190 results about "Vulnerability evaluation" patented technology

The invention relates to a reliability evaluation method and system for a flexible AC / DC power distribution network containing an optical storage system in the field of flexible AC / DC power distribution networks. The method comprises the following steps: collecting photovoltaic data, energy storage data, AC / DC bus data and the like in real time; constructing a multi-time scale dynamic topology model, and establishing a failure probability distribution function; load flow calculation based on an improved forward-backward substitution method; adopting an N-1 + N-1-1 composite fault screening strategy to simulate a typical disturbance scene; establishing a vulnerability evaluation matrix to calculate power failure probability and other indexes; designing an optimal scheduling strategy based on model predictive control; multi-time-scale reliability evaluation is carried out; and outputting an evaluation report and a topology reconstruction suggestion. The system comprises a data acquisition unit, a dynamic modeling unit and the like, and an evaluation and analysis unit is internally provided with an algorithm module and is configured with an FPGA acceleration card. The method breaks through the limitation of traditional steady state evaluation in the technology level, improves the load flow calculation speed in the calculation efficiency level, reduces the operation cost in the application value level, improves the energy storage efficiency, and provides support for reliability evaluation of a novel power system.

The invention relates to a power distribution network two-stage recovery method based on an electric vehicle and mobile energy storage, and belongs to the technical field of disaster prevention and reduction and emergency recovery of a power system. According to the technical scheme, the method is divided into two stages of recovery: stage 1 pre-disaster pre-configuration and stage 2 post-disaster collaborative recovery; establishing a power distribution network line vulnerability evaluation model, and generating a fault scene in extreme weather based on a wind speed-fault probability curve; constructing a charging station and maintenance station pre-configuration optimization model, and determining the optimal site selection in the traffic network by adopting integer nonlinear programming; establishing a post-disaster recovery mixed integer programming model, coordinating and dispatching the electric vehicle, the mobile energy storage system and the maintenance team, and realizing collaborative optimization of load recovery and line repair; and designing a traffic network and power distribution network coupling mechanism, quantifying a system toughness index and optimizing a resource scheduling strategy. Through multi-resource cooperative scheduling, the post-disaster recovery capability of the power distribution network is remarkably improved, and an innovative solution is provided for improving the toughness of the power distribution network in extreme weather.

The invention discloses an order logistics timeliness management system, and relates to the technical field of logistics intelligence, and the system comprises a risk prediction module which collects order logistics full-link data, carries out the preprocessing of the data, calculates the logistics node delay risk probability through a virtual intervention scene and a causal graph model, and obtains a delay risk prediction result; the visualization module is used for performing overlay analysis on the delay risk prediction result and the preprocessed order logistics full-link data to generate a delay risk thermodynamic diagram; the destroy-resistant module is used for carrying out vulnerability evaluation on the delay risk thermodynamic diagram to generate a vulnerability analysis report, and carrying out multi-objective optimization evaluation through a k-shortest path algorithm to generate a destroy-resistant strategy table; the scheduling module is used for carrying out transport capacity resource matching and scheduling scheme selection by utilizing a multi-target resource optimization model based on the survivability strategy table to obtain a scheduling instruction set; according to the invention, causal traceability and probability quantification of logistics delay risks are realized by constructing a collaborative prediction mechanism of a virtual intervention scene and a causal graph model.

The invention relates to the technical field of power system assessment, and discloses a power system vulnerability assessment method and system considering extreme weather influence, and the method comprises the steps: building a line fault probability model of a power system in extreme weather to determine the line time-varying fault probability under dynamic weather influence; generating a typical scene through a time sequence relativity matching generative adversarial network model with gradient penalty and Monte Carlo simulation; taking the generated typical scene as input, simulating cascading failure evolution through an alternating current cascading failure model, and outputting a cascading failure evolution path; and based on a fault evolution result, through a spectrogram theory and a Bayesian network, in combination with severity analysis, structural vulnerability evaluation and causal-probability pre-judgment, multi-level vulnerability quantification of the power system is realized, and the problems that single-level evaluation is insufficient and causal association is ignored in the prior art are solved. And the risk assessment precision and the disaster prevention capability of the power system in extreme weather are improved.

The invention provides a domain self-adaption-based generative attack and defense confrontation method. Directional interference on a black-box intelligent system is realized by constructing a generative confrontation network framework. The method takes a domain self-adaption theory in transfer learning as a core, builds a dynamic mapping relation between a white box model group and a black box target system, designs a dual-channel generative adversarial network, jointly supervises sample authenticity and cross-model transfer consistency through cross entropy loss and KL divergence constraint, builds a dynamic boundary feedback system, and achieves the dynamic boundary feedback. And a disturbance generation strategy of the generator is adjusted according to the real-time attack effect, and a self-adaptive attack and defense game closed loop is formed. Compared with a traditional black box attack method, the method has the advantages that migration robustness of an adversarial sample in a model heterogeneous scene is remarkably improved, stable attack efficiency can still be maintained when a defense system has protection mechanisms such as adversarial training and input reconstruction, and the method can be applied to the fields such as penetration testing of an intelligent security system and the like. And a technical support is provided for vulnerability evaluation of the intelligent system in a complex environment.

A computing platform provides code vulnerability detection and evaluation. The computing platform may use a quantum graph categorizer along with quantum polymorphism execution channels to reduce false positives and provide optimized real-time vulnerability evaluation of code and code segments. The computing platform may use a hybrid approach comprising a mix of static and dynamic vulnerability validation and real-time parallel technique execution.

The invention provides a physical attribute-external environment fused single building flood vulnerability assessment method and device. According to the method, the physical attribute vulnerability characteristics of the single building are automatically and efficiently extracted based on the deep learning model; adopting an entropy weight method to quantify external environment vulnerability indexes of the single building; a four-quadrant coupling analysis model fusing physical attributes of a single building and an external environment is constructed, a multi-dimensional building flood grading system is established, and visualization and accurate positioning of vulnerability grades are realized in combination with a GIS spatial analysis technology. According to the method, the technical level of urban flood vulnerability assessment is improved, the assessment result is more accurate and efficient, and a scientific basis and technical support are provided for urban flood control and disaster reduction decisions.

The invention provides a supply chain risk mitigation strategy evaluation method based on a multi-criterion decision model, and the method comprises the steps: carrying out the cross analysis of a supplier number weight adjustment coefficient and a component technical parameter, obtaining a technical risk coefficient, and carrying out the analysis of component replaceability data, obtaining a technical path dependency degree and supply source limitation, calculating comprehensive risk scores of the key components, and determining a risk priority sequence; formulating a multi-supplier configuration scheme based on a vulnerability assessment result, assessing supplier distribution and component compatibility, calculating a component interchange rate and a supplier coverage range, obtaining a standardized replacement power according to the interchange rate and the coverage range, and if the standardized replacement power is lower than a threshold value, increasing a supplier number weight; and analyzing supply interruption response time and replacement switching efficiency based on the component interchange rate, the supplier coverage range and the quantity weight, monitoring the supplier capacity utilization rate, measuring and calculating the production maintaining time length and the capacity recovery speed, and obtaining risk relief parameters after dynamic adjustment.

The invention provides a gradation-oriented meteorological disaster risk grading intelligent evaluation and pushing method, and relates to the technical field of meteorological disaster early warning, and the method comprises the steps: obtaining meteorological monitoring data of a plurality of monitoring points in a town, employing a space-time attention mechanism and a dynamic weight mechanism to extract and fuse space-time features and geographic element features, and obtaining a gradation-oriented meteorological disaster risk grading intelligent evaluation and pushing result; generating a meteorological disaster risk level through a bidirectional long-short-term memory network; meanwhile, a vulnerability evaluation model is constructed based on functional area distribution information, and risk levels and vulnerability features are dynamically fused to obtain a comprehensive risk index; and finally, constructing a distributed storage network through a hierarchical cascade block chain, and carrying out encryption and fragmentation pushing on the early warning information. According to the invention, accurate assessment and efficient early warning of the meteorological disaster risk of the calmweight can be realized, and the reliability and timeliness of early warning information are improved.

The invention provides a method, a device and equipment for identifying vulnerability driving factors of disaster-bearing bodies under typhoon disasters and a storage medium. The method comprises the following steps: obtaining a comprehensive data set according to obtained historical typhoon disaster intensity data and historical typhoon disaster loss characterization data of a plurality of areas; obtaining an optimal function and a target disaster-causing intensity variable according to the comprehensive data set and a plurality of preset candidate functions; according to the optimal function and the target disaster-causing intensity variable, obtaining a disaster-bearing body loss rate and a disaster-bearing body absolute loss value; performing typhoon disaster vulnerability assessment according to the disaster-bearing body loss rate and the disaster-bearing body absolute loss value to obtain a typhoon disaster vulnerability assessment result; and according to the typhoon disaster vulnerability assessment result and a plurality of preset driving factors, determining an identification result of the vulnerability driving factors of the disaster-bearing body under the typhoon disaster. According to the invention, the accuracy of typhoon disaster vulnerability assessment and driving factor identification can be improved, and the pertinence and scientificity of typhoon disaster risk management are effectively improved.

The invention discloses a dynamic verifiable fuzzy multi-keyword cloud ciphertext search method based on a block chain, and relates to the technical field of cloud native security. According to the method, full-process automatic analysis from vulnerability collection to attack graph generation to vulnerability evaluation is realized by combining vulnerability data of CNNVD, Docker container information and various vulnerability analysis algorithms. The core part in the design is attack graph generation and vulnerability analysis, and the part aims to comprehensively discover vulnerabilities existing in the system as much as possible and evaluate potential safety influences of the vulnerabilities. Through fusion evaluation of the analytic hierarchy process and the improved BP algorithm, the accuracy of vulnerability evaluation can be ensured, and the purpose of providing deeper analysis for the overall security condition of the system through the prediction capability of the neural network can be achieved. According to the multi-angle evaluation mode, the comprehensiveness and scientificity of vulnerability evaluation can be improved, so that a security team is helped to manage security risks in the system more effectively.

The invention discloses a single landslide risk quantitative evaluation method and system. The method comprises the following steps: acquiring landslide topographic files and landslide rock-soil body physical parameters; the method comprises the following steps: simulating a motion intensity field in a landslide evolution process through MassFlow software to obtain the maximum motion intensity; establishing a multi-level multi-dimensional vulnerability evaluation system, and extracting disaster-bearing body index data in the landslide risk area according to the vulnerability evaluation system; according to the disaster-bearing body index data, carrying out quantitative evaluation on the vulnerability of the disaster-bearing body based on an AHP-entropy weight method coupling model to obtain vulnerability values of different disaster-bearing bodies in a landslide motion range; according to the maximum movement intensity and the vulnerability value of the disaster-bearing body, the landslide risk is quantitatively calculated based on the spatial relation between the risk and the vulnerability, and the risk partition conditions of different disaster-bearing bodies under the landslide starting damage threat are obtained. According to the method, the popularization applicability and the calculation efficiency of the quantitative evaluation method for the risk under the single landslide scale are effectively improved.

The invention relates to the technical field of metro key station identification and vulnerability evaluation methods, in particular to a metro key station identification and vulnerability evaluation method, which specifically comprises the following steps: step 1, metro network construction: establishing a weighted metro network model by adopting a Space-L space method according to a topological relation between metro stations and intervals, taking stations as nodes, intervals as edges and passenger flow as edge weights to form a weighted graph structure reflecting passenger travel rules, constructing an OD passenger flow matrix based on subway passenger flow card swiping data, and quantifying passenger flow connection strength between different stations; according to the method, the time-varying importance evaluation of the dynamic Domiank is combined with the feature learning ability of the graph convolutional network, and meanwhile, the structural complexity and passenger flow dynamic features of the subway network are described, so that the high-precision identification of the key stations and the comprehensive evaluation of the network vulnerability are realized.

The invention relates to the technical field of hydropower station lightning protection, and relates to a hydropower station power transmission line lightning stroke risk assessment method comprising the following steps: based on power transmission line tower footing types and position information, combining a preset height threshold to divide lightning stroke types, and marking high-risk candidate tower footing according to protection range parameters of a lightning suppressor; operating parameters and overvoltage monitoring data of the lightning suppressor are collected in real time, environmental factors and tower footing insulation configuration differences are fused, and a tower footing correlation model is constructed; carrying out vulnerability evaluation on the high-risk candidate tower footing by adopting the tower footing type correction coefficient; constructing a lightning stroke risk assessment model for the tower footing exceeding the preset height threshold, optimizing risk assessment model parameters by using the overvoltage monitoring data, and generating a risk assessment result; and correcting protection range parameters according to lightning stroke event feedback, and iteratively updating the risk assessment model in combination with historical operation data of the lightning suppressor. Lightning stroke risk assessment of the whole hydropower station power transmission line is realized, and lightning stroke risk assessment efficiency and accuracy are improved.

The invention provides a system stability evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-dimensional data of a distributed system, the multi-dimensional data comprising system performance fluctuation data of a time dimension, node connection relation data of a space dimension and service protocol index data of a business dimension; multi-dimensional analysis is carried out according to the system performance fluctuation data, the node connection relation data and the service protocol index data, and multi-dimensional analysis results are obtained and comprise a performance fluctuation prediction result, a topology vulnerability evaluation result and a service influence degree evaluation result; and generating a system stability score according to the plurality of dimension analysis results. Therefore, the accuracy of stability evaluation in the distributed environment is improved.

The invention discloses a digital twinning long-span bridge earthquake wind vibration and vibration reduction and isolation system and method, the system takes a high-fidelity digital model for constructing a long-span bridge as a basis, comprises full life cycle monitoring and management of the bridge under earthquake, wind vibration and vibration reduction and isolation conditions, and is based on a sky-air-ground-water integrated ubiquitous sensing module. In combination with artificial intelligence algorithms such as deep learning and a pulse neural network, real-time state evaluation of a bridge structure, material nonlinear analysis, geometric nonlinear analysis, multi-point excitation and pile-soil-structure interaction simulation, wind-induced fatigue damage prediction, earthquake vulnerability evaluation and closed-loop control scientific decision based on an artificial intelligence body are realized. According to the invention, system support and method guidance can be provided for large-span bridge anti-seismic and anti-wind design, intelligent monitoring, operation and maintenance management and vibration reduction and isolation system optimization.

The invention relates to the technical field of financial risk early warning, and particularly discloses an intelligent financial risk early warning method and system based on a management decision, and the method comprises the steps: carrying out the fund chain vulnerability evaluation analysis of enterprise financial operation data, and obtaining the fund chain vulnerability evaluation data; third-party institution credit evaluation data introduction processing is carried out based on the third-party institution credit evaluation data to obtain third-party credit evaluation data; performing enterprise fund chain vulnerability dynamic simulation according to the third-party credit evaluation data to obtain fund chain vulnerability evolution trajectory data; performing fund chain fracture risk conduction effect quantification based on the fund chain vulnerability evolution trajectory data to obtain a fund chain fracture risk conduction effect index; performing financial risk prefactor identification according to the fund chain fracture risk conduction effect index to obtain financial risk prefactor learning data; the accuracy and the real-time performance of fund chain vulnerability identification are remarkably improved, and necessary technical support is provided for constructing an intelligent enterprise financial risk control system.

The invention discloses a cascade failure-based double-layer international railway freight network toughness evaluation method, which belongs to the technical field of road traffic network evaluation, and comprises the following steps: constructing topology models of a basic line network and a transportation service network; constructing a network evaluation system and an importance evaluation system of each node in the network; constructing a network vulnerability evaluation model based on cascade failure, wherein the model adopts an improved CML node state model and a cargo redistribution strategy; and in combination with each network and the evaluation model, a cascade failure condition is simulated through a simulation experiment, and the network is evaluated in combination with an evaluation system. The characteristics of international railway freight are considered, a double-layer coupling network of a basic line network and a transportation service network is constructed, a multi-dimensional toughness evaluation index system and a cargo distribution mechanism are set up, and the cascade failure condition in the international railway freight network can be simulated.

Systems and methods provide agentless security assessment for workloads and cloud posture control across multi-cloud environments. Discovery modules are configured with collection intervals to ingest posture control data including assets, identities, configurations, activities, network flows, and build-time artifacts. A multi-cloud configuration inventory maintains current and historical states and produces misconfiguration and identity-activity findings. For workload vulnerability evaluation, an external snapshot manager obtains point-in-time root-disk state without installing an in-workload agent. A file system data processor derives operating system and package metadata, and a detector matches the metadata against a vulnerability feed refreshed on a recurring basis to identify vulnerabilities. Identified vulnerabilities are correlated with misconfiguration and activity findings to generate prioritized risk exposures. Results are stored per workload and presented through graphical user interfaces that display risk levels, timelines, alerts, and guided remediation, enabling continuous, low-overhead security coverage for cloud workloads and configurations.

The invention discloses an earthquake and secondary disaster vulnerability assessment method in combination with multiple agents and deep learning, and the method comprises the following steps: (1), collecting and preprocessing data related to an earthquake and an infrasound disaster thereof, and obtaining multi-source static data; key features are extracted from the multi-source static data; step (2), constructing a hierarchical structure index system of vulnerability assessment by utilizing expert knowledge, constructing a judgment matrix by adopting an analytic hierarchy process, and calculating the weight of each index to obtain an AHP weight vector; step (3), multi-agent system construction and disaster propagation dynamic simulation; step (4), designing a GCN + Transform deep learning model, and combining the multi-source static data with the dynamic evolution data to construct a spatial-temporal feature tensor as the input of the GCN + Transform deep learning model; and step (5), outputting a vulnerability evaluation result. According to the method, the problems of the existing earthquake and secondary disaster vulnerability assessment precision and timeliness can be solved.

A distributed photovoltaic-containing low-voltage transformer area vulnerability evaluation method, system, terminal and medium are characterized in that the method comprises the following steps: constructing a low-voltage transformer area state vulnerability evaluation index system, collecting and utilizing power distribution network operation parameters, and calculating vulnerability evaluation indexes by the low-voltage transformer area state vulnerability evaluation index system; constructing the vulnerability evaluation indexes into a decision matrix, and calculating adjustment coefficients for different indexes in the decision matrix so as to obtain correlation coefficients between the indexes and the information amount of each index; calculating the objective correction weight of each index by using the information amount and the correction variance of each index, and collecting the subjective weight of each index to obtain the comprehensive weight of each index; and determining the optimal solution and the worst solution of each index in the decision matrix by adopting a TOPSIS model, and evaluating the vulnerability degree of the low-voltage transformer area through the comprehensive weight and the distance between the current index and the optimal solution as well as the worst solution.

The invention discloses an e-commerce logistics distribution system based on an intelligent supply chain, and particularly relates to the technical field of scheduling optimization and path planning. The method comprises the following steps: performing dynamic demand fluctuation analysis on user order data to generate an order fluctuation feature vector; and performing path stability evaluation on the distribution network node state data to generate a path vulnerability evaluation value. And based on the order fluctuation feature vector and the path vulnerability evaluation value, performing conjoint analysis in combination with a preset rule, and outputting a path adjustment type determination result. The real-time position data and the road network temporary constraint data of the logistics vehicle are collected, and the distribution path decision is executed in combination with the path adjustment type judgment result, so that the stability of the distribution path decision and the overall efficiency of logistics distribution are improved.

The application discloses a kind of industrial control system vulnerability evaluation methods, comprising: constructing several minimum check units for industrial control system vulnerability evaluation, each minimum check unit is configured evaluation function and the core check item of industrial control system, and the statistical function and weight parameter are configured for each core check item;Each minimum check unit is executed, and the check result of minimum check unit is evaluated by evaluation function, and the evaluation score corresponding to each minimum check unit is obtained;According to the statistical function corresponding to each core check item, the evaluation score of corresponding minimum check unit is processed, and the statistical score of each core check item is obtained, and the vulnerability score is calculated according to the statistical score of each core check item and weight parameter, and then the vulnerability of industrial control system is evaluated according to the vulnerability score, which can evaluate the vulnerability of industrial control system.

The invention discloses a digital archive management vulnerability assessment method and system based on multi-dimensional influence factors, and relates to the technical field of digital information management, and the method comprises the steps: collecting an internal data integrity verification result, external file format support data and an associated source flow record of a digital object; calculating a format outdated risk index, a data integrity attenuation degree index and a source flow chain vulnerability index; calculating the weighted sum of the format outdated risk index, the data integrity attenuation index and the source flow chain vulnerability index to generate a comprehensive vulnerability index; and comparing the comprehensive vulnerability index with a preset vulnerability response threshold value, and triggering a disposal process according to a comparison result. According to the method, accurate and prospective measurement of the vulnerability of the digital archives is realized through a multi-dimensional and dynamically quantitative evaluation model, and a key decision basis is provided for an automatic and verifiable long-term storage strategy in combination with a strategy-driven adaptive weight, so that the scientificity and initiative of risk management are remarkably improved.

The invention discloses a partition blasting construction analysis method for a tunnel crossing a cultural relic protection area, and relates to the technical field of blasting construction, and the method comprises the steps: building a three-dimensional geologic model and a cultural relic three-dimensional model, and constructing a finite element modal analysis environment of the blasting construction of the cultural relic protection area; the method comprises the following steps: applying a cultural relic resonance frequency in a finite element modal analysis environment of blasting construction in a cultural relic protection area, determining a limit damage threshold value of a cultural relic and a cultural relic danger node, and calculating an initial limit value of a blasting vibration safety speed according to the limit damage threshold value of the cultural relic and the cultural relic resonance frequency; calculating a cultural relic vulnerability evaluation index according to the cultural relic vulnerability influence data, and correcting the blasting vibration safety speed initial limit value to obtain a blasting vibration safety speed correction limit value; and delimiting a three-level risk area according to the cultural relic vulnerability evaluation index, and calculating and outputting the explosive quantities of the buffer area and the general construction area by using a Sadgowski formula as a vibration attenuation model. Construction vibration is ensured to be within a safety threshold, and the risk of damage to cultural relics is reduced.

The invention discloses a multi-modal stability identification and evaluation method and system for a transformer substation monitoring system. The method comprises the following steps: evaluating static model vulnerabilities in a system configuration file based on the transformer substation monitoring system; dynamically linking a transformer substation monitoring system through an application system, performing application function test on the monitoring system by utilizing a dynamic linkage test case, and performing dynamic vulnerability evaluation on the model; and monitoring system application function module faults through verification function logic, memory leakage, alarm coverage rate and system fault tolerance verification, and carrying out positioning inversion. Technical difficulties existing in application scenes such as equipment research and development, network access detection, transformer substation design, factory joint debugging, field debugging, operation and maintenance and the like of an intelligent transformer substation monitoring system can be taken as entry points, and model vulnerability detection, data flow section detection and function and performance defect testing can be carried out; and a defect panoramic evaluation means suitable for a novel power system intelligent substation monitoring system is formed.

The embodiment of the invention discloses a bridge pier vulnerability evaluation method under ship impact. The method comprises the steps that pier bottom tensile stress threshold values of a bridge pier in different damage states under ship impact are acquired; utilizing an optimal Latin hypercube sampling method to extract a plurality of parameter combinations from a core parameter space influencing the tensile stress of the pier bottom; calculating a pier bottom tensile stress corresponding to each parameter combination by using a pier finite element model under ship impact, and fitting an initial proxy model of the pier bottom tensile stress; according to the prediction precision of the initial proxy model, supplementing and extracting a plurality of parameter combinations in a parameter space region in which a prediction error or a function nonlinearity degree is greater than a corresponding threshold value; the complemented and extracted parameter combinations are used for returning the calculation operation by using the finite element model to obtain a new agent model; repeating the steps until the prediction precision of the final agent model meets the requirement; and fitting a bridge pier vulnerability curve for each damage state. The embodiment of the invention better conforms to the damage mechanism of concrete cracking and structure failure.

The application discloses a kind of partitioned blasting construction analysis methods for tunnel crossing cultural relic protection zone, it is related to blasting construction technical field, including establishing three-dimensional geological model and cultural relic three-dimensional model, the finite element modal analysis environment of cultural relic protection zone blasting construction is constructed;Under the finite element modal analysis environment of cultural relic protection zone blasting construction, the limit damage threshold of cultural relic and cultural relic dangerous node are determined by applying cultural relic resonance frequency, according to the limit damage threshold of cultural relic and cultural relic resonance frequency, the initial limit value of blasting vibration safety speed is calculated, and the blasting vibration safety speed correction limit value is obtained by correcting the initial limit value of blasting vibration safety speed according to the cultural relic vulnerability evaluation index calculated by the vulnerability influence data of cultural relic;According to the three-level risk area of cultural relic vulnerability evaluation index, the output is calculated by using the Sadovsky formula as the vibration attenuation model for the explosive quantity of buffer zone and safety zone.It is ensured that construction vibration is within the safety threshold, and the risk of cultural relic damage is reduced.

The present application relates to the technical field of industrial control, and more particularly to a DCS autonomous upgrade method and system for redundancy control and fault prediction. A fault evolution graph is constructed by collecting fault event sequences and redundancy switching trajectories in the upgrade cycle, a redundancy configuration vulnerability evaluation model is established by extracting cross-cycle implicit fault modes, a multi-objective optimization constraint is generated by quantifying the vulnerability score of the current topology input model, an evolutionary algorithm is used to reconstruct the redundancy layout and communication path weight to generate a reinforcement scheme, and new data is backfilled to update the model after execution of the upgrade, thereby improving the accuracy of redundancy control and fault prediction during the upgrade process.