172 results about "Vulnerability index" patented technology

The invention provides a multi-source information fusion data spatial analysis method and system based on a knowledge graph, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the quality calibration and fusion of multi-source heterogeneous data, and obtaining standardized fusion data; a city system knowledge graph is constructed, the city system knowledge graph comprises city element nodes with category and hierarchical relations and association strength of the city element nodes, and city function communities are constructed and optimized through a modularity optimization method; an urban element diffusion wave equation model is constructed based on an urban system knowledge graph, an urban element state propagation rule is analyzed, urban function division is performed, an urban system coupling differential equation set model is established to predict urban element state change, an urban system vulnerability index is constructed, and urban system toughness analysis is performed. According to the method, multi-source city data can be effectively fused, the city system knowledge graph is constructed, and decision support is provided for city space planning and management.

The invention discloses a multi-modal traffic anomaly identification and defense method for campus Internet of Things, and particularly relates to the technical field of Internet of Things security, and the method comprises the steps: dividing independent partitions, deploying traffic feature collection nodes, and collecting multi-modal feature data; monitoring an interaction pair flow interaction state and extracting a risk feature data set; calculating a partition transaction index and a boundary vulnerability index; inputting the index into a machine learning classification model to generate a risk level through reasoning; executing dynamic regulation and control operations of limiting a communication rate, increasing an authentication mechanism and isolating a high-risk interaction path according to the risk level; according to the invention, through micronization subunit flow characteristic collection and multi-modal characteristic fusion, the early perception capability of abnormal changes is improved; through dual-index modeling of a partition transaction index and a boundary vulnerability index, adaptive risk identification and accurate evaluation are realized; through a dynamic regulation and control mechanism based on a risk level, intelligent hierarchical defense and diffusion suppression of traffic abnormity in a campus Internet of Things environment are realized.

The invention discloses a multi-element power distribution network planning method for coping with a network attack, and relates to the technical field of power distribution network safety planning, and the method comprises the steps: S1, analyzing the vulnerability index of a power distribution network under the network attack; s2, generating a corresponding power network attack scene according to the vulnerability index in combination with historical network attack data; s3, establishing a power distribution network defense-attack-defense planning model based on the power network attack scene; s4, performing layout optimization on the power distribution network defense-attack-defense planning model based on the optimal energy storage configuration; s5, planning the power distribution network by adopting the optimized power distribution network defense-attack-defense planning model; the problem that the power distribution network is difficult to effectively prevent network attacks due to lack of pre-layout of network attack defense measures in a power distribution network planning technology is solved, the power distribution network is ensured to have effective and reliable network attack prevention capability, the defense efficiency of the power distribution network to the network attacks is remarkably improved, and safe and stable operation of the power distribution network is further improved.

The invention discloses a bridge safety assessment method based on multi-intelligent algorithm fusion, and belongs to the technical field of bridge engineering and intelligent algorithms. Aiming at the problems of insufficient multi-source data processing, lack of physical constraints and the like of a traditional evaluation technology, the method integrates multi-dimensional indexes through eight steps of multi-source data fusion preprocessing, physical information neural network construction, adaptive deep forest feature learning and the like, and generates a structural vulnerability index (SVI) to guide a maintenance decision. Innovation points comprise a five-algorithm deep coupling architecture, a physically embedded digital twinborn body, a dynamic weight proof theory and the like. The method has the advantages that high-precision recognition, interpretable prediction and visual decision making of bridge damage are achieved, evaluation precision and operation and maintenance efficiency are remarkably improved, and the method is suitable for full-life-cycle management of various bridges.

The invention discloses a vehicle-road cloud cooperative traffic abnormity early warning method, which comprises the following steps that: a road side unit receives abnormal event data reported by a vehicle side, checks the abnormal event data in combination with road side data, and uploads the abnormal event data to a cloud side; the cloud receives the data reported by the road side unit, performs fusion in combination with the cloud data, and evaluates the risk level after the occurrence of the abnormal event: constructs a space-time diagram neural network, and calculates the vulnerability index of the road network based on the traffic road network diagram structure; constructing a Bayesian network, updating probability distribution of the network by using detected data corresponding to the abnormal event as an observation value, and predicting a conduction path of the abnormal event; carrying out preliminary risk grade assessment according to the vulnerability index and the predicted conduction path; and performing traffic intervention simulation based on the abnormal event, calculating an intervention index improvement rate according to a simulation result, and correcting a risk level. According to the method, deep fusion is carried out on the vehicle and road cloud data, the traffic abnormal event identification capability is improved, and the dynamic risk level evaluation of the whole road network is realized.

The invention relates to the technical field of monitoring and identification, and discloses a rainfall type group landslide risk area and hidden danger point comprehensive identification and evaluation method. The method comprises the following steps: determining a landslide danger index and a disaster-bearing body vulnerability index of each calculation unit in a target area according to an updated comprehensive database; determining a risk evaluation result according to a landslide risk index of each sub-region determined by the landslide risk index and the disaster-bearing body vulnerability index, wherein the risk evaluation result comprises a landslide risk grade corresponding to each sub-region; determining a deformation area in the target area according to synthetic aperture radar data; determining a hidden danger point identification result corresponding to the deformation area, wherein the hidden danger point identification result comprises disaster hidden danger points in the deformation area and hidden danger risk levels corresponding to the disaster hidden danger points; adjusting a hidden danger point identification result according to the risk evaluation result; and adjusting a risk evaluation result according to a hidden danger point identification result and a disaster hidden danger point treatment result. According to the method, dynamic feedback and optimization of risk and hidden danger assessment can be realized, and misjudgment is reduced.

The invention provides a flood control risk identification and early warning system and method for a reservoir group, and belongs to the technical field of flood prevention and fighting. According to the method, a standard data set is generated by collecting rainfall, reservoir water level, flood discharge flow and landform data and performing data processing. And based on the standardized data set, constructing a hydrological model of the reservoir group in combination with GIS data, simulating flood discharge capacities under different rainfall scenes, calculating a flood discharge capacity index, a flood control risk index, a downstream vulnerability index, an upstream flood formation index and a midstream flood propagation index, and carrying out weighted fusion to generate a comprehensive risk index. And according to comparison between the comprehensive risk index and preset low, medium and high early warning threshold values, determining a current risk level and issuing early warning information. Meanwhile, user feedback is received, and model parameters and an early warning strategy are dynamically adjusted. According to the invention, through a multi-source data fusion technology, a deep learning algorithm and a dynamic feedback mechanism, accurate identification and timely early warning of the flood control risk of the reservoir group are realized.

A method of determining ecological risk with spatial aggregation and regional imbalance level applicable for use in a river basin which involves geographic information, ecology, environmental engineering and computer technology includes the steps of: dividing a river basin into a plurality of fishnets; obtaining a basin landscape disturbance index and a basin landscape vulnerability index; obtaining a basin ecological risk index based on the basin landscape disturbance index and the basin landscape vulnerability index; and determining and evaluating the basin ecological risk in spatial aggregation and the regional imbalance with reference to a spatial autocorrelation value and a basin ecological risk imbalance value obtained by using the basin ecological risk index. The ecological risk in spatial aggregation and the regional imbalance is output and evaluate so that the ecological risk in river basin can be assessed comprehensively with high evaluation accuracy.

The invention discloses a three-network fault propagation risk assessment method and system for complex network analysis, and belongs to the technical field of power system toughness assessment and disaster risk management, and the method comprises the steps: collecting node and edge data of a power network, an information network and a traffic network, and forming a heterogeneous network topology structure; constructing a node importance evaluation function; carrying out weighted correction, and outputting a three-network coupling node importance degree sequence; generating a fault event triggering list; updating the node state matrix until the node state does not change any more, and outputting a fault influence range and a fault propagation path; and calculating a three-network overall connectivity loss rate, function recovery time estimation, key node fault sensitivity and a coupling dependence vulnerability index, and outputting a three-network fault propagation risk assessment index. According to the method, the dependency relationship and the influence strength among the three networks can be truly reflected, the contribution degree of the nodes to the system toughness is quantified, and the multi-dimensional, quantifiable and explainable effective evaluation of the fault propagation risk under the complex network is realized.

The invention belongs to the technical field of base station equipment management, and discloses a base station equipment management method and system based on big data. The method comprises the following steps: firstly, collecting security situation data of a physical layer, a network layer and an application layer, extracting time sequence behavior characteristics, performing cross-layer correlation analysis, and constructing an equipment security credibility dynamic evaluation model; then, identifying vulnerability indexes of control nodes according to the trust attenuation curve, and constructing a cascade risk conduction model in combination with a topological connection relationship; dynamically dividing a security isolation domain based on the model and generating protection parameters and control rules; further, a differentiated security policy is configured, and a cross-domain collaborative response channel is established; and finally, dynamically adjusting the isolation domain boundary and the security policy by monitoring the security event frequency and the interception success rate in real time. According to the invention, the conversion from passive defense to active prediction is realized, and the safety protection capability and operation stability of the base station equipment are improved.

The invention provides a charging station access node selection method and system considering the vulnerability of a power distribution network, and belongs to the field of charging station site selection. The method comprises the steps that a power distribution network topological structure of a power distribution network is acquired, and the power distribution network topological structure comprises a plurality of power distribution nodes; calculating a plurality of node vulnerability parameter sets of the plurality of power distribution network nodes according to the power distribution node vulnerability index set; based on a Jaya-AHP method and an entropy weight method, performing index weight fusion distribution on the power distribution node vulnerability index set to obtain a vulnerability index weight set; and according to the vulnerability index weight set and the multiple node vulnerability parameter sets, calculating and screening to obtain an optimal access node, and performing charging station access. By accurately identifying the vulnerability difference of each node of the power distribution network, the centralized layout of the charging station in a node area with relatively high vulnerability is effectively avoided, and the access node with low vulnerability and proper geographic position is preferably selected, so that the impact of the charging load on the power grid is reduced.

The invention discloses a multi-model game method for multi-scene intelligent safety management. The method comprises the following steps: acquiring multi-domain original data and generating information structure features; the heterogeneous indexes are uniformly mapped into information loss components based on the features, and stable routing of the game template is carried out in combination with sequential statistics and hysteretic decision; performing game payment-oriented decision sensitive subspace compression on the unified semantic state output after routing; a dynamic risk-to-go (dynamic risk surplus) time difference method is adopted to generate step risk signals meeting total amount conservation, and the step risk signals are injected into a compression space; and through online estimation of the spectral radius of the Jacobian matrix of the optimal response operator, an equilibrium vulnerability index is generated, and stability management and closed-loop adjustment of an equilibrium point are realized. According to the invention, the decision adaptability, stability and robustness of the intelligent system in a dynamic confrontation environment can be improved.

The invention discloses an island ecological restoration effect comprehensive evaluation method, and particularly relates to the technical field of ecological environment evaluation. Obtaining a restoration area ecological basic parameter set, and constructing an original ecological characteristic matrix; calculating a restoration response curve based on the time sequence change characteristics, and determining the restoration response weight of each ecological factor; calculating a restoration net effect through a normalized difference model of parameters of a contrast area, and eliminating the influence of natural fluctuation; inputting the net effect vector into an ecological system structure coupling model, and quantifying the coupling strength between ecological factors to obtain a restoration comprehensive contribution degree; further performing space-time interpolation analysis to form a restoration effect distribution matrix and calculating a restoration vulnerability index; generating a repair optimization suggestion set according to a threshold value distribution result, and outputting a comprehensive evaluation report; according to the method, dynamic quantification and systematized diagnosis of the ecological restoration process are realized, the spatio-temporal evolution characteristics and restoration effect differences of the island ecosystem can be accurately reflected, and a scientific basis is provided for ecological restoration optimization and intelligent decision making.

The invention discloses a meteorological-based geological disaster risk early warning method, a server and a storage medium, and the method comprises the steps: constructing a two-factor dynamic evaluation framework, scientifically quantifying a disaster-bearing body vulnerability index reflecting a geological background and a disaster-causing inoculation factor reflecting a recent rainfall accumulation effect, and carrying out the inverse coupling of the disaster-bearing body vulnerability index and the disaster-causing inoculation factor, and generating a current rainfall disaster threshold dynamically adjusted in real time for each grid unit. By implementing the method and the device, the problem of insufficient early warning accuracy caused by the fact that traditional early warning cannot adapt to geological environment time-space difference is effectively solved, and accurate, dynamic and differentiated evaluation and early warning of geological disaster risks are realized.

The invention relates to the technical field of supply chain risk management, and particularly discloses a comprehensive risk early warning method and system fusing a supply chain graph and machine learning, and the method comprises the steps: collecting supply chain multi-modal heterogeneous data and prior risk knowledge, dynamically constructing and updating a sequential supply chain knowledge graph, and carrying out the early warning of the risk of a supply chain. Performing node-level, edge-level and system-level multi-level anomaly detection, generating an anomaly score and a network vulnerability index, when the anomaly score exceeds a dynamic threshold value, deducing risk propagation and node sweep probability by improving an SEIR model, calculating a dynamic fusion weight in combination with data source authority and the like, and performing weighted fusion on multi-dimensional indexes to generate a comprehensive risk score, and dynamically adjusting a threshold value to generate graded early warning, an optimization model, a map and a priori knowledge base. According to the method, the defects of lag, isolation and stiffness of a traditional method can be overcome, risk perception in advance, comprehensive coverage and accurate early warning are achieved, the method has the self-evolution capacity, the method adapts to dynamic changes of a supply chain, and powerful support is provided for safety and stability of an industrial chain.

The invention discloses a power transmission line disaster monitoring data fusion analysis method and system. The method comprises the following steps: acquiring historical meteorological data in a target area; acquiring satellite remote sensing data of a target area, and extracting a plurality of microtopographic factors; obtaining standing book and space topology information of the power transmission line in the target area, and obtaining an anti-galloping vulnerability index of each line section; constructing and training a statistical interpolation model and a machine learning fusion model based on the historical meteorological data and the plurality of microtopographic factors, and generating a meteorological element sequence along the power transmission line in the target area; and based on the meteorological element sequence, the anti-galloping vulnerability index and a multi-meteorological element collaborative threshold condition, calculating the historical galloping occurrence frequency and intensity of the target area, and combining with meteorological prediction data to obtain a galloping disaster dynamic risk early warning result of the power transmission line. According to the invention, the anti-galloping vulnerability index, the multi-meteorological-element collaborative threshold and the meteorological prediction data are combined, and the accuracy of a disaster risk early warning result is improved.

The invention relates to the technical field of power system analysis, and discloses an active power distribution network vulnerability evaluation and weak link identification method, which comprises the following steps of: firstly, constructing an equivalent sparse graph model of a power distribution network according to an actual topological structure and parameters of the active power distribution network, and then, considering the influence of degrees of adjacent nodes and power flow distribution information; calculating an improved betweenness index of each node and an improved degree index of the line; then considering the influence of each load on actual operation, designing and calculating a node voltage out-of-limit index, finally determining a weight coefficient of each evaluation index by adopting an analytic hierarchy process, and calculating a comprehensive vulnerability index of each node and line according to the weight coefficient of each index; the comprehensive vulnerability indexes of the nodes and the lines are arranged in a descending order, the vulnerable nodes and the vulnerable lines are determined, the improved node degree and line degree indexes provide a finer and more comprehensive view angle for evaluating the vulnerability of the power distribution network, and the operation safety and reliability of the power distribution network can be 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 power equipment risk assessment method and device based on a space-time diagram neural network. The method comprises the following steps: acquiring historical meteorological data of a location of target power equipment, coordinates of the target power equipment, a used age limit, a maintenance frequency and a load rate, and coordinates of a plurality of first power equipment; on the basis of historical meteorological data, meteorological data at a future moment are predicted through a space-time diagram neural network; calculating an equipment vulnerability index based on the used age limit, the maintenance frequency and the load rate; for each first power device, calculating a risk propagation coefficient corresponding to the first power device based on the meteorological data at the future moment, the coordinate of the first power device and the coordinate of the target power device; and based on the vulnerability index and the risk propagation coefficients corresponding to the plurality of first power devices, determining the risk occurrence probability of the target power device. The accuracy of equipment risk assessment in the power grid can be improved, adjustment is performed according to the assessment result, and the stability of the power grid is ensured.

An event chain-oriented offshore oil spill dynamic risk assessment method comprises a data preparation stage, an event chain modeling stage and a dynamic risk assessment stage, and the data preparation stage comprises the steps of A, determining a risk assessment area and a risk assessment unit; the event chain modeling stage comprises the steps of B, oil spill event chain construction and node analysis; the dynamic risk assessment stage comprises the steps that C, from the perspective of quantitative risk assessment, the risk index of a current oil spill event, the vulnerability index of a disaster-bearing body and the risk index of a native event are calculated respectively, and oil spill native event risk assessment is executed; step D, oil spill secondary / derivative event risk assessment; step E, oil spill event chain risk assessment; and step F, carrying out oil spill event chain risk early warning grade division. The evaluation method analyzes and evaluates the influence object of the oil spill event and the risk of secondary / derivative events caused by further disaster due to disaster elements contained in the influence object or the disaster event from the perspective of event evolution.

The invention relates to the technical field of quality supervision and early warning management, in particular to an AI-based subway engineering construction quality supervision and early warning management system and method, and the system comprises a process data fusion module which obtains the sensor monitoring data deviation degree, the material detection qualified rate and the personnel operation specification conformity of a shield tunneling process. According to the method, a quality vulnerability index for expressing process state fluctuation is established by fusing a sensor monitoring data deviation degree, a material detection qualification rate and a personnel operation specification conformity, and an accumulated risk assessment mode based on a process chain conduction relation is formed by combining dynamic quantification of an upstream process defect on a downstream influence probability. And the capability of identifying the coupling influence between the working procedures is enhanced. And synchronously analyzing information request metadata, scheduling personnel hour data, access control behavior records and rectification cycle time, and constructing a multi-dimensional collaborative efficiency index sequence covering information circulation efficiency, personnel operation load and problem solving efficiency.

The invention relates to the technical field of sewage treatment and gas monitoring control, in particular to a distributed monitoring system for gas emission tracking. The system comprises a working condition data acquisition unit, an emission reference modeling unit, a model deviation analysis unit, a vulnerability deduction unit and an anti-vulnerability control unit. The system obtains a predicted emission value by constructing a physical information neural network model, compares the predicted emission value with a measured value to calculate a deviation, and generates an attack state flag; the method is characterized in that a microbial flora vulnerability index is calculated in combination with an attack state flag and a carbon source fluctuation rate; when the vulnerability index exceeds a preset biological safety threshold value, the system adaptively switches to a biological priority mode; according to the method, real-time quantification of the credibility of the model is realized, and invisible biochemical attacks with unknown toxicity sources can be effectively identified.

The invention discloses a weak link identification method and device for a power distribution system, electronic equipment and a program product, and relates to the field of power distribution systems.The method comprises the steps that classified typical disaster scenes and line fault probabilities in the typical disaster scenes are analyzed; evaluating a power supply operation state of the power distribution system after each typical disaster scene occurs by using a maximum power supply capability model, calculating element risk parameters of the power distribution system based on the power supply operation state and the line fault probability, and weighting and calculating a state weakness index by combining the element risk parameters of the power distribution system and scene weight parameters; and calculating a structure vulnerability index based on the centrality index of the node and the scene weight parameter weighting, fusing the state vulnerability index and the structure vulnerability index, and identifying a node weak link in the power distribution system. According to the invention, a technical problem of low accuracy of an evaluation result because only evaluation of a physical structure of the power distribution system is carried out in weak link evaluation of the power distribution system in related technologies is solved.

The invention relates to the technical field of natural disaster risk assessment, in particular to a sandstorm disaster risk zoning method, which comprises the following steps of: performing comprehensive risk assessment by combining a disaster-inducing factor risk index, a disaster-pregnant environment sensitivity index, a disaster-bearing body vulnerability index and a disaster prevention and reduction capability index and adopting a weighted synthesis method to obtain a sandstorm disaster risk zoning map; and then a sandstorm hazard source can be determined through a sandstorm disaster risk area, that is, the wind speed reaches a main area in which the surface sand is generated, so that the degree of influence and harm caused by sandstorm diffusion to the environment, animals, plants and human beings can be correctly evaluated, a scientific basis is provided for formulating targeted risk management measures, and the method is suitable for popularization and application. Therefore, the technical problem that the life and property of people cannot be safely guaranteed due to the fact that no assessment method capable of comprehensively considering the risk of disaster-inducing factors, the vulnerability of disaster-bearing bodies and the risk area prone to sand storm exists at present is effectively solved.

The invention relates to a multi-disaster coupling tough power grid dynamic defense method and a storage medium, and the method comprises the steps: a multi-disaster coupling analysis module receives disaster data and power grid data in real time, carries out the grid processing, obtains a disaster coupling value H of each grid, and carries out the risk grade division of each grid; the dynamic toughness evaluation module calculates the node vulnerability index of each grid according to the disaster coupling value H of each grid and the real-time data of the power grid; the collaborative recovery decision module formulates an optimal recovery strategy for the execution terminal by adopting a mixed integer linear programming model MILP according to the vulnerability index of each grid node, the equipment importance grading information and the fault influence range information; and the execution terminal executes the instruction according to the optimal recovery strategy and feeds back the executed state data in real time. Compared with the prior art, the method provided by the invention solves the three major problems of vulnerability identification lagging, low recovery resource scheduling efficiency and difficulty in cross-department collaboration of the oversize urban power grid under composite extreme disasters.

The invention discloses a dynamic voltage recovery and flicker suppression method based on light-storage cooperation, and relates to the technical field of power quality control of a power distribution network, and the method comprises the steps: obtaining a dynamic topological structure and a node impedance spectrum of the power distribution network in real time; calculating the vulnerability index of each node based on the data; generating a risk thermodynamic diagram according to the vulnerability index; proactive power scheduling is performed on the energy storage system based on a thermodynamic diagram, node impedance characteristics can be actively shaped and optimized in combination with impedance, the system comprises an intelligent monitoring unit, a central control platform, the energy storage system and a communication network, weak links of the system can be accurately recognized, the problems that a voltage disturbance propagation path is uncertain and the early warning adaptability is poor are solved, and the system reliability is improved. The perspectiveness and the accuracy of voltage disturbance suppression are improved, and the voltage stability and the power supply reliability of the power distribution network are enhanced.

The invention relates to the technical field of information asset management and control, and discloses an information asset management system and a protection intensity evaluation method thereof. An asset identification module of the method obtains original attribute data of information assets, and generates asset identification codes according to asset types and storage positions; the classification mapping module extracts asset key attributes and establishes an association relationship between the asset identification codes and the key attributes; the risk assessment module calculates an asset vulnerability index based on the key attribute, and generates a risk level in combination with external threat data; the dynamic adjustment module updates the key attribute according to the risk level change, and recalculates the vulnerability index; and the access control module generates an access authority strategy according to the updated vulnerability index, and restricts asset access behaviors. The system realizes refined and dynamic management of information assets, balances asset security and management efficiency, and meets the requirements of organizations for information asset management.

The invention relates to the technical field of data analysis, in particular to a natural disaster engineering rescue data platform construction and intelligent analysis system, which comprises an edge intelligent sensing module for generating a field three-dimensional live-action grid through laser radar point cloud and visible light video fusion and extracting disaster feature vectors; the cloud deep analysis module adopts a graph neural network to aggregate multi-source data, and generates a global disaster evolution graph composed of disaster grade distribution and infrastructure vulnerability indexes; the dynamic decision generation module is used for generating a space-time cooperative rescue instruction set composed of an equipment scheduling path and an engineering disposal sequence in combination with a rescue resource library and engineering constraints including an equipment mechanical arm operation radius and an aftershock influence coefficient; and the closed-loop optimization module is used for collecting operation feedback logs, correcting extraction weights of disaster feature vectors through a difference comparison engine, and dynamically updating an engineering constraint library. According to the invention, unified cooperation of disaster sensing intelligence, task allocation optimization and response process closed loop is realized.

The invention provides a high-order network vulnerability analysis method based on a hypergraph, relates to the technical field of systems and information, newly constructs a hypergraph network model with adjustable degree distribution as a bottom layer topology, and provides a novel vulnerability index suitable for a hypergraph network through theoretical analysis. And the relation between hypergraph degree distribution heterogeneity and vulnerability is disclosed. According to the method, high-order and randomness characteristics of interaction of an actual network system are fully considered, the constructed degree distribution adjustable hypergraph network model can be used for describing an ecological network, a social network and a traffic system, and the provided novel vulnerability analysis method can effectively evaluate the ability of the actual network system to deal with external interference. The method is wider in application range in practical application, and particularly has important technical value in the aspects of network security, survivability design, risk prediction and the like.

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