each task stage is calculated; calculating a phase response time deviation index based on the actual response time, then counting the fault occurrence frequency of various faults and the average fault occurrence frequency of a plurality of unit time periods according to the historical fault record of the power grid safety supervision service system, and calculating a fault processing efficiency fluctuation index; and determining a risk assessment result of the power grid safety supervision service system. According to the scheme, the network connection state, the task response time, the fault processing efficiency and other key indexes of the power grid safety supervision service system are monitored in real time, abnormal conditions existing in the system can be found and processed in time, and therefore the stability and safety of the system are effectively improved.

The invention belongs to the technical field of receiving-end power grid evaluation, and particularly relates to a receiving-end power grid active support capability evaluation method and system based on the total risk potential of a system, and the method comprises the steps: firstly constructing an active support capability evaluation index system of a receiving-end power grid; then calculating an individual risk potential function of each index and a coupling risk coefficient between each index and other indexes, and then calculating a system total risk potential based on the individual risk potential function and the coupling risk coefficient, the system total risk potential including a system individual risk potential and a system coupling risk potential; and finally, active support capability evaluation of the receiving-end power grid is realized based on the total risk potential of the system. According to the method, on one hand, a scientific and comprehensive evaluation system is constructed, the deep evaluation of the active support capability is realized, on the other hand, on the basis of quantifying the deviation degree by using the individual risk potential, the non-linear superposition risk additionally generated by the interaction or simultaneous degradation among multiple indexes is quantified through the coupling risk potential field, and the evaluation accuracy of the active support capability is improved. And the diagnosis precision is obviously improved.

Methods and systems for generating an attack path based on user and system risk profiles are presented. A method comprises determining user information associated with a computing device; determining system exploitability information of the computing device; determining system criticality information of the computing device; determining a risk profile for the computing device based on the user information, the system exploitability information, and the system criticality information; and generating an attack path based on the risk profile. The attack path indicates a route through which an attacker accesses the computing device. The system exploitability information is associated with or based on one or more of the vulnerability associated with the computing device, an exposure window associated with the computing device, and a protection window associated with the computing device. The system criticality information is associated with or based on one or more assets and services associated with the computing device.

The invention discloses an electric power system risk management and control method, device and system and a storage medium. The method comprises the following steps: receiving inquiry data which is input by a user and is related to electric power system risk management and control; inputting the inquiry data into a language model for semantic analysis to obtain a semantic vector, and inputting the semantic vector into a classification model to determine a target intention category of the user; extracting scene keywords related to a power grid system from the target intention category, mapping the scene keywords into a preset professional knowledge base to obtain retrieval fields, performing retrieval in the professional knowledge base based on the retrieval fields to obtain risk knowledge fragments related to risk management and control, and performing risk management and control on the risk knowledge fragments. Filling the risk knowledge fragment into a preset answer template to generate a risk management and control scheme; and managing and controlling the risk prevention and control process of the power system based on the risk management and control scheme. The accuracy of risk management and control of the power system can be improved based on the professional knowledge base.

The invention discloses a production process quality system risk assessment method for a digital workshop, and relates to the technical field of intelligent quality management and control, and the method comprises the steps: endowing each work-in-process entering the digital workshop with a unique initial risk identifier; after the processing of the current procedure is completed, collecting industrial data of the in-process product, generating a risk characteristic value based on the industrial data, and updating the initial risk identifier to form a dynamic risk chromosome; the industrial data comprises quality detection data, process parameter flow and equipment state data; constructing a cellular automaton network based on a process logic relationship in the production scheduling plan; when the work-in-process flows to a new process, inputting the risk characteristic value into a cellular automaton network, and calculating a risk conduction direction and a risk conduction probability through a cellular state evolution equation; according to the method, the process logic relation in the production scheduling plan is mapped into the cellular automaton network, so that dynamic modeling and quantitative analysis of the risk conduction path in the production process are realized.

The application discloses a kind of multi-energy collaborative heating system risk assessment and regulation method and device, based on improved GNOME model, risk source in multi-energy collaborative heating system is analogized as oil spill source, risk propagation is analogized as oil spill diffusion, by defining risk particle and giving its risk type, risk level, initial position and influence range etc. Property, using similar convection and diffusion mechanism in oil spill model, in combination with energy flow and information transmission in heating system and other factors, the propagation path and change of risk in heating network are simulated. The present application helps to master the risk development trend in real time, early warning potential risks, provides strong support for formulating targeted risk control measures, and ensures the safe, stable and efficient operation of multi-energy collaborative heating 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 invention discloses a power system risk assessment device and method based on extended dimension adaptive sparse grid interpolation and a medium, and relates to the field of power system risk assessment, the device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, a processor executes a computer program to realize a power system risk assessment method based on extended dimension adaptive sparse grid interpolation, and the method comprises the following steps: firstly, extending DASGI by using a relaxation technology, so that the DASGI can effectively approach a complex risk assessment function which takes a load shedding amount as an output, has strict non-negativity, and gathers a large number of results near a zero value; based on the constructed EDASGI proxy model, an analytic relationship between the unit commitment and the system risk is established; the iterative recursion solving process of the RSUC is converted into a mixed integer quadratic programming problem, efficient and unified solving is achieved, and the efficiency and precision of risk decision making in a high-dimensional uncertainty scene are remarkably improved.

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.

The invention discloses a firmware updating method based on mixing of asymmetric encryption and symmetric encryption. According to the invention, the security and compatibility are improved through a dual-signature mechanism. According to the method, two asymmetric algorithms, namely RSA and edDSA, are used at the same time to sign a firmware hash value, so that the risk caused by cracking of a single signature algorithm is avoided, and the authenticity and integrity of a firmware source are doubly guaranteed. The asymmetric encryption part is responsible for signature and verification, the security problem of key distribution is solved, and it is ensured that only firmware of a legal source can pass verification; the symmetric encryption part is used for efficiently encrypting and transmitting the upgrade package, so that the confidentiality of data is ensured, too high calculation burden cannot be brought to the embedded equipment, the balance between safety and performance is considered, seamless update is realized, the original version can be automatically returned when the update fails, the continuous and stable operation of the equipment is ensured, and the service life of the equipment is prolonged. And the system risk in the updating process is reduced.

This invention discloses a method for calculating the dynamic reliability of electromechanical systems based on inter-component interoperability, comprising: (1) constructing a hidden Markov model for system risk transmission and an inter-component risk state transmission matrix; (2) combining load effects to calculate inter-component interoperability and resistance coefficients, quantifying relevant parameters of the state transition matrix, and calculating the probability that the risk of a component will not escalate after risk transmission; (3) applying inter-component interoperability, determining the final state sequence of the system based on the idea of ​​stochastic computation, and calculating the dynamic reliability of the system. For complex electromechanical systems, the interoperability calculation method of this invention can make the system reliability calculation closer to the actual situation, and based on this, an improved maintenance strategy is proposed. Compared with the traditional maintenance strategy, the maintenance strategy considering inter-component interoperability proposed in this invention can maintain the system at a higher reliability and have a lower maintenance cost in the long term.

The invention discloses a power distribution network information physical system risk analysis method, system and device based on grid division and a storage medium, and relates to the field of power system risk analysis. Dividing operation data of a physical system and an information system of the power distribution network into unit grids and grid groups; searching potential faults, and generating a fault event set; performing batch simulation on fault events based on a multi-thread programming framework by adopting a grid group parallel computing technology, quantifying load loss under different faults and generating load loss data; based on load loss data and introduced defense resource parameters, calculating a feeder risk coefficient and a system risk coefficient, and quantitatively analyzing a risk propagation path; according to the method, accurate risk positioning and rapid early warning can be realized, multi-dimensional coupling risks are comprehensively covered, the risk assessment real-time performance of the power distribution network is greatly improved, and safe and stable operation of a novel power system is guaranteed.

The invention relates to a novel power system risk prevention method and device and a storage medium, and is applied to the technical field of power market risk protection, and the method comprises the steps: constructing a multi-dimensional risk monitoring index system covering economy, society, fairness and the like, and driving market optimization through the system; the crossing from single price limitation to systematic risk active management is realized, and the ability of the market to deal with complex uncertainty is improved; through design of collaborative constraints of electric energy, capacity, auxiliary service and green electricity markets, the relationships among the markets are straightened out, optimal configuration of various resources is promoted, and the overall market efficiency is improved; a dynamic planning and closed-loop feedback mechanism is introduced, so that market parameters (such as a price limit value and a risk weight) and an operation strategy can be dynamically adjusted according to real-time states such as supply and demand, new energy output and the like, and the adaptability of the market mechanism to the dynamic characteristics of the novel power system is remarkably enhanced.