224 results about "Vulnerability evaluation" patented technology

Systems and methods are provided for multi-domain vulnerability assessment. An exemplary system includes a plurality of wireless signal detectors, wherein each of the wireless signal detectors is configured to detect wireless signals of a respective wireless signal protocol of a plurality of wireless signal protocols. The wireless signal detectors transmit the signal data to a data processor that classifies the plurality of devices based on the wireless signal data, determines one or more device vulnerabilities associated with the plurality of devices based on the classification of the plurality of devices, and determine a plurality of device risks for the plurality of devices based on the plurality of device vulnerabilities.

The invention provides an adjacent tunnel excavation disturbance vulnerability evaluation method based on Bayesian updating, which introduces a concept of system stiffness to represent uncertainty of tunnel damage caused by adjacent excavation disturbance, and brings obtained system stiffness data into an adjacent excavation disturbance model through a fitting equation. Calculating a large number of numerical values to establish a tunnel damage database; establishing an agent model between the parameters corresponding to the disturbance working condition and the tunnel damage index; collecting data for parameter characteristic statistics to obtain prior probability distribution of tunnel damage indexes, and combining engineering actual monitoring data for Bayesian updating to obtain posterior probability distribution of the tunnel damage indexes; and correcting the disturbance probability demand model by using the updated mean value and standard deviation of the tunnel damage indexes, and updating the vulnerability curve in combination with tunnel damage state threshold division and a logarithmic normal probability distribution function. The probability of different damage degrees of the tunnel structure can be made to fit the reality, and the applicability of an existing disturbance damage database is improved.

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 the technical field of power distribution network operation and maintenance, in particular to a power distribution network communication network vulnerability assessment method based on a graph neural network, comprising the following steps: acquiring power distribution network communication network operation data including node equipment attribute data and link attribute data; based on the operation data of the communication network of the power distribution network, grading and marking the vulnerability of each node to form a training data set; establishing a power distribution network communication network vulnerability evaluation model; and training a power distribution network communication network vulnerability evaluation model by taking the operation data in the training data set as input and the vulnerability level label as supervision. According to the embodiment of the invention, the operation data of the power distribution network communication network part or the historical nodes are labeled in advance, and then the trained model is used to comprehensively and accurately evaluate the vulnerability level of each node in the power distribution network communication network.

The invention provides a vulnerability assessment system, method and device based on malicious software behavior analysis, and relates to the technical field of network security. The system comprises a behavior capturing module which is used for operating malicious software through a virtual sandbox, collecting behavior logs generated in the operation process of the malicious software and generating a behavior data set; the feature extraction module is used for extracting behavior features from the behavior data set and generating a standardized feature set; the feature matching module is used for performing multi-dimensional mapping matching on the standardized feature set and the vulnerability features in the vulnerability feature database to obtain a matching result and a vulnerability identifier; the behavior modeling module is used for generating a behavior chain model through a time sequence modeling method based on the matching result and the standardized feature set; and the vulnerability evaluation module is used for generating an evaluation result. According to the technical scheme provided by the invention, the analysis capability and the quantification precision of the system on the vulnerability characteristics and the associated behaviors thereof can be improved, so that more accurate vulnerability evaluation is realized.

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 a muck site landslide total probability risk quantitative evaluation method considering muck three-dimensional space variability, comprising the following steps: carrying out muck site drilling investigation to obtain muck site data; random field parameters representing the spatial variability of the muck are obtained; simulating three-dimensional space variation characteristics of the muck; based on a limit equilibrium method, carrying out three-dimensional slope stability evaluation and potential landslide volume estimation on the muck field; possible migration ranges under various landslide volumes are simulated; counting life and property data of a downstream disaster-bearing body, and estimating the vulnerability of the disaster-bearing body according to the landslide strength based on a building and personnel vulnerability evaluation formula; and establishing a muck site landslide total probability risk assessment formula, and carrying out landslide risk quantitative assessment and risk rating. According to the method, the unique spatial variation characteristic of the muck is considered, the stability and the potential slip position and quantity are analyzed from the three-dimensional perspective, and accurate risk assessment is realized in combination with a vulnerability quantitative assessment method.

The invention discloses a power system external impact vulnerability assessment method based on a multi-target zero-sum game. The method comprises the following steps: establishing a power grid unit damage correlation propagation model according to a power grid structure and the types and number of actually included power grid units; on the basis of the power grid unit damage correlation propagation model, an attack and defense multi-target zero-sum game model is established according to optimization targets of attackers and defenders for different target functions and resource constraints of the attackers and the defenders; the attack and defense multi-target zero-sum game model is expressed as a double-layer mixed integer nonlinear optimization problem, and the problem is solved to obtain the minimum attack cost required by an external attacker to cause complete failure of the power system. According to the method, a multi-target attack and defense game model is established, and the minimum attack cost required by complete failure of the power system is solved from the perspective of attackers so as to reflect the vulnerability and security of a defense strategy of the power system on the overall level.

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 discloses a landslide risk and risk evaluation method suitable for a tectonic active area, and belongs to the field of geological disaster risk evaluation, and the risk evaluation method is characterized in that risk evaluation is superposed with vulnerability evaluation. The risk evaluation method comprises the following steps: calculating a deterministic coefficient value of each evaluation factor, and weighting to a grid layer of each evaluation factor to generate a deterministic coefficient grid layer of the evaluation factor; importing the deterministic coefficient layer into a logistic regression model, and generating a landslide risk evaluation result based on a logistic regression-deterministic coefficient coupling model; the vulnerability evaluation method is an analytic hierarchy process. By coupling a deterministic coefficient model and a logistic regression model, a linear relationship of multiple evaluation factors is effectively processed, and nonlinear contribution of a single evaluation factor to landslide development is captured, so that the landslide risk evaluation precision of a geological environment complex region and a structure active region is improved; and the evaluation result can more objectively and truly reflect the landslide risk of the active fault zone and the nearby area.

The invention discloses a vulnerability severity assessment method and system based on a vulnerability knowledge graph. The method comprises the following steps: S1, acquiring a vulnerability report according to a CVE-ID number, and processing to form a vulnerability element labeling set; s2, training and generating an extraction model based on the vulnerability element labeling set, and extracting target vulnerability elements; s3, constructing a vulnerability knowledge graph based on the target vulnerability element, the description text, the vulnerability code and the CVE-ID number; s4, after representing the code nodes and the vulnerability description nodes, splicing the code nodes and the vulnerability description nodes to form corresponding CVE-ID representation; taking a vulnerability evaluation result as a label, taking the CVE-ID representation as input, and training to generate a GRU classifier; and S5, obtaining a to-be-evaluated vulnerability description text, predicting the size of a vulnerability severity index based on the extraction model, the vulnerability knowledge graph and the GRU classifier, and calculating the vulnerability severity level of the to-be-evaluated vulnerability description text according to the size of the vulnerability severity index. The method has the characteristics of high correlation, high accuracy and high operability.

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.

The invention belongs to the technical field of power system operation, and provides a collaborative optimization method and system for a multi-region interconnected power system, and the technical scheme is as follows: determining a direct consumption coefficient matrix and a final demand vector according to the power production and consumption data of each region; combining the consumption coefficient matrix and the final demand vector to construct a power quasi input-output model constraint; analyzing a node degree, a clustering coefficient and a shortest path based on the obtained power grid topological graph, calculating an importance index of nodes based on the obtained node degree, the clustering coefficient and the shortest path, performing identification to obtain key nodes, and performing vulnerability evaluation based on the key nodes obtained through identification to obtain a transmission optimization constraint; and designing a double-layer optimization structure, introducing the power quasi input-output model constraint into upper-layer optimization, and respectively introducing the transmission optimization constraint into double-layer optimization to obtain an optimized multi-region interconnected power system collaborative optimization scheme. The impact of cross-regional power transmission on carbon emissions is evaluated.

The invention provides a decentralized vulnerability assessment method based on multi-modal data fusion, belongs to the technical field of computers, and solves the technical problems that a traditional vulnerability assessment method cannot fully combine multi-modal data and cannot effectively protect data privacy. According to the technical scheme, the method comprises the following steps: S1, preprocessing a vulnerability assessment data set; s2, extracting structure information, lexical features and annotation information of the source code; s3, inputting the source code and the annotation information into a CodeT5 model, and performing multi-modal feature fusion in combination with the graph structure information processed by Joern; s4, cross-modal information fusion is carried out through an adaptive weight mechanism, and model performance is optimized; s5, carrying out model training locally; and S6, inputting the test set into the trained model for vulnerability evaluation, and outputting a classification result. And the accuracy, comprehensiveness and privacy protection capability of vulnerability assessment are improved.

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 a new energy power system power flow prediction method considering source-network-load uncertainty, and the method comprises the specific steps: carrying out the normalization processing of the historical parameters of a power grid, extracting multi-dimensional track features through variational mode decomposition, and constructing an operation trend set; a cellular automaton model is combined to simulate a spatio-temporal evolution law, and a fault feature recognition mechanism is optimized; constructing a trend state classification database based on the residual transformation rate; dynamically distributing state contribution weights by using a multi-constraint regression model, and establishing a prediction model; and iteratively optimizing the model through error feedback, and constructing a vulnerability evaluation system based on sensitivity and interference analysis. According to the method, multi-source data and dynamic weight distribution are fused, the problems that traditional prediction precision is low and fault response is lagged are solved through a bidirectional feedback optimization framework, the accuracy of power grid trend prediction and vulnerability assessment can be improved, and support is provided for security defense.

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 discloses a power grid safety margin on-line monitoring method and system for multi-level vulnerability assessment, and relates to the technical field of power system safety margin on-line monitoring, and the method comprises the steps: building a multi-level power grid topological structure model based on 110kV and 35kV transformer substations and line sections, and calculating the node importance and the inter-level coupling degree; designing a static safety margin and a dynamic response characteristic index, and constructing a comprehensive vulnerability evaluation system; training an LSTM deep learning model based on the historical operation data; acquiring power grid operation data in real time, and performing standardization processing; evaluating the current safety margin by using the trained LSTM deep learning model; and judging whether early warning needs to be triggered or not according to an early warning grading mechanism, and outputting an evaluation result and early warning information. According to the invention, real-time monitoring and accurate prediction of the power grid safety margin are realized, weak links are effectively identified, the safety and reliability of the power grid are improved, an early warning grading mechanism ensures quick response and risk management, and stable operation and power supply safety of the power grid are guaranteed.

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.