215 results about "System risk" patented technology

The invention relates to the field of power grids, and particularly discloses a multi-type extreme weather oriented power system risk assessment method and device. Comprising the steps of defining risk scene elements, determining a risk scene judgment confidence value based on a threshold value under a power grid normal operation condition, constructing a full-scene comprehensive evaluation framework, establishing an extreme weather occurrence probability model, performing generation and screening by using an approximate daily search method and an approximate distance method, and obtaining a source load power probability model. A fault probability model under the influence of different factors is constructed, a series model is utilized, a comprehensive time-varying fault probability model is determined, a power grid fault state occurrence probability model is obtained, comprehensive analysis is carried out, a risk scene probability model is obtained, and a risk consequence index system is established according to a power grid system operation potential risk. And inputting the target power grid data into the risk scene probability model, and performing evaluation by using the risk consequence index system to obtain a risk evaluation result. And the disaster response capability of various aspects of a future power system can be improved.

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.

The invention relates to the technical field of risk data detection, in particular to a risk data detection method and system based on edge calculation. The risk data detection method comprises the following steps: S1, collecting risk data through a sensor; s2, preprocessing the acquired data; s3, constructing a risk data detection model based on a deep forest algorithm, and updating model parameters; s4, training the model, and optimizing hyper-parameters of the risk data detection model by using a pruning strategy; s5, taking the trained detection model as a core component of risk detection; and S6, regularly uploading a detection result to a cloud computing center. According to the invention, the data related to the risk is collected in real time, and preliminary data preprocessing is carried out, so that the data quality is improved, and the computing pressure of the cloud is reduced. The risk data detection model is trained and optimized in a centralized mode, it is ensured that the risk data detection model has high performance, and real-time processing and accurate risk detection of data are achieved.

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 instrument panel light control method and system based on risk estimation and man-machine credibility, and the method comprises the following steps: collecting and extracting the behavior information of a driver, including the state information of the eyes, hands and feet of the driver; collecting a road traffic environment image, and obtaining a driving environment and traffic event information; taking the behavior information of the driver as an input pre-constructed credibility evaluation model, and outputting a credibility evaluation value of the driver on the automatic driving system at the current moment; the current automatic driving risk is evaluated according to the driving environment and the traffic event information, and an automatic driving system risk evaluation result is obtained; and according to the credibility evaluation value of the driver at the current moment and the automatic driving risk evaluation result, the adaptation degree between the credibility and the automatic driving system risk is calculated, the threshold value interval of the adaptation degree is judged, and light prompts of different colors are correspondingly controlled according to different threshold value intervals. According to the invention, the coordination of man-machine co-driving cooperation can be optimized, and the traffic safety is improved.

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 discloses a power system risk identification and evaluation method and system considering source load uncertainty, and the method comprises the steps: constructing a plurality of risk identification and evaluation indexes considering the source load uncertainty based on the scene probability distribution of a power system, carrying out the fuzzification of the plurality of risk identification and evaluation indexes through employing a self-adaptive bee colony algorithm, and obtaining the risk identification and evaluation result of the power system. Obtaining fuzzification threshold values, corresponding to different operation risk levels, of the risk identification evaluation indexes; and obtaining a plurality of risk identification evaluation indexes corresponding to the current operation data of the power system and corresponding operation risk levels, and comprehensively evaluating the operation risk level corresponding to each risk identification evaluation index to obtain a probabilistic power system risk identification evaluation scheme. According to the method, the plurality of risk identification and evaluation indexes are fuzzified, and the fuzzification threshold values of the risk identification and evaluation indexes corresponding to different operation risk levels are obtained, so that the effectiveness of the risk identification and evaluation indexes of the power system is improved.

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 power distribution network information system risk assessment method and system based on a graph convolutional network, and relates to the technical field of power distribution network information physical systems, and the method comprises the steps: capturing the time dynamic characteristics in a fault propagation process through combining with an LSTM, and precisely assessing the risk level of node voltage out-of-limit; a node voltage out-of-limit condition is taken as a core evaluation standard, and risk distribution in different attack scenes is comprehensively reflected; electrical characteristics of the system are learned under the non-fault condition, the evaluation precision is improved, and power distribution network information system risk evaluation is completed. According to the method, the combined model based on the GCN and the LSTM is introduced, the space-time characteristics in the power distribution network information physical system can be effectively captured, the node voltage out-of-limit risk level can be accurately evaluated, the risk evaluation precision and real-time performance can be remarkably improved, high-risk nodes can be identified, and the risk evaluation accuracy and real-time performance can be improved. And reliable support can be provided for safe operation of the power grid, so that the safety and toughness of the power distribution network are improved.

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 relates to the technical field of power system risk monitoring, and discloses an overheating and insulation fault risk assessment and early warning system under multiple sensing parameters, and the system comprises a data collection module which is used for collecting the operation parameter information and key information of power equipment; the defect evolution rate analysis module is used for carrying out defect analysis on the operation parameter information by adopting a machine learning method and extracting change characteristics of defects along with time; the risk rating module is used for storing division standards of different risk grades and carrying out risk grade division on the change characteristics of the defects along with time to obtain the risk grades of the power equipment; the comprehensive evaluation module is used for identifying the operation state of the power equipment according to the risk level of the power equipment according to a set first fuzzy logic rule and a set second fuzzy logic rule to obtain a risk evaluation result; the early warning module is used for judging whether an early warning mechanism is started or not according to the risk assessment result; according to the invention, the accuracy and reliability of electric power risk early warning and evaluation are 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 invention discloses a power distribution network system risk coupling quantitative analysis method and system, and the method comprises the steps: collecting the operation data of a power distribution network system, and building an information physical fusion system model; determining a potential risk type of the information system, and identifying and quantifying the risk of the information system; establishing an information attack model, and defining a system risk coupling degree and a node risk coupling degree; and performing simulation analysis on the risk coupling degree, and calculating the risk coupling degree of the system and the node under different attack types to complete the risk analysis of the power distribution network system. According to the power distribution network system risk coupling quantitative analysis method provided by the invention, the system risk coupling degree index directly and clearly reflects the risk superposition effect of the information system and the power system under the information attack; the negative influence of the integrity attack on the power distribution network is greater than that of the availability attack, so that the information attack should be emphatically prevented in a power distribution network information system; the security configuration scheme based on the node risk coupling degree can effectively reduce the system risk.

The invention relates to the technical field of information security, and particularly discloses an information security assessment method and system based on a cloud platform, and the method comprises the following steps: S1, obtaining a system operation and maintenance record, and extracting vulnerability data during each operation and maintenance from the system operation and maintenance record; s2, calculating a vulnerability value according to the vulnerability data; coordinate points are generated and fitted to obtain a fitted curve, and the fitted curve is divided into a first curve and a second curve; determining a reference line according to the first curve, and determining a normal proportion according to the position relation between the reference line and the second curve; and S3, calculating a system risk value according to the first average vulnerability value, the second curve and the abnormal proportion, and adjusting a preset operation and maintenance period according to the system risk value. According to the method, the actual operation and maintenance period of the system can be dynamically adjusted, the overall operation and maintenance cost is reduced, and the situation of operation and maintenance service interruption is reduced.