148 results about "Vulnerability evaluation" patented technology

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 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 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 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 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.

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 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.

The invention relates to the technical field of internet analysis, in particular to an internet vulnerability evaluation and analysis system, which comprises the following steps of: acquiring original vulnerability data, binding an evaluation terminal equipment fingerprint, an operator biological characteristic hash, a network environment snapshot and generating a vulnerability responsibility label; stripping an environment dependency item in the vulnerability responsibility label, replacing the environment dependency item with a cross-platform equivalent function, and generating a repair instruction chain; executing the restoration instruction chain in a target environment, and capturing restoration behavior offset; and associating the system repair behavior offset with the vulnerability responsibility label, reversely positioning a responsibility node and generating a traceability tree. The method not only breaks through the information splitting problem that the repair behavior of the traditional system cannot reversely deduce the responsibility attribution, but also supports the positioning of the responsibility node through the traceability tree, and provides a matched evidence data packet.

This invention discloses an e-commerce logistics and delivery system based on an intelligent supply chain, specifically relating to the fields of scheduling optimization and route planning. It generates an order fluctuation feature vector by dynamically analyzing user order data for demand fluctuations and generates a route vulnerability assessment value by evaluating the route stability of delivery network node status data. Based on the order fluctuation feature vector and the route vulnerability assessment value, a joint analysis is performed using preset rules to output a route adjustment type determination result. Real-time location data of logistics vehicles and temporary road network constraint data are collected, and combined with the route adjustment type determination result, delivery route decisions are executed, improving the stability of delivery route decisions and the overall efficiency of logistics and delivery.

The application relates to a power grid cyber physical system protection method. The method comprises the following steps: determining an attack scene based on the structure of a power grid cyber physical system (CPPS) interaction model, wherein different attack scenes correspond to different attack targets and defense targets; obtaining a CPPS vulnerability evaluation framework based on the defense target of the attack target in different attack scenes; obtaining a CPPS active load loss model based on Newton-Raphson power flow calculation equations and preset power constraint conditions; obtaining the defense resources allocated to the attack target based on the CPPS vulnerability evaluation framework and the CPPS active load loss model; establishing a CPPS attack and defense game model and designing an attack and defense game scene based on the structure of the CPPS interaction model and the defense resources allocated to the attack target; and obtaining a CPPS protection scheme based on game theory. The method can consider the structure of the power grid cyber physical system interaction model and give an optimal defense resource allocation strategy.

The invention discloses a single event upset irradiation test method based on a modularized fifth-generation reduced instruction set soft core. The method is implemented on a ZYNQ chip. The method comprises the following steps: firstly, dividing a fifth-generation reduced instruction set soft core into a plurality of independent circuit modules according to functions, wherein the independent circuit modules comprise a decoder module, a jump processing module, an access control module, an arithmetic logic unit module, a data path module, a static memory module and a write-back circuit module; in the irradiation process, a program burnt by the ZYNQ chip outputs a running result data stream of each module to the upper computer in real time; the upper computer compares the received data with a pre-stored module expected correct value in real time; once data mismatch is found, an error event and a timestamp corresponding to the error event are immediately recorded, and circuit resources occupied by an error event occurrence bit are reversely positioned, so that module-level accurate positioning and vulnerability evaluation of errors caused by single event upset in the soft core are realized. The problem that the error source recognition rate is low when the whole soft core is tested is solved, and the fault resolution and the testing efficiency are remarkably improved.

The application discloses a kind of intelligent vehicle information security vulnerability evaluation method and system, non-transitory machine-readable medium, data processing system and car machine system, to analyze the security vulnerability possibly existing in vehicle ECU, so that evaluation personnel can master whether there is a vulnerability and the level of vulnerability in time.The intelligent vehicle information security evaluation method comprises: reading data stream from the electronic control unit of the vehicle through the CAN bus;Based on the data stream obtained, generate vulnerability assessment information for assessing the level of vulnerability;And according to the level of vulnerability of vulnerability assessment information, issue early warning information corresponding to vulnerability assessment information.

The invention belongs to the technical field of geological disaster risk assessment and urban safety disaster prevention, and particularly discloses an urban karst surface collapse risk assessment method and a storage medium, and the method comprises the steps: screening out dominant factors from a plurality of susceptibility evaluation indexes through a geographic detector, and carrying out the quantification of each dominant factor; quantizing the plurality of induction factors, and inputting the quantized dominant factors and induction factors into a support vector machine model to obtain a risk evaluation result; quantifying the plurality of vulnerability evaluation indexes, performing combined weighting by using an analytic hierarchy process and an entropy evaluation method to determine the weight of each vulnerability evaluation index, and calculating the vulnerability index of the research area based on the weights; and based on the risk evaluation result and the vulnerability evaluation result, superposition is carried out through the same risk level discrimination matrix to form a risk evaluation result, and the research area is divided into a plurality of risk levels. Evaluation indexes can be scientifically screened, human activity induction factors are fused, and high precision and strong interpretation are achieved.

The invention provides a power distribution network weak link identification method and system considering photovoltaic output uncertainty, and relates to the technical field of power distribution network safety. Comprising the following steps: establishing a node comprehensive vulnerability index and a line comprehensive vulnerability index; setting node degrees, node betweenness and node voltage out-of-limit risk indexes in the node comprehensive vulnerability indexes; setting line degree, line betweenness and line current-carrying capacity out-of-limit risk indexes in the line comprehensive vulnerability indexes; and setting a target layer and a factor layer based on the node comprehensive vulnerability index and the line comprehensive vulnerability index, calculating each factor weight of each layer by using an analytic hierarchy process, obtaining a comprehensive vulnerability evaluation result of the node and the line through index weighting, and determining the weak node and the line of the power distribution network. According to the method, the weakness evaluation index of the element state of the power distribution network is improved, the characteristic of high-proportion access of the topological characteristics of the power distribution network and renewable energy sources is incorporated, and the weak links of the nodes and lines of the power distribution network are quantified more comprehensively and accurately.

The present application provides a kind of vulnerability assessment and vulnerable node identification method for regional bridge and tunnel network security operation and maintenance.The method comprises the following steps:1, the basic information of regional bridge and tunnel is preprocessed and integrated, and regional bridge and tunnel network is established;2, based on graph theory and complex network theory analysis bridge and tunnel network characteristics, get the key node of bridge and tunnel network topology structure;3, comprehensive accessibility and network efficiency, select appropriate vulnerability index, take different node failure strategy, calculate the corresponding relationship of node removal ratio and vulnerability index;4, calculate the overall vulnerability of bridge and tunnel network, analyze the vulnerability of network and record the vulnerability of each bridge and tunnel node;5, using Monte Carlo sampling method, through multiple random calculation of the vulnerability of bridge and tunnel node, identify the vulnerable node of bridge and tunnel network.The present application is suitable for the topological analysis, vulnerability analysis and key vulnerable node identification process of general regional bridge and tunnel network.

The invention discloses a tunnel structure earthquake vulnerability evaluation method and device based on multi-model integration and interpretability analysis. The method comprises the following steps: acquiring tunnel parameters, geological conditions and seismic oscillation parameters, and constructing a training database; training is carried out based on various machine learning models such as extreme gradient lifting, random forest, a support vector machine and a neural network, and multiple model outputs are fused by adopting an integrated learning strategy; inputting seismic oscillation characteristics of a tunnel to be evaluated and a target, and outputting damage levels, damage probabilities or seismic vulnerability curves of the tunnel under different seismic intensities; and identifying key influence factors by utilizing feature importance analysis, Shapley additive interpretation or sensitivity analysis, and generating an analysis report containing a prediction result and interpretation information. The method has the advantages of high prediction efficiency, high result interpretability, high robustness and wide application range, and can be widely applied to aseismic design and safety evaluation of tunnels and other underground structures.

The invention discloses a system vulnerability assessment method and device and a readable storage medium, and the method comprises the steps: collecting the resource indexes of nodes in a system, and constructing a node communication topological graph based on the communication behaviors of all nodes in a cluster; the resource indexes comprise the CPU utilization rate, the memory occupancy rate, the network throughput and the disk I / O of the node; calling chain data are collected, a directed dependency graph between services is constructed based on the calling chain data, and the calling chain data comprise calling source service ID, called service ID, calling frequency in cycle time, average calling time delay, calling error rate and timestamp; calculating a vulnerability score of the node based on the resource index of the node, and calculating a vulnerability score of the micro-service based on the call chain data; and calculating an overall vulnerability index of the system based on the vulnerability score of the node, the vulnerability score of the micro-service and a preset weight value. According to the method, comprehensive assessment of the system vulnerability can be effectively carried out.