176 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.

The invention discloses a goat milk product cold-chain logistics system risk evaluation method. The method comprises the steps of S1, collecting a plurality of key parameters in a goat milk product cold-chain logistics process in real time; s2, historical cold-chain logistics data are collected, risk event labeling is carried out, and a historical data set is generated; s3, preprocessing the real-time data set and the historical data set, and generating a feature vector for risk assessment by applying a feature engineering technology; s4, based on the feature vectors, a support vector machine algorithm is adopted to construct a multi-level risk assessment model through a multi-dimensional feature fusion technology; s5, performing dynamic risk assessment according to the real-time data set and the multi-level risk assessment model; and S6, according to a dynamic risk assessment result, by setting an intelligent threshold value, automatically identifying a potential risk and triggering an early warning mechanism, forming an early warning signal, and according to an early warning level, automatically starting an intervention measure. The method has the advantages of being accurate, real-time and dynamic in response, and the safety and reliability in the cold-chain logistics process are effectively improved.

The invention relates to the technical field of network security, in particular to a computer system risk monitoring method and system based on digital twinning, and the method comprises the following steps: collecting the real-time operation data of a computer system, constructing a digital twinning model according to the real-time operation data, classifying and grouping the operation data in the digital twinning model, and determining the risk of the computer system. And recording the real-time value of each type of data according to the time sequence, and forming a classified real-time operation data table by referring to the real-time value of each type of data. According to the invention, real-time operation data is collected and a digital twinborn model is constructed, and the difference value and the change trend of the operation data are combined. According to the method, the trend sequence is divided according to the fixed time, the abnormal data intervals are screened, and the multi-dimensional parameters are counted, so that the key features of the abnormal data can be identified from the finer dimension, and the accuracy of data analysis is improved. And the associated features of the abnormal parameters are extracted based on the state transition record, and the transition probability is calculated, so that the future trend can be predicted with higher accuracy.

The invention discloses a bank system risk monitoring and early warning method and system based on artificial intelligence, and relates to the field of data processing suitable for administrative, commercial, financial, management, supervision or prediction purposes, and the method comprises the steps: constructing an account transaction network according to transaction data of a target time window, an account being a node, and a transaction relationship being a directed edge; an initial importance score is calculated by analyzing a flow direction characteristic, a time correlation characteristic and a business mode characteristic of an account, importance transfer between nodes is carried out, a link position characteristic is constructed by combining a fund dispersion degree and a path collaboration degree of a preorder transaction path, an account suspicious degree score is finally calculated, and an early warning is given to the account exceeding a threshold value. By implementing the method, the timeliness of discovering the bank transaction risk behavior can be improved.

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.

The invention discloses an electricity-hydrogen coupling system risk scheduling method based on robust safety deep reinforcement learning, and the method comprises the following steps: 1), considering the Faraday efficiency loss effect of an electrolytic cell, and constructing an electricity-hydrogen hybrid energy storage dynamic operation model; 2) considering the wind power uncertainty, and establishing an electricity-hydrogen coupling system risk scheduling model considering the dynamic operation of electricity-hydrogen hybrid energy storage; 3) constructing a robust constraint Markov decision architecture; 4) parameterizing the robust constraint Markov decision architecture by using a flexible action device-evaluator baseline algorithm; and 5) solving the risk scheduling model of the electricity-hydrogen coupling system by using the parameterized robust constraint Markov decision process architecture to obtain a risk scheduling scheme of the electricity-hydrogen coupling system. According to the method, the new safety deep reinforcement learning algorithm is provided for the important requirements of energy low-carbon green transformation and energy safety, efficient and strict management and control of the electric hydrogen coupling system risk are achieved, and the reliability and safety of system operation are guaranteed.

The invention relates to the technical field of computer network security, in particular to a computer network information security management method and system, which comprises the following steps: S1, risk assessment, S2, security policy making, S3, security protection measure implementation, S4, management measure implementation, S5, security training and propaganda, S6, emergency response, and S7, accident handling and asset identification: comprehensively carding network assets in an enterprise or an organization, comprise hardware equipment, software systems, network services, data and the like, and the importance and value are determined. According to the computer network information security management method and system, through an information security management method, such as a data encryption technology, the data can be converted into a ciphertext form for storage and transmission, and only an authorized officer with a correct key can decrypt and restore the data into original data; therefore, abnormal user access behaviors and potential security holes can be found in time, a basis is provided for improving security measures, and the situation that a database is stolen or leaked in the data transmission process is effectively prevented.

A taxpayer false opening behavior-based risk assessment and disposal method comprises the steps of S1, determining a to-be-assessed taxpayer list which comprises at least one taxpayer, and obtaining tax data and attribute data of the taxpayer according to the to-be-assessed taxpayer list; s2, cleaning and preprocessing the tax data and attribute data of the taxpayer, and then constructing a plurality of risk indexes of the taxpayer according to practical experience; s3, establishing an association relationship and a risk relationship between the taxpayers according to the upstream and downstream transaction relationship between the taxpayers; s4, a risk scoring system is constructed, the risk scoring system is provided with a risk scoring model, the risk scoring model is used for calculating a business risk score S1 and an attribute risk score S2 of the taxpayer, and then a comprehensive risk score S of the taxpayer is obtained through calculation; and S5, carrying out real-time tracking and data analysis on the business risk score S1 and the attribute risk score S2 of the taxpayer by the risk scoring model, and adjusting numerical values of weights a, b, c, e, f and g in real time.

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 belongs to but not limited to the technical field of safety control, and particularly relates to a quantitative risk assessment method and system for a petroleum refining device protection layer, and the method comprises the steps: S1, carrying out the harmfulness analysis of a system, and forming a system HAZOP analysis table; s2, according to a system HAZOP analysis table, fusing system process knowledge such as operation regulations, and constructing a safety knowledge graph of the system; s3, comprehensively considering the influence of the individual background of the expert and the evaluation process on the weight of the expert, determining a weight coefficient, and assisting in making a quantitative risk evaluation rule; and S4, according to the structural characteristics of the security knowledge graph, considering the path risk propagation influence and the reduction effect of the protection layer on the influence, performing rule supplementation on the RPN calculation method to obtain a quantitative risk assessment rule, and completing system risk assessment based on the rule.

The invention discloses an SRL-based intelligent networked automobile function degradation decision-making method and system. The method comprises the following steps: step 1, acquiring environment information and monitoring a trigger event; 2, grading the system risk assessment indexes of the trigger event monitored in the step 1, determining a corresponding automobile safety integrity grade according to an actual driving mode, and obtaining the system risk of the automobile; 3, according to the environment information collected in the step 1, integrating the risk fields of the static obstacles, the dynamic obstacles and the road boundaries, and utilizing a driving risk field model to obtain the driving risk of the vehicle; 4, describing the function degradation decision-making process of the intelligent networked automobile through a CMDP model to obtain an optimal function degradation strategy; wherein the state space S and the cost function of the CMDP model both comprise the system risk in the step 2 and the driving risk in the step 3. According to the method, efficient, safe and stable function degradation decision making of the intelligent network connection automobile in a complex and changeable scene can be achieved.

The present invention relates to the technical field of data processing, and specifically discloses an intelligent security management and risk prediction system and method based on cloud computing. The system is provided with a multi-modal threat analysis module, a spatio-temporal risk prediction module, an adaptive defense decision-making module, a federated model evolution module, and an intelligent forensics and traceability module. It solves the problems that traditional security management relies on rules and experience, has a lag in response, many false positives and false negatives, is difficult to process massive data, and is difficult to detect and analyze quickly and accurately when dealing with new and complex security threats, and cannot meet the current security requirements. It helps to mine and analyze massive security data, intuitively present the system risk distribution, reduce the probability of security incidents, discover potential risk patterns and trends, realize automated and intelligent security management and risk prediction, adapt to the constantly changing security threat environment, and continuously improve the intelligent analysis and processing capabilities of the system.

The invention relates to the technical field of power system risk assessment, in particular to a frequency safety assessment method, device and equipment considering source load uncertainty and a medium, and the method comprises the steps: generating a source load scene at a future moment in a rolling manner based on source load data at a plurality of historical moments before the future moment, obtaining an uncertain operation scene set in combination with future plan information; establishing a time-phased frequency safety evaluation model before the day, evaluating the frequency safety under the uncertain operation scene set, and updating the time-phased frequency safety evaluation model based on a source load scene under the future moment of rolling refreshing; a risk analysis method is introduced, a frequency deviation out-of-limit risk is calculated based on an uncertain scene evaluation result, a safety risk threshold value is set, and a frequency safety dynamic risk threshold value represented by the central moment of each order of the uncertain quantity of the source load is defined through the relation between the safety risk threshold value and the uncertain quantity of the source load in different time periods. According to the method, the frequency safety in a large number of uncertain scenes can be quickly evaluated.

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 invention is suitable for the technical field of industrial control system safety, and provides an industrial control system risk assessment method and system, computer equipment and a storage medium, and the industrial control system risk assessment method comprises the steps: determining an overall system risk value of an industrial control system based on a preset hierarchical risk assessment model, under the condition that the overall system risk value meets a preset condition, determining the probability of attacking to select each target based on a preset attack and defense game model, and determining attack propagation information based on a preset time probability Petri network model and the probability of attacking to select each target; wherein the attack propagation information comprises an attack propagation path and an attack propagation probability, so that the security risk in the ICS can be identified and evaluated more accurately, a basis is provided for formulating an effective defense strategy, and the overall security and reliability of the ICS are 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 embodiment of the invention provides a system risk analysis method and device, computing equipment and a readable storage medium, and the method comprises the steps: in the operation process of a target service in a target system, obtaining information of various data indexes for the target service; according to the analysis sequence of the multiple data indexes, sequentially analyzing the information of the multiple data indexes to obtain operation risk information for the target service; and determining a to-be-adjusted service in the target system based on the operation risk information of the target service. According to the method, the maintenance effect on the target system can be improved, and the operation effect of the target system 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.