84 results about "Vulnerability evaluation" patented technology

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 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 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 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 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.

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 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.

A cloud-edge-end power internet of things vulnerability hazard evaluation method and system. The method comprises the following steps: determining an influence factor score and a exploitability factor score of a target vulnerability based on the type of the target vulnerability; calculating a weight using an entropy weight method to determine a basic characteristic evaluation value; determining a time influence evaluation value according to the average time of the target vulnerability being exploited and the number of days of existence; obtaining the data value of a cloud server, the computing resource value of an edge server and the device usage rate of a terminal device, calculating the cloud server value, the edge server value and the terminal value in combination with the deployment quantity and the business value, determining a network asset risk evaluation value based on a bidirectional penetration attack graph; and evaluating the hazard level of the target vulnerability based on the basic characteristic evaluation value, the time influence evaluation value and the network asset risk evaluation value. The scheme improves the accuracy and reliability of the cloud-edge-end power internet of things vulnerability evaluation.

The application relates to the technical field of tunnel engineering, and provides a tunnel engineering earthquake vulnerability evaluation method and system based on explainability analysis, which comprises the following steps: acquiring tunnel characteristics, obtaining a tunnel earthquake damage index through a tunnel earthquake vulnerability intelligent evaluation model based on small sample learning; adopting a Shapley additive feature explanation method and a local explainable model agnostic explanation method to respectively calculate the importance scores of the tunnel characteristics on the tunnel earthquake vulnerability intelligent evaluation model prediction; and adopting a weighted geometric mean to fuse the two importance scores to obtain a fusion index importance; wherein the weights of the weighted geometric mean are calculated according to the prediction reconstruction errors of the Shapley additive feature explanation method and the local explainable model agnostic explanation method on a local perturbation data set. The contribution degrees of the influence parameters can be quantitatively and reliably revealed.

The invention discloses a vulnerability assessment system, method, device and equipment. According to the system, a multi-agent framework is adopted, a vulnerability utilization generation process is decomposed into action agents such as vulnerability detection, function input constraint extraction, vulnerability utilization code generation and vulnerability verification, and a task supervision agent is responsible for iteration coordination work of the vulnerability utilization generation process. In the task execution process, potential vulnerabilities are identified through a vulnerability detection agent; performing input constraint extraction on a function in the stain propagation path through a constraint extraction agent; generating a vulnerability utilization code through the code generation agent; and the vulnerability authenticity is verified through the vulnerability verification intelligent experience. By the adoption of the processing mode, vulnerability utilization code generation based on a large language model is achieved, function input constraints described by natural languages can be extracted, construction of function complex input is supported, specific control flow constraints are met, and a deep execution path is covered; therefore, the vulnerability assessment accuracy can be effectively improved.

The invention relates to the technical field of digital archive management and information security, in particular to a digital archive management vulnerability assessment method and system. The system comprises a data acquisition module, a semantic extraction module, a potential energy calculation module, an entropy analysis module and a hierarchical defense module. The system extracts semantic vectors by using a large language model and constructs a topological network; the core of the method is that local semantic potential energy is solved according to semantic coupling degree and edge reputation weight, abnormal probability is obtained through Boltzmann distribution, and a traceability computing power distribution coefficient is generated based on a Fermi-Dirac model; executing hierarchical defense and updating the weight according to the abnormal probability and the topological entropy; according to the method, the limitation of traditional Hash verification is broken through, the crossing from byte physical detection to semantic logic level forgery identification is realized, and the logic integrity of archives is ensured.

The invention discloses a cascading failure evolution and fragile path identification system and method for an AC / DC power grid containing new energy, and relates to the field of power system safety analysis. The system comprises a power system modeling module, an element fault modeling module, a vulnerability evaluation index module, a cascading fault simulation module and a vulnerability path identification module. A power system and component fault model containing new energy and alternating current and direct current hybrid connection is constructed, a random scene is generated through Monte Carlo sampling on the basis of an entropy weight method fusion structure, power flow and voltage multi-dimensional vulnerability index, cascading fault evolution is simulated to generate a fault chain, a key vulnerable path and an alternating current and direct current strong coupling risk line are accurately identified, and the reliability of the system is improved. And the influence of uncertain factors such as new energy output fluctuation is quantified. According to the method, traditional analysis limitation is overcome, fault evolution can be truly simulated, the fragile path identification accuracy is improved, and a quantitative basis is provided for power grid planning and operation control.

The invention discloses a traditional village fire scene cascade failure road vulnerability assessment method, and relates to the technical field of building group fire prevention and control and personnel evacuation. The method comprises the following steps: firstly, acquiring space vector data of traditional village building groups, single buildings and roads, and constructing a building group fire spreading network and a road network; then, constructing a road vulnerability evaluation system, introducing an accessibility index to dynamically represent the road traffic efficiency, and defining the vulnerability index to quantify the influence of the disaster on the road system; finally, a multi-layer network cascade failure simulation framework is provided, the framework can quantify the dynamic cascade failure process, and a multi-scene vulnerability thermodynamic diagram is generated to locate a high-risk area. According to the method, the influence of the traditional village building fire on the road network is fully considered, the fire-road double-layer coupling network model is established, dynamic quantification is realized, a scientific decision-making tool is provided for the traditional village building fire and the road evacuation risk, and the method has important application value for improving the toughness of the traditional village.

The invention discloses an electric power monitoring system vulnerability index prediction method based on text classification and a related device, and belongs to the technical field of electric power monitoring system vulnerability evaluation. The method comprises the following steps: firstly, constructing an evaluation system comprising specific environment indexes of a power system, extracting deep semantic features of a vulnerability description text through a lightweight pre-training language model DistilBERT, and realizing parallel prediction of multiple indexes such as attack vectors and permission requirements by adopting a multi-task learning framework. And carrying out interpretability analysis on a model decision process by combining an integrated gradient algorithm, and identifying key semantic features influencing a prediction result. According to the method, the automation level and the result credibility of vulnerability assessment are effectively improved, and intelligent technical support is provided for security protection of power key information infrastructures.

The application provides a vulnerability evaluation method based on multi-model rejection sampling and reinforcement learning alignment, and belongs to the technical field of computers, and solves the technical problem of poor vulnerability evaluation effect.The technical scheme comprises the following steps: S1: constructing a vulnerability data set, preprocessing source code, extracting vulnerability description, source code and corresponding labels; S2: through a multi-model rejection sampling strategy; S3: supervised fine-tuning training, optimizing the understanding ability of the model to vulnerability characteristics; S4: based on a group relative strategy optimization method, improving the reliability and consistency of the model output; S5: inputting the double-mode information of the target vulnerability into the trained model to obtain the final result.The application constructs a high-quality data set through multi-model rejection sampling, optimizes the model output in combination with a reinforcement learning alignment mechanism, and significantly improves the accuracy of vulnerability evaluation.

The invention discloses a multi-disaster-oriented environment coupling type power grid vulnerability assessment method and system, and relates to the technical field of fault assessment. Firstly, a power transmission line is decomposed into an electric tower node and a virtual node, so that the linear damage calculation complexity is reduced, power grid second-level time sequence simulation is supported, and the power grid vulnerability assessment efficiency is improved; secondly, by constructing a multi-disaster unified coupling mechanism: constructing a geographic matrix, an environment matrix and a meteorological matrix, driving a plurality of disaster damage models, and realizing joint quantification of a disaster chain physical process; and finally, by taking the topology power supply availability rate and the cascade failure fault condition as damage measurement indexes, power grid vulnerability evaluation is performed in an earthquake disaster scene and a storm disaster scene, and the problem of fault underestimation caused by neglecting power redistribution in a traditional model is solved.

The invention relates to the technical field of urban intelligent management and control, and provides an urban composite environment health risk assessment and intervention method and system, and the method comprises the steps: obtaining the geographic space data and ground observation data of an urban region; constructing a collaborative environmental stress index, and calculating an SESI predicted value; performing grid construction on the urban area to construct a graph structure, performing feature learning by using a GNN algorithm, predicting a node SESI value, and generating a collaborative stress map; constructing a comprehensive vulnerability evaluation model, calculating a vulnerability index, and identifying a high-vulnerability region; fuzzy comprehensive evaluation and TOPSIS methods are fused, intervention measures of the high-fragility area are sorted, and an intervention scheme is generated. According to the method, the nonlinear interaction modeling and the high-resolution graph neural network are fused to realize accurate recognition of the composite environment health risk, and the intervention strategy is optimized through the multi-criterion decision model, so that scientific regulation and dynamic optimization of the urban intervention scheme are realized.

The invention relates to the technical field of unmanned aerial vehicle safety, provides an unmanned aerial vehicle confrontation vulnerability multi-dimensional analysis and defense method, and aims to solve the problem that an existing unmanned aerial vehicle safety assessment method is insufficient in reliability in an actual confrontation environment due to lack of pertinence of a defense strategy. According to the main scheme, the method comprises the steps of constructing a multi-dimensional anti-vulnerability evaluation framework, including establishing a diversified anti-attack model library including white-box attack, black-box attack, targeted attack and target-free attack, constructing a multi-dimensional evaluation index system covering attack success rate, system performance reduction and task completion degree, and developing a high-fidelity simulation environment; on the basis of the multi-dimensional evaluation framework, performing a vulnerability resistance test on the unmanned aerial vehicle system, quantitatively analyzing vulnerability characteristics under each attack model by using a statistical method, and mining key factors influencing the system robustness resistance; and designing a targeted defense strategy set for the key factors, and constructing an integrated defense framework by dynamically optimizing weight coefficients to maximize the defense effect.

The invention discloses an abnormal traffic analysis method based on big data analysis, and the method comprises the following steps: S1, collecting edge traffic, verifying the authority, and generating a traffic data set passing verification; s2, executing structured preprocessing, extracting features, and constructing a traffic data set in a unified format; s3, performing block coding processing and symmetric encryption operation, introducing differential privacy, and generating a traffic feature data set; s4, packaging the traffic feature data in batches, and transmitting the traffic feature data to the cloud to form a global traffic feature data pool; s5, constructing a traffic behavior reference model, and generating a feature distribution description result; s6, executing deviation calculation and mode comparison, and identifying an abnormal traffic candidate set; and S7, executing vulnerability assessment analysis, generating a corresponding vulnerability risk score and outputting an abnormal traffic judgment result. According to the invention, efficient, real-time and accurate anomaly detection of large-scale network traffic is realized, and data security and privacy are guaranteed.

The present application relates to the technical field of power grid dispatching, and specifically provides a power distribution network vulnerability assessment and collaborative dispatching method, device and medium, the method comprising: based on the historical data and prediction error distribution of new energy output in the power distribution network, a Monte Carlo simulation is used to sample and generate multiple scenes with occurrence probability; according to the multiple scenes, a two-stage stochastic programming model is constructed with the comprehensive minimization of power distribution network operation cost and conditional risk value as the target; wherein the two-stage stochastic programming model includes a day-ahead pre-decision stage, a real-time balancing stage and a risk measurement item; the two-stage stochastic programming model is solved to obtain the vulnerability assessment result and collaborative dispatching scheme of the power distribution network within the target time. The present application method significantly improves the economy and safety of the power distribution network operation under the high proportion of new energy access scene through deep integration of uncertainty modeling and risk quantification technology.

The application relates to a kind of vehicle engine system electromagnetic vulnerability evaluation methods, belong to electromagnetic environment effect evaluation field.Establish the engine system bayesian network fault model, get the triangular fuzzy number corresponding to the fuzzy expression of the failure correlation between each node in the bayesian network fault model through semantic contrast table, calculate fuzzy conditional probability, convert it into interval grey number conditional probability;Determine electromagnetic stress probability density function and component sensitivity threshold probability density function, calculate component layer interval grey number failure probability, and calculate engine system interval grey number failure probability in combination with interval grey number conditional probability.Affirmative effect is: determine the weight of each sensor through fuzzy analytic hierarchy process, solve the shortcoming that traditional analytic hierarchy process is more subjective, combine grey system theory and fuzzy theory with bayesian network, can more accurately express the uncertain information in the whole evaluation process and carry out failure probability calculation.

This application belongs to the fields of software security, program analysis, and big data technology. Specifically, it discloses a method, apparatus, and device for constructing a multi-granularity vulnerability assessment dataset. The method includes: for multiple data sources, crawling raw data and performing data cleaning, deduplication, and cross-data source entity linking to obtain a unified vulnerability entity; based on the unified vulnerability entity, using abstract syntax tree analysis and in-process data dependency and control dependency analysis to obtain code slices and patch pair samples of different granularities; performing multi-dimensional annotation of metadata information to obtain annotated samples with vulnerability information, context integrity level, and compilation information; based on the annotated samples, obtaining a positive sample set and a negative sample set through positive sample filtering and negative sample construction; and performing data fusion, dynamic filtering, and structured encapsulation to construct a target vulnerability assessment dataset. This application enables the effective construction of vulnerability assessment datasets.

The invention relates to the technical field of communication network topology control, and provides a link self-healing topology control method for a redundant communication network, which realizes dynamic coupling evaluation of performance and environment by fusing the instantaneous deterioration degree and historical trend of performance data with the risk probability of environmental data, and improves the reliability of the communication network. The vulnerability index can truly reflect the current fault risk of the link, and a basis is provided for topology reconstruction triggering; after a high-fragility link is shielded, an alternative topological structure is explored through a graph theory algorithm, and it is ensured that the alternative topological structure adapts to service requirements; during service migration, service interruption caused by direct switching is avoided through parallel transmission and staged migration, and the requirement for continuity of high-real-time service is particularly met; by monitoring operation feedback data of a target topological path, vulnerability evaluation parameters and weighted evaluation parameters are dynamically updated, so that subsequent vulnerability index output and target topological structure selection can adapt to the actual operation state and service demand change of a link, and control precision attenuation caused by static parameters is avoided.

The application provides a program instruction vulnerability evaluation method based on bit-level cloning, comprising the following steps: loading a target program to be evaluated as an input into an evaluation system; the evaluation system analyzes dynamic instruction groups of the program, determines clonable bit positions of each dynamic instruction group, including clonable SDC bit positions and clonable non-SDC bit positions; performing fine-grained bit-level cloning and fault injection on the clonable bit positions and other bit positions respectively to determine SDC vulnerability of each dynamic instruction. The application has the beneficial effects that: through the combination of bit-level cloning and fault injection, repeated operations are reduced, cost is lowered, bit-level accurate analysis is realized, differentiated evaluation of complex programs is supported, and quantitative basis is provided for high-reliability software design.

The invention discloses a computer program vulnerability detection method based on a graph neural network, and the method comprises the following steps: S1, analyzing a source code into a statement unit and a dependency relationship, and constructing a program graph structure; s2, performing word embedding coding on nodes in the graph to generate code feature vectors fusing semantics and structures; s3, executing graph information transmission through the graph neural network model, and extracting a graph feature vector; s4, constructing a vulnerability evaluation network, calculating vulnerability scores, and screening candidate nodes; s5, extracting a multi-scale adjacent graph, and generating a fusion feature vector; s6, label distribution is executed, whether the candidate nodes are vulnerability nodes or not is judged, and a detection result is output; and S7, updating the model and the network parameters according to the detection result and the error of the artificial label. The invention provides a program vulnerability detection method fusing a graph neural network and self-supervised learning. The method has the advantages of being high in structure expression ability, few in dependent labels and high in detection accuracy.

The application discloses a fault-tolerant quantum architecture design method, device, storage medium and electronic equipment, wherein the fault-tolerant quantum architecture design method comprises the following steps: obtaining hardware configuration parameters of a target quantum computing architecture, and constructing a fault tree model based on the hardware configuration parameters; performing vulnerability evaluation on the fault tree model, generating a vulnerability quantification index, and determining a candidate component set requiring deployment of a resilience mechanism according to the vulnerability quantification index; configuring a corresponding resilience strategy for each component in the candidate component set, and generating a graceful degradation planning table based on the resilience strategy; inputting the fault tree model, the resilience strategy and the graceful degradation planning table into a formal verification engine to output a reliability certificate; and generating an authenticated resilience architecture netlist through a hardware description language according to the reliability certificate. The application can solve the technical problems that the quantum computing system lacks systematic fault-tolerant design, the fault response capability is insufficient, and the reliability index cannot be verified in the prior art.